Merge "Add CTS/GTS tests for TH presumbit."
diff --git a/automotive/OWNERS b/automotive/OWNERS
index 3cf4489..83ee63c 100644
--- a/automotive/OWNERS
+++ b/automotive/OWNERS
@@ -1,4 +1,5 @@
-randolphs@google.com
pirozzoj@google.com
twasilczyk@google.com
pfg@google.com
+gurunagarajan@google.com
+keunyoung@google.com
diff --git a/automotive/can/1.0/default/Android.bp b/automotive/can/1.0/default/Android.bp
index 0a4afd6..8aa1d6b 100644
--- a/automotive/can/1.0/default/Android.bp
+++ b/automotive/can/1.0/default/Android.bp
@@ -47,7 +47,6 @@
shared_libs: [
"android.hardware.automotive.can@1.0",
"libhidlbase",
- "libhidltransport",
],
static_libs: [
"android.hardware.automotive.can@libnetdevice",
diff --git a/automotive/can/1.0/tools/Android.bp b/automotive/can/1.0/tools/Android.bp
index 8c26985..21f364b 100644
--- a/automotive/can/1.0/tools/Android.bp
+++ b/automotive/can/1.0/tools/Android.bp
@@ -23,7 +23,6 @@
shared_libs: [
"android.hardware.automotive.can@1.0",
"libhidlbase",
- "libhidltransport",
],
header_libs: [
"android.hardware.automotive.can@hidl-utils-lib",
@@ -39,7 +38,6 @@
shared_libs: [
"android.hardware.automotive.can@1.0",
"libhidlbase",
- "libhidltransport",
],
header_libs: [
"android.hardware.automotive.can@hidl-utils-lib",
@@ -55,6 +53,5 @@
shared_libs: [
"android.hardware.automotive.can@1.0",
"libhidlbase",
- "libhidltransport",
],
}
diff --git a/automotive/evs/1.1/default/Android.bp b/automotive/evs/1.1/default/Android.bp
index 411f0ff..a463471 100644
--- a/automotive/evs/1.1/default/Android.bp
+++ b/automotive/evs/1.1/default/Android.bp
@@ -19,7 +19,6 @@
"libcutils",
"libhardware",
"libhidlbase",
- "libhidltransport",
"liblog",
"libui",
"libutils",
diff --git a/automotive/vehicle/2.0/default/impl/vhal_v2_0/DefaultConfig.h b/automotive/vehicle/2.0/default/impl/vhal_v2_0/DefaultConfig.h
index c8e11e3..094a372 100644
--- a/automotive/vehicle/2.0/default/impl/vhal_v2_0/DefaultConfig.h
+++ b/automotive/vehicle/2.0/default/impl/vhal_v2_0/DefaultConfig.h
@@ -786,7 +786,7 @@
.initialValue = {.int32Values = {toInt(VehicleApPowerStateReq::ON), 0}}},
{.config = {.prop = toInt(VehicleProperty::AP_POWER_STATE_REPORT),
- .access = VehiclePropertyAccess::WRITE,
+ .access = VehiclePropertyAccess::READ_WRITE,
.changeMode = VehiclePropertyChangeMode::ON_CHANGE},
.initialValue = {.int32Values = {toInt(VehicleApPowerStateReport::WAIT_FOR_VHAL), 0}}},
diff --git a/automotive/vehicle/2.0/types.hal b/automotive/vehicle/2.0/types.hal
index 8c84c0a..1355d9f 100644
--- a/automotive/vehicle/2.0/types.hal
+++ b/automotive/vehicle/2.0/types.hal
@@ -1310,7 +1310,7 @@
*
* @change_mode VehiclePropertyChangeMode:ON_CHANGE
- * @access VehiclePropertyAccess:WRITE
+ * @access VehiclePropertyAccess:READ_WRITE
*/
AP_POWER_STATE_REPORT = (
0x0A01
@@ -2537,7 +2537,7 @@
* power controller must change power state to this state to shutdown
* system.
*
- * int32Values[1] : one of enum_vehicle_ap_power_state_shutdown_param_type
+ * int32Values[1] : one of VehicleApPowerStateShutdownParam
*
* SHUTDOWN_PRPARE may be requested from either WAIT_FOR_VHAL or ON states.
*/
diff --git a/boot/1.1/Android.bp b/boot/1.1/Android.bp
new file mode 100644
index 0000000..6a8d57a
--- /dev/null
+++ b/boot/1.1/Android.bp
@@ -0,0 +1,18 @@
+// This file is autogenerated by hidl-gen -Landroidbp.
+
+hidl_interface {
+ name: "android.hardware.boot@1.1",
+ root: "android.hardware",
+ vndk: {
+ enabled: true,
+ },
+ srcs: [
+ "types.hal",
+ "IBootControl.hal",
+ ],
+ interfaces: [
+ "android.hardware.boot@1.0",
+ "android.hidl.base@1.0",
+ ],
+ gen_java: true,
+}
diff --git a/boot/1.1/IBootControl.hal b/boot/1.1/IBootControl.hal
new file mode 100644
index 0000000..939dfb3
--- /dev/null
+++ b/boot/1.1/IBootControl.hal
@@ -0,0 +1,66 @@
+/*
+ * Copyright 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.boot@1.1;
+
+import @1.0::IBootControl;
+
+interface IBootControl extends @1.0::IBootControl {
+ /**
+ * Sets whether a snapshot-merge of any dynamic partition is in progress.
+ *
+ * After the merge status is set to a given value, subsequent calls to
+ * getSnapshotMergeStatus must return the set value.
+ *
+ * The merge status must be persistent across reboots. That is, getSnapshotMergeStatus
+ * must return the same value after a reboot if the merge status is not altered in any way
+ * (e.g. set by setSnapshotMergeStatus or set to CANCELLED by bootloader).
+ *
+ * Read/write access to the merge status must be atomic. When the HAL is processing a
+ * setSnapshotMergeStatus call, all subsequent calls to getSnapshotMergeStatus must block until
+ * setSnapshotMergeStatus has returned.
+ *
+ * A MERGING state indicates that dynamic partitions are partially comprised by blocks in the
+ * userdata partition.
+ *
+ * When the merge status is set to MERGING, the following operations must be prohibited from the
+ * bootloader:
+ * - Flashing or erasing "userdata" or "metadata".
+ *
+ * The following operations may be prohibited when the status is set to MERGING. If not
+ * prohibited, it is recommended that the user receive a warning.
+ * - Changing the active slot (e.g. via "fastboot set_active")
+ *
+ * @param status Merge status.
+ *
+ * @return success True on success, false otherwise.
+ */
+ setSnapshotMergeStatus(MergeStatus status) generates (bool success);
+
+ /**
+ * Returns whether a snapshot-merge of any dynamic partition is in progress.
+ *
+ * This function must return the merge status set by the last setSnapshotMergeStatus call and
+ * recorded by the bootloader with one exception. If the partitions are being flashed from the
+ * bootloader such that the pending merge must be canceled (for example, if the super partition
+ * is being flashed), this function must return CANCELLED.
+ *
+ * @return success True if the merge status is read successfully, false otherwise.
+ * @return status Merge status.
+ */
+ getSnapshotMergeStatus() generates (MergeStatus status);
+};
+
diff --git a/boot/1.1/types.hal b/boot/1.1/types.hal
new file mode 100644
index 0000000..6346078
--- /dev/null
+++ b/boot/1.1/types.hal
@@ -0,0 +1,44 @@
+/*
+ * Copyright 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.boot@1.1;
+
+enum MergeStatus : int32_t {
+ /**
+ * No snapshot or merge is in progress.
+ */
+ NONE = 0,
+
+ /**
+ * The merge status could not be determined.
+ */
+ UNKNOWN,
+
+ /**
+ * Partitions are being snapshotted, but no merge has been started.
+ */
+ SNAPSHOTTED,
+
+ /**
+ * At least one partition has merge is in progress.
+ */
+ MERGING,
+
+ /**
+ * A merge was in progress, but it was canceled by the bootloader.
+ */
+ CANCELLED,
+};
diff --git a/compatibility_matrices/compatibility_matrix.current.xml b/compatibility_matrices/compatibility_matrix.current.xml
index ef1cd75..a409650 100644
--- a/compatibility_matrices/compatibility_matrix.current.xml
+++ b/compatibility_matrices/compatibility_matrix.current.xml
@@ -89,7 +89,7 @@
</hal>
<hal format="hidl" optional="true">
<name>android.hardware.boot</name>
- <version>1.0</version>
+ <version>1.1</version>
<interface>
<name>IBootControl</name>
<instance>default</instance>
@@ -204,7 +204,7 @@
</hal>
<hal format="hidl" optional="false">
<name>android.hardware.graphics.composer</name>
- <version>2.1-3</version>
+ <version>2.1-4</version>
<interface>
<name>IComposer</name>
<instance>default</instance>
@@ -307,7 +307,7 @@
</hal>
<hal format="hidl" optional="true">
<name>android.hardware.neuralnetworks</name>
- <version>1.0-2</version>
+ <version>1.0-3</version>
<interface>
<name>IDevice</name>
<regex-instance>.*</regex-instance>
@@ -467,7 +467,7 @@
</hal>
<hal format="hidl" optional="true">
<name>android.hardware.vibrator</name>
- <version>1.0-3</version>
+ <version>1.0-4</version>
<interface>
<name>IVibrator</name>
<instance>default</instance>
@@ -507,7 +507,7 @@
</hal>
<hal format="hidl" optional="true">
<name>android.hardware.wifi.supplicant</name>
- <version>1.0-2</version>
+ <version>1.0-3</version>
<interface>
<name>ISupplicant</name>
<instance>default</instance>
diff --git a/confirmationui/1.0/vts/functional/Android.bp b/confirmationui/1.0/vts/functional/Android.bp
index d19d702..fd088cd 100644
--- a/confirmationui/1.0/vts/functional/Android.bp
+++ b/confirmationui/1.0/vts/functional/Android.bp
@@ -23,7 +23,7 @@
static_libs: [
"android.hardware.confirmationui@1.0",
"android.hardware.keymaster@4.0",
- "libcrypto",
+ "libcrypto_static",
"libcn-cbor",
"android.hardware.confirmationui-support-lib",
],
diff --git a/contexthub/1.0/default/OWNERS b/contexthub/1.0/default/OWNERS
index 5373073..90c2330 100644
--- a/contexthub/1.0/default/OWNERS
+++ b/contexthub/1.0/default/OWNERS
@@ -1,4 +1,3 @@
-aarossig@google.com
arthuri@google.com
bduddie@google.com
-bstack@google.com
+stange@google.com
diff --git a/contexthub/1.0/vts/functional/OWNERS b/contexthub/1.0/vts/functional/OWNERS
index ee01441..045cc4e 100644
--- a/contexthub/1.0/vts/functional/OWNERS
+++ b/contexthub/1.0/vts/functional/OWNERS
@@ -1,8 +1,7 @@
#Context Hub team
-aarossig@google.com
arthuri@google.com
bduddie@google.com
-bstack@google.com
+stange@google.com
#VTS team
yim@google.com
diff --git a/current.txt b/current.txt
index 83657b2..87498a1 100644
--- a/current.txt
+++ b/current.txt
@@ -575,7 +575,23 @@
2410dd02d67786a732d36e80b0f8ccf55086604ef37f9838e2013ff2c571e404 android.hardware.camera.device@3.5::types
b69a7615c508acf5c5201efd1bfa3262167874fc3594e2db5a3ff93addd8ac75 android.hardware.keymaster@4.0::IKeymasterDevice
eb2fa0c883c2185d514be0b84c179b283753ef0c1b77b45b4f359bd23bba8b75 android.hardware.neuralnetworks@1.0::IPreparedModel
+f1109cbb10297b7429a11fab42afa912710b303c9bf20bd5cdb8bd57b9c84186 android.hardware.neuralnetworks@1.0::types
+9d8ee57c490ffeaa28f702eaea8d198cb510e4bbfb99e6cb5f63e73341057c7c android.hardware.neuralnetworks@1.1::types
fb382e986c10b8fbb797a8546e8f9ea6d1107bfe6f3fb7e57f6bbbf1f807a906 android.hardware.neuralnetworks@1.2::IDevice
40e71cd693de5b832325c5d8f081f2ff20a7ba2b89d401cee5b4b3eb0e241681 android.hardware.neuralnetworks@1.2::IPreparedModel
+71c0f7127335e5b74d1615d5e7f129831b43ffbae5318ad0924d7d8d8910a859 android.hardware.neuralnetworks@1.2::types
+a785a57447a81e9c130eef6904c3a5c256076c6a04588c40620ebd6fa2660d77 android.hardware.radio@1.2::types
1a6e2bd289f22931c526b21916910f1d4c436b7acb9556e4243de4ce8e6cc2e4 android.hardware.soundtrigger@2.0::ISoundTriggerHwCallback
fd65298e1e09e0e3c781ab18305920d757dbe55a3b459ce17814ec5cf6dfee99 android.hardware.wifi@1.0::IWifiP2pIface
+
+# HALs released in Android R
+07d0a252b2d8fa35887908a996ba395cf392968395fc30afab791f46e0c22a52 android.hardware.boot@1.1::IBootControl
+74049a402be913963edfdd80828a53736570e9d8124a1bf18166b6ed46a6b0ab android.hardware.boot@1.1::types
+34515afa2bb792d3c6d8495a5f5d907d179c8507ca5e55c10050d02ae1d516ef android.hardware.neuralnetworks@1.3::IDevice
+b74fe72cfe438f50e772e6a307657ff449d5bde83c15dd1f140ff2edbe73499c android.hardware.neuralnetworks@1.3::types
+04395b26be33db17747c3d3b0e8066d323f891ff4f9f3b3ddb490b2f3f844a18 android.hardware.wifi@1.4::IWifi
+270f0eb670dfd9bc5cd718e09711f2534fa8425f54d06c1a46523ca156b509e2 android.hardware.wifi.supplicant@1.3::ISupplicant
+dd4b7cfbb6e1c6ff011c33920762ad89dd02240c63a4d3a3d5037f154eae3e3b android.hardware.wifi.supplicant@1.3::ISupplicantStaIface
+619fc9839ec6e369cfa9b28e3e9412e6885720ff8f9b5750c1b6ffb905120391 android.hardware.wifi.supplicant@1.3::ISupplicantStaIfaceCallback
+6fe09b18e913608579638594788198ec45bb2369e567d7df661db46c4f0e5f08 android.hardware.wifi.supplicant@1.3::ISupplicantStaNetwork
+91931b05bd70ea6bdffbe075086183f803379571788564e28854207620eb75cf android.hardware.wifi.supplicant@1.3::types
diff --git a/drm/1.0/vts/functional/Android.bp b/drm/1.0/vts/functional/Android.bp
index d6ebfdd..61d4d58 100644
--- a/drm/1.0/vts/functional/Android.bp
+++ b/drm/1.0/vts/functional/Android.bp
@@ -30,7 +30,7 @@
"libhidlmemory",
"libnativehelper",
"libssl",
- "libcrypto",
+ "libcrypto_static",
],
test_suites: ["general-tests"],
}
diff --git a/drm/1.2/vts/functional/Android.bp b/drm/1.2/vts/functional/Android.bp
index 6b4a4c0..95883bf 100644
--- a/drm/1.2/vts/functional/Android.bp
+++ b/drm/1.2/vts/functional/Android.bp
@@ -34,7 +34,7 @@
"libhidlmemory",
"libnativehelper",
"libssl",
- "libcrypto",
+ "libcrypto_static",
],
test_suites: ["general-tests"],
}
diff --git a/dumpstate/1.0/default/DumpstateDevice.cpp b/dumpstate/1.0/default/DumpstateDevice.cpp
index 25d92b0..c57bf43 100644
--- a/dumpstate/1.0/default/DumpstateDevice.cpp
+++ b/dumpstate/1.0/default/DumpstateDevice.cpp
@@ -37,11 +37,6 @@
// NOTE: this is just an example on how to use the DumpstateUtil.h functions to implement
// this interface.
- // Exit when dump is completed since this is a lazy HAL.
- addPostCommandTask([]() {
- exit(0);
- });
-
if (handle == nullptr || handle->numFds < 1) {
ALOGE("no FDs\n");
return Void();
diff --git a/dumpstate/1.0/default/service.cpp b/dumpstate/1.0/default/service.cpp
index 4f276b7..76c72b5 100644
--- a/dumpstate/1.0/default/service.cpp
+++ b/dumpstate/1.0/default/service.cpp
@@ -15,22 +15,26 @@
*/
#define LOG_TAG "android.hardware.dumpstate@1.0-service"
+#include <hidl/HidlLazyUtils.h>
#include <hidl/HidlSupport.h>
#include <hidl/HidlTransportSupport.h>
#include "DumpstateDevice.h"
-using ::android::hardware::configureRpcThreadpool;
-using ::android::hardware::dumpstate::V1_0::IDumpstateDevice;
-using ::android::hardware::dumpstate::V1_0::implementation::DumpstateDevice;
-using ::android::hardware::joinRpcThreadpool;
using ::android::OK;
using ::android::sp;
+using ::android::hardware::configureRpcThreadpool;
+using ::android::hardware::joinRpcThreadpool;
+using ::android::hardware::LazyServiceRegistrar;
+using ::android::hardware::dumpstate::V1_0::IDumpstateDevice;
+using ::android::hardware::dumpstate::V1_0::implementation::DumpstateDevice;
int main(int /* argc */, char* /* argv */ []) {
sp<IDumpstateDevice> dumpstate = new DumpstateDevice;
configureRpcThreadpool(1, true /* will join */);
- if (dumpstate->registerAsService() != OK) {
+
+ auto registrar = LazyServiceRegistrar::getInstance();
+ if (registrar.registerService(dumpstate) != OK) {
ALOGE("Could not register service.");
return 1;
}
diff --git a/graphics/composer/2.2/vts/functional/Android.bp b/graphics/composer/2.2/vts/functional/Android.bp
index 2872880..21ba9f3 100644
--- a/graphics/composer/2.2/vts/functional/Android.bp
+++ b/graphics/composer/2.2/vts/functional/Android.bp
@@ -30,8 +30,6 @@
"libfmq",
"libgui",
"libhidlbase",
- "libhidltransport",
- "libhwbinder",
"libprocessgroup",
"libsync",
"libui",
diff --git a/graphics/composer/2.4/IComposer.hal b/graphics/composer/2.4/IComposer.hal
index 34801da..d3b3cb6 100644
--- a/graphics/composer/2.4/IComposer.hal
+++ b/graphics/composer/2.4/IComposer.hal
@@ -17,12 +17,10 @@
package android.hardware.graphics.composer@2.4;
import IComposerClient;
-
import @2.1::Error;
import @2.3::IComposer;
interface IComposer extends @2.3::IComposer {
-
/**
* Creates a v2.4 client of the composer. Supersedes @2.3::createClient.
*
diff --git a/graphics/composer/2.4/IComposerClient.hal b/graphics/composer/2.4/IComposerClient.hal
index 8fe0976..60445f5 100644
--- a/graphics/composer/2.4/IComposerClient.hal
+++ b/graphics/composer/2.4/IComposerClient.hal
@@ -21,13 +21,12 @@
import @2.3::IComposerClient;
interface IComposerClient extends @2.3::IComposerClient {
-
/**
* Required capabilities which are supported by the display. The
* particular set of supported capabilities for a given display may be
* retrieved using getDisplayCapabilities.
*/
- enum DisplayCapability : uint32_t {
+ enum DisplayCapability : @2.3::IComposerClient.DisplayCapability {
/**
* Indicates that the display supports protected contents.
* When returned, hardware composer must be able to accept client target
@@ -37,6 +36,20 @@
};
/**
+ * Supersedes {@link @2.1::IComposerClient.DisplayType}.
+ */
+ enum DisplayConnectionType : uint32_t {
+ /**
+ * Display is connected through internal port, e.g. DSI, eDP.
+ */
+ INTERNAL = 0,
+ /**
+ * Display is connected through external port, e.g. HDMI, DisplayPort.
+ */
+ EXTERNAL = 1,
+ };
+
+ /**
* Provides a list of supported capabilities (as described in the
* definition of DisplayCapability above). This list must not change after
* initialization.
@@ -46,6 +59,14 @@
* @return capabilities is a list of supported capabilities.
*/
getDisplayCapabilities_2_4(Display display)
- generates (Error error,
- vec<DisplayCapability> capabilities);
+ generates (Error error, vec<DisplayCapability> capabilities);
+
+ /**
+ * Returns whether the given physical display is internal or external.
+ *
+ * @return error is NONE upon success. Otherwise,
+ * BAD_DISPLAY when the given display is invalid or virtual.
+ * @return type is the connection type of the display.
+ */
+ getDisplayConnectionType(Display display) generates (Error error, DisplayConnectionType type);
};
diff --git a/graphics/composer/2.4/default/Android.bp b/graphics/composer/2.4/default/Android.bp
index a44e687..a30609b 100644
--- a/graphics/composer/2.4/default/Android.bp
+++ b/graphics/composer/2.4/default/Android.bp
@@ -37,7 +37,6 @@
"libfmq",
"libhardware",
"libhidlbase",
- "libhidltransport",
"libhwc2on1adapter",
"libhwc2onfbadapter",
"liblog",
diff --git a/graphics/composer/2.4/utils/hal/include/composer-hal/2.4/ComposerClient.h b/graphics/composer/2.4/utils/hal/include/composer-hal/2.4/ComposerClient.h
index 7110c80..c810186 100644
--- a/graphics/composer/2.4/utils/hal/include/composer-hal/2.4/ComposerClient.h
+++ b/graphics/composer/2.4/utils/hal/include/composer-hal/2.4/ComposerClient.h
@@ -46,6 +46,14 @@
return Void();
}
+ Return<void> getDisplayConnectionType(
+ Display display, IComposerClient::getDisplayConnectionType_cb hidl_cb) override {
+ IComposerClient::DisplayConnectionType type;
+ Error error = mHal->getDisplayConnectionType(display, &type);
+ hidl_cb(error, type);
+ return Void();
+ }
+
static std::unique_ptr<ComposerClientImpl> create(Hal* hal) {
auto client = std::make_unique<ComposerClientImpl>(hal);
return client->init() ? std::move(client) : nullptr;
diff --git a/graphics/composer/2.4/utils/hal/include/composer-hal/2.4/ComposerHal.h b/graphics/composer/2.4/utils/hal/include/composer-hal/2.4/ComposerHal.h
index 0074808..c3bb535 100644
--- a/graphics/composer/2.4/utils/hal/include/composer-hal/2.4/ComposerHal.h
+++ b/graphics/composer/2.4/utils/hal/include/composer-hal/2.4/ComposerHal.h
@@ -38,6 +38,8 @@
public:
virtual Error getDisplayCapabilities_2_4(
Display display, std::vector<IComposerClient::DisplayCapability>* outCapabilities) = 0;
+ virtual Error getDisplayConnectionType(Display display,
+ IComposerClient::DisplayConnectionType* outType) = 0;
};
} // namespace hal
diff --git a/graphics/composer/2.4/utils/passthrough/include/composer-passthrough/2.4/HwcHal.h b/graphics/composer/2.4/utils/passthrough/include/composer-passthrough/2.4/HwcHal.h
index 65d47d7..fd05f66 100644
--- a/graphics/composer/2.4/utils/passthrough/include/composer-passthrough/2.4/HwcHal.h
+++ b/graphics/composer/2.4/utils/passthrough/include/composer-passthrough/2.4/HwcHal.h
@@ -62,15 +62,34 @@
return Error::NONE;
}
+ Error getDisplayConnectionType(Display display,
+ IComposerClient::DisplayConnectionType* outType) override {
+ if (!mDispatch.getDisplayConnectionType) {
+ return Error::UNSUPPORTED;
+ }
+
+ uint32_t type = HWC2_DISPLAY_CONNECTION_TYPE_INTERNAL;
+ int32_t error = mDispatch.getDisplayConnectionType(mDevice, display, &type);
+ *outType = static_cast<IComposerClient::DisplayConnectionType>(type);
+ return static_cast<Error>(error);
+ }
+
protected:
bool initDispatch() override {
if (!BaseType2_3::initDispatch()) {
return false;
}
+
+ this->initOptionalDispatch(HWC2_FUNCTION_GET_DISPLAY_CONNECTION_TYPE,
+ &mDispatch.getDisplayConnectionType);
return true;
}
private:
+ struct {
+ HWC2_PFN_GET_DISPLAY_CONNECTION_TYPE getDisplayConnectionType;
+ } mDispatch = {};
+
using BaseType2_1 = V2_1::passthrough::detail::HwcHalImpl<Hal>;
using BaseType2_3 = V2_3::passthrough::detail::HwcHalImpl<Hal>;
using BaseType2_1::mDevice;
diff --git a/graphics/composer/2.4/utils/vts/ComposerVts.cpp b/graphics/composer/2.4/utils/vts/ComposerVts.cpp
index ee4f3a3..937b50e 100644
--- a/graphics/composer/2.4/utils/vts/ComposerVts.cpp
+++ b/graphics/composer/2.4/utils/vts/ComposerVts.cpp
@@ -51,7 +51,6 @@
Error ComposerClient::getDisplayCapabilities(
Display display, std::vector<IComposerClient::DisplayCapability>* outCapabilities) {
- std::vector<IComposerClient::DisplayCapability> capabilities;
Error error = Error::NONE;
mClient->getDisplayCapabilities_2_4(display,
[&](const auto& tmpError, const auto& tmpCapabilities) {
@@ -61,6 +60,16 @@
return error;
}
+Error ComposerClient::getDisplayConnectionType(Display display,
+ IComposerClient::DisplayConnectionType* outType) {
+ Error error = Error::NONE;
+ mClient->getDisplayConnectionType(display, [&](const auto& tmpError, const auto& tmpType) {
+ error = tmpError;
+ *outType = tmpType;
+ });
+ return error;
+}
+
} // namespace vts
} // namespace V2_4
} // namespace composer
diff --git a/graphics/composer/2.4/utils/vts/include/composer-vts/2.4/ComposerVts.h b/graphics/composer/2.4/utils/vts/include/composer-vts/2.4/ComposerVts.h
index 0a301c6..a7d7f86 100644
--- a/graphics/composer/2.4/utils/vts/include/composer-vts/2.4/ComposerVts.h
+++ b/graphics/composer/2.4/utils/vts/include/composer-vts/2.4/ComposerVts.h
@@ -71,6 +71,9 @@
Display display,
std::vector<IComposerClient::DisplayCapability>* outDisplayCapabilities);
+ Error getDisplayConnectionType(Display display,
+ IComposerClient::DisplayConnectionType* outType);
+
private:
const sp<IComposerClient> mClient;
};
diff --git a/graphics/composer/2.4/vts/functional/Android.bp b/graphics/composer/2.4/vts/functional/Android.bp
index 6ee7873..921c421 100644
--- a/graphics/composer/2.4/vts/functional/Android.bp
+++ b/graphics/composer/2.4/vts/functional/Android.bp
@@ -22,7 +22,6 @@
// TODO(b/64437680): Assume these libs are always available on the device.
shared_libs: [
"libfmq",
- "libhidltransport",
"libsync",
],
static_libs: [
diff --git a/graphics/composer/2.4/vts/functional/VtsHalGraphicsComposerV2_4TargetTest.cpp b/graphics/composer/2.4/vts/functional/VtsHalGraphicsComposerV2_4TargetTest.cpp
index 0fccc58..76c0039 100644
--- a/graphics/composer/2.4/vts/functional/VtsHalGraphicsComposerV2_4TargetTest.cpp
+++ b/graphics/composer/2.4/vts/functional/VtsHalGraphicsComposerV2_4TargetTest.cpp
@@ -179,6 +179,16 @@
EXPECT_EQ(Error::BAD_DISPLAY, error);
}
+TEST_F(GraphicsComposerHidlTest, getDisplayConnectionType) {
+ IComposerClient::DisplayConnectionType type;
+ EXPECT_EQ(Error::BAD_DISPLAY,
+ mComposerClient->getDisplayConnectionType(mInvalidDisplayId, &type));
+
+ for (Display display : mComposerCallback->getDisplays()) {
+ EXPECT_EQ(Error::NONE, mComposerClient->getDisplayConnectionType(display, &type));
+ }
+}
+
} // namespace
} // namespace vts
} // namespace V2_4
diff --git a/graphics/mapper/2.0/utils/passthrough/include/mapper-passthrough/2.0/Gralloc1Hal.h b/graphics/mapper/2.0/utils/passthrough/include/mapper-passthrough/2.0/Gralloc1Hal.h
index d9beb4f..db7e67d 100644
--- a/graphics/mapper/2.0/utils/passthrough/include/mapper-passthrough/2.0/Gralloc1Hal.h
+++ b/graphics/mapper/2.0/utils/passthrough/include/mapper-passthrough/2.0/Gralloc1Hal.h
@@ -282,14 +282,16 @@
}
if (flex.planes[0].component != FLEX_COMPONENT_Y ||
- flex.planes[1].component != FLEX_COMPONENT_Cb ||
- flex.planes[2].component != FLEX_COMPONENT_Cr) {
+ ((flex.planes[1].component != FLEX_COMPONENT_Cb || flex.planes[2].component != FLEX_COMPONENT_Cr) &&
+ (flex.planes[2].component != FLEX_COMPONENT_Cb || flex.planes[1].component != FLEX_COMPONENT_Cr))) {
return false;
}
const auto& y = flex.planes[0];
- const auto& cb = flex.planes[1];
- const auto& cr = flex.planes[2];
+ const auto& cb = (flex.planes[1].component == FLEX_COMPONENT_Cb)?
+ flex.planes[1] : flex.planes[2];
+ const auto& cr = (flex.planes[2].component == FLEX_COMPONENT_Cr)?
+ flex.planes[2] : flex.planes[1];
if (cb.h_increment != cr.h_increment || cb.v_increment != cr.v_increment) {
return false;
diff --git a/graphics/mapper/2.1/utils/passthrough/include/mapper-passthrough/2.1/Gralloc0Hal.h b/graphics/mapper/2.1/utils/passthrough/include/mapper-passthrough/2.1/Gralloc0Hal.h
index 18fbb6d..8540068 100644
--- a/graphics/mapper/2.1/utils/passthrough/include/mapper-passthrough/2.1/Gralloc0Hal.h
+++ b/graphics/mapper/2.1/utils/passthrough/include/mapper-passthrough/2.1/Gralloc0Hal.h
@@ -37,6 +37,10 @@
Error validateBufferSize(const native_handle_t* bufferHandle,
const IMapper::BufferDescriptorInfo& descriptorInfo,
uint32_t stride) override {
+ if (descriptorInfo.layerCount != 1) {
+ return Error::BAD_VALUE;
+ }
+
if (!mModule->validateBufferSize) {
return Error::NONE;
}
diff --git a/keymaster/3.0/vts/functional/Android.bp b/keymaster/3.0/vts/functional/Android.bp
index b0371c7..69aa56d 100644
--- a/keymaster/3.0/vts/functional/Android.bp
+++ b/keymaster/3.0/vts/functional/Android.bp
@@ -26,7 +26,7 @@
],
static_libs: [
"android.hardware.keymaster@3.0",
- "libcrypto",
+ "libcrypto_static",
"libsoftkeymasterdevice",
],
test_suites: ["general-tests"],
diff --git a/keymaster/4.0/vts/functional/Android.bp b/keymaster/4.0/vts/functional/Android.bp
index 333e408..0401362 100644
--- a/keymaster/4.0/vts/functional/Android.bp
+++ b/keymaster/4.0/vts/functional/Android.bp
@@ -25,7 +25,7 @@
],
static_libs: [
"android.hardware.keymaster@4.0",
- "libcrypto",
+ "libcrypto_static",
"libkeymaster4support",
"libsoftkeymasterdevice",
],
diff --git a/keymaster/4.0/vts/functional/KeymasterHidlTest.cpp b/keymaster/4.0/vts/functional/KeymasterHidlTest.cpp
index 3af1df3..4838e7e 100644
--- a/keymaster/4.0/vts/functional/KeymasterHidlTest.cpp
+++ b/keymaster/4.0/vts/functional/KeymasterHidlTest.cpp
@@ -48,10 +48,11 @@
SecurityLevel KeymasterHidlTest::securityLevel_;
hidl_string KeymasterHidlTest::name_;
hidl_string KeymasterHidlTest::author_;
+string KeymasterHidlTest::service_name_;
-void KeymasterHidlTest::SetUpTestCase() {
- string service_name = KeymasterHidlEnvironment::Instance()->getServiceName<IKeymasterDevice>();
- keymaster_ = ::testing::VtsHalHidlTargetTestBase::getService<IKeymasterDevice>(service_name);
+void KeymasterHidlTest::InitializeKeymaster() {
+ service_name_ = KeymasterHidlEnvironment::Instance()->getServiceName<IKeymasterDevice>();
+ keymaster_ = ::testing::VtsHalHidlTargetTestBase::getService<IKeymasterDevice>(service_name_);
ASSERT_NE(keymaster_, nullptr);
ASSERT_TRUE(keymaster_
@@ -62,18 +63,22 @@
author_ = author;
})
.isOk());
+}
+
+void KeymasterHidlTest::SetUpTestCase() {
+
+ InitializeKeymaster();
os_version_ = ::keymaster::GetOsVersion();
os_patch_level_ = ::keymaster::GetOsPatchlevel();
auto service_manager = android::hidl::manager::V1_0::IServiceManager::getService();
ASSERT_NE(nullptr, service_manager.get());
-
all_keymasters_.push_back(keymaster_);
service_manager->listByInterface(
IKeymasterDevice::descriptor, [&](const hidl_vec<hidl_string>& names) {
for (auto& name : names) {
- if (name == service_name) continue;
+ if (name == service_name_) continue;
auto keymaster =
::testing::VtsHalHidlTargetTestBase::getService<IKeymasterDevice>(name);
ASSERT_NE(keymaster, nullptr);
@@ -269,6 +274,13 @@
return GetCharacteristics(key_blob, client_id, app_data, key_characteristics);
}
+ErrorCode KeymasterHidlTest::GetDebugInfo(DebugInfo* debug_info) {
+ EXPECT_TRUE(keymaster_->getDebugInfo([&](const DebugInfo& hidl_debug_info) {
+ *debug_info = hidl_debug_info;
+ }).isOk());
+ return ErrorCode::OK;
+}
+
ErrorCode KeymasterHidlTest::Begin(KeyPurpose purpose, const HidlBuf& key_blob,
const AuthorizationSet& in_params, AuthorizationSet* out_params,
OperationHandle* op_handle) {
@@ -611,6 +623,20 @@
return ciphertext;
}
+string KeymasterHidlTest::EncryptMessage(const string& message, BlockMode block_mode,
+ PaddingMode padding, uint8_t mac_length_bits,
+ const HidlBuf& iv_in) {
+ SCOPED_TRACE("EncryptMessage");
+ auto params = AuthorizationSetBuilder()
+ .BlockMode(block_mode)
+ .Padding(padding)
+ .Authorization(TAG_MAC_LENGTH, mac_length_bits)
+ .Authorization(TAG_NONCE, iv_in);
+ AuthorizationSet out_params;
+ string ciphertext = EncryptMessage(message, params, &out_params);
+ return ciphertext;
+}
+
string KeymasterHidlTest::DecryptMessage(const HidlBuf& key_blob, const string& ciphertext,
const AuthorizationSet& params) {
SCOPED_TRACE("DecryptMessage");
diff --git a/keymaster/4.0/vts/functional/KeymasterHidlTest.h b/keymaster/4.0/vts/functional/KeymasterHidlTest.h
index 015fc43..b09da45 100644
--- a/keymaster/4.0/vts/functional/KeymasterHidlTest.h
+++ b/keymaster/4.0/vts/functional/KeymasterHidlTest.h
@@ -37,6 +37,7 @@
using ::android::sp;
using ::std::string;
+using hidl::base::V1_0::DebugInfo;
class HidlBuf : public hidl_vec<uint8_t> {
typedef hidl_vec<uint8_t> super;
@@ -95,6 +96,7 @@
// SetUpTestCase runs only once per test case, not once per test.
static void SetUpTestCase();
+ static void InitializeKeymaster();
static void TearDownTestCase() {
keymaster_.clear();
all_keymasters_.clear();
@@ -140,6 +142,8 @@
const HidlBuf& app_data, KeyCharacteristics* key_characteristics);
ErrorCode GetCharacteristics(const HidlBuf& key_blob, KeyCharacteristics* key_characteristics);
+ ErrorCode GetDebugInfo(DebugInfo* debug_info);
+
ErrorCode Begin(KeyPurpose purpose, const HidlBuf& key_blob, const AuthorizationSet& in_params,
AuthorizationSet* out_params, OperationHandle* op_handle);
ErrorCode Begin(KeyPurpose purpose, const AuthorizationSet& in_params,
@@ -201,6 +205,8 @@
HidlBuf* iv_out);
string EncryptMessage(const string& message, BlockMode block_mode, PaddingMode padding,
const HidlBuf& iv_in);
+ string EncryptMessage(const string& message, BlockMode block_mode, PaddingMode padding,
+ uint8_t mac_length_bits, const HidlBuf& iv_in);
string DecryptMessage(const HidlBuf& key_blob, const string& ciphertext,
const AuthorizationSet& params);
@@ -235,6 +241,7 @@
static SecurityLevel securityLevel_;
static hidl_string name_;
static hidl_string author_;
+ static string service_name_;
};
} // namespace test
diff --git a/keymaster/4.0/vts/functional/keymaster_hidl_hal_test.cpp b/keymaster/4.0/vts/functional/keymaster_hidl_hal_test.cpp
index 9e6cce7..0ac7e48 100644
--- a/keymaster/4.0/vts/functional/keymaster_hidl_hal_test.cpp
+++ b/keymaster/4.0/vts/functional/keymaster_hidl_hal_test.cpp
@@ -18,6 +18,7 @@
#include <cutils/log.h>
#include <iostream>
+#include <signal.h>
#include <openssl/evp.h>
#include <openssl/mem.h>
@@ -2706,6 +2707,40 @@
}
/*
+ * EncryptionOperationsTest.AesWrongPurpose
+ *
+ * Verifies that AES encryption fails in the correct way when an unauthorized purpose is specified.
+ */
+TEST_F(EncryptionOperationsTest, AesWrongPurpose) {
+ auto err = GenerateKey(AuthorizationSetBuilder()
+ .Authorization(TAG_NO_AUTH_REQUIRED)
+ .AesKey(128)
+ .Authorization(TAG_PURPOSE, KeyPurpose::ENCRYPT)
+ .Authorization(TAG_BLOCK_MODE, BlockMode::GCM)
+ .Authorization(TAG_MIN_MAC_LENGTH, 128)
+ .Padding(PaddingMode::NONE));
+ ASSERT_EQ(ErrorCode::OK, err) << "Got " << err;
+
+ err = Begin(KeyPurpose::DECRYPT,
+ AuthorizationSetBuilder().BlockMode(BlockMode::GCM).Padding(PaddingMode::NONE));
+ EXPECT_EQ(ErrorCode::INCOMPATIBLE_PURPOSE, err) << "Got " << err;
+
+ CheckedDeleteKey();
+
+ ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
+ .Authorization(TAG_NO_AUTH_REQUIRED)
+ .AesKey(128)
+ .Authorization(TAG_PURPOSE, KeyPurpose::DECRYPT)
+ .Authorization(TAG_BLOCK_MODE, BlockMode::GCM)
+ .Authorization(TAG_MIN_MAC_LENGTH, 128)
+ .Padding(PaddingMode::NONE)));
+
+ err = Begin(KeyPurpose::ENCRYPT,
+ AuthorizationSetBuilder().BlockMode(BlockMode::GCM).Padding(PaddingMode::NONE));
+ EXPECT_EQ(ErrorCode::INCOMPATIBLE_PURPOSE, err) << "Got " << err;
+}
+
+/*
* EncryptionOperationsTest.AesEcbNoPaddingWrongInputSize
*
* Verifies that AES encryption fails in the correct way when provided an input that is not a
@@ -3225,6 +3260,92 @@
}
/*
+ * EncryptionOperationsTest.AesGcmRoundTripWithDelaySuccess
+ *
+ * Verifies that AES GCM mode works, even when there's a long delay
+ * between operations.
+ */
+TEST_F(EncryptionOperationsTest, AesGcmRoundTripWithDelaySuccess) {
+ ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
+ .Authorization(TAG_NO_AUTH_REQUIRED)
+ .AesEncryptionKey(128)
+ .Authorization(TAG_BLOCK_MODE, BlockMode::GCM)
+ .Padding(PaddingMode::NONE)
+ .Authorization(TAG_MIN_MAC_LENGTH, 128)));
+
+ string aad = "foobar";
+ string message = "123456789012345678901234567890123456";
+
+ auto begin_params = AuthorizationSetBuilder()
+ .BlockMode(BlockMode::GCM)
+ .Padding(PaddingMode::NONE)
+ .Authorization(TAG_MAC_LENGTH, 128);
+
+ auto update_params =
+ AuthorizationSetBuilder().Authorization(TAG_ASSOCIATED_DATA, aad.data(), aad.size());
+
+ // Encrypt
+ AuthorizationSet begin_out_params;
+ ASSERT_EQ(ErrorCode::OK, Begin(KeyPurpose::ENCRYPT, begin_params, &begin_out_params))
+ << "Begin encrypt";
+ string ciphertext;
+ AuthorizationSet update_out_params;
+ sleep(5);
+ ASSERT_EQ(ErrorCode::OK,
+ Finish(op_handle_, update_params, message, "", &update_out_params, &ciphertext));
+
+ ASSERT_EQ(ciphertext.length(), message.length() + 16);
+
+ // Grab nonce
+ begin_params.push_back(begin_out_params);
+
+ // Decrypt.
+ ASSERT_EQ(ErrorCode::OK, Begin(KeyPurpose::DECRYPT, begin_params)) << "Begin decrypt";
+ string plaintext;
+ size_t input_consumed;
+ sleep(5);
+ ASSERT_EQ(ErrorCode::OK, Update(op_handle_, update_params, ciphertext, &update_out_params,
+ &plaintext, &input_consumed));
+ EXPECT_EQ(ciphertext.size(), input_consumed);
+ sleep(5);
+ EXPECT_EQ(ErrorCode::OK, Finish("", &plaintext));
+ EXPECT_EQ(message.length(), plaintext.length());
+ EXPECT_EQ(message, plaintext);
+}
+
+/*
+ * EncryptionOperationsTest.AesGcmDifferentNonces
+ *
+ * Verifies that encrypting the same data with different nonces produces different outputs.
+ */
+TEST_F(EncryptionOperationsTest, AesGcmDifferentNonces) {
+ ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
+ .Authorization(TAG_NO_AUTH_REQUIRED)
+ .AesEncryptionKey(128)
+ .Authorization(TAG_BLOCK_MODE, BlockMode::GCM)
+ .Padding(PaddingMode::NONE)
+ .Authorization(TAG_MIN_MAC_LENGTH, 128)
+ .Authorization(TAG_CALLER_NONCE)));
+
+ string aad = "foobar";
+ string message = "123456789012345678901234567890123456";
+ string nonce1 = "000000000000";
+ string nonce2 = "111111111111";
+ string nonce3 = "222222222222";
+
+ string ciphertext1 =
+ EncryptMessage(message, BlockMode::GCM, PaddingMode::NONE, 128, HidlBuf(nonce1));
+ string ciphertext2 =
+ EncryptMessage(message, BlockMode::GCM, PaddingMode::NONE, 128, HidlBuf(nonce2));
+ string ciphertext3 =
+ EncryptMessage(message, BlockMode::GCM, PaddingMode::NONE, 128, HidlBuf(nonce3));
+
+ ASSERT_NE(ciphertext1, ciphertext2);
+ ASSERT_NE(ciphertext1, ciphertext3);
+ ASSERT_NE(ciphertext2, ciphertext3);
+}
+
+/*
* EncryptionOperationsTest.AesGcmTooShortTag
*
* Verifies that AES GCM mode fails correctly when a too-short tag length is specified.
@@ -4456,6 +4577,84 @@
EXPECT_EQ(result, std::make_pair(ErrorCode::OK, HidlBuf()));
}
+
+using ClearOperationsTest = KeymasterHidlTest;
+
+/*
+ * ClearSlotsTest.TooManyOperations
+ *
+ * Verifies that TOO_MANY_OPERATIONS is returned after the max number of
+ * operations are started without being finished or aborted. Also verifies
+ * that aborting the operations clears the operations.
+ *
+ */
+TEST_F(ClearOperationsTest, TooManyOperations) {
+ ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
+ .Authorization(TAG_NO_AUTH_REQUIRED)
+ .RsaEncryptionKey(2048, 65537)
+ .Padding(PaddingMode::NONE)));
+
+ auto params = AuthorizationSetBuilder().Padding(PaddingMode::NONE);
+ int max_operations = SecLevel() == SecurityLevel::STRONGBOX ? 4 : 16;
+ OperationHandle op_handles[max_operations];
+ AuthorizationSet out_params;
+ for(int i=0; i<max_operations; i++) {
+ EXPECT_EQ(ErrorCode::OK, Begin(KeyPurpose::ENCRYPT, key_blob_, params, &out_params, &(op_handles[i])));
+ }
+ EXPECT_EQ(ErrorCode::TOO_MANY_OPERATIONS,
+ Begin(KeyPurpose::ENCRYPT, key_blob_, params, &out_params, &op_handle_));
+ // Try again just in case there's a weird overflow bug
+ EXPECT_EQ(ErrorCode::TOO_MANY_OPERATIONS,
+ Begin(KeyPurpose::ENCRYPT, key_blob_, params, &out_params, &op_handle_));
+ for(int i=0; i<max_operations; i++) {
+ EXPECT_EQ(ErrorCode::OK, Abort(op_handles[i]));
+ }
+ EXPECT_EQ(ErrorCode::OK,
+ Begin(KeyPurpose::ENCRYPT, key_blob_, params, &out_params, &op_handle_));
+ AbortIfNeeded();
+}
+
+/*
+ * ClearSlotsTest.ServiceDeath
+ *
+ * Verifies that the service is restarted after death and the ongoing
+ * operations are cleared.
+ */
+TEST_F(ClearOperationsTest, ServiceDeath) {
+
+ ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
+ .Authorization(TAG_NO_AUTH_REQUIRED)
+ .RsaEncryptionKey(2048, 65537)
+ .Padding(PaddingMode::NONE)));
+
+ auto params = AuthorizationSetBuilder().Padding(PaddingMode::NONE);
+ int max_operations = SecLevel() == SecurityLevel::STRONGBOX ? 4 : 16;
+ OperationHandle op_handles[max_operations];
+ AuthorizationSet out_params;
+ for(int i=0; i<max_operations; i++) {
+ EXPECT_EQ(ErrorCode::OK, Begin(KeyPurpose::ENCRYPT, key_blob_, params, &out_params, &(op_handles[i])));
+ }
+ EXPECT_EQ(ErrorCode::TOO_MANY_OPERATIONS,
+ Begin(KeyPurpose::ENCRYPT, key_blob_, params, &out_params, &op_handle_));
+
+ DebugInfo debug_info;
+ GetDebugInfo(&debug_info);
+ kill(debug_info.pid, SIGKILL);
+ // wait 1 second for keymaster to restart
+ sleep(1);
+ InitializeKeymaster();
+
+ for(int i=0; i<max_operations; i++) {
+ EXPECT_EQ(ErrorCode::OK, Begin(KeyPurpose::ENCRYPT, key_blob_, params, &out_params, &(op_handles[i])));
+ }
+ EXPECT_EQ(ErrorCode::TOO_MANY_OPERATIONS,
+ Begin(KeyPurpose::ENCRYPT, key_blob_, params, &out_params, &op_handle_));
+ for(int i=0; i<max_operations; i++) {
+ EXPECT_EQ(ErrorCode::OK, Abort(op_handles[i]));
+ }
+}
+
+
} // namespace test
} // namespace V4_0
} // namespace keymaster
diff --git a/light/utils/main.cpp b/light/utils/main.cpp
index d07e799..b834132 100644
--- a/light/utils/main.cpp
+++ b/light/utils/main.cpp
@@ -25,7 +25,7 @@
std::cerr << msg << std::endl;
}
-int main() {
+int main(int argc, char* argv[]) {
using ::android::hardware::hidl_vec;
using ::android::hardware::light::V2_0::Brightness;
using ::android::hardware::light::V2_0::Flash;
@@ -41,10 +41,29 @@
return -1;
}
- const static LightState off = {
- .color = 0u, .flashMode = Flash::NONE, .brightnessMode = Brightness::USER,
+ static LightState off = {
+ .color = 0u,
+ .flashMode = Flash::NONE,
+ .brightnessMode = Brightness::USER,
};
+ if (argc > 2) {
+ error("Usage: blank_screen [color]");
+ return -1;
+ }
+
+ if (argc > 1) {
+ char* col_ptr;
+ unsigned int col_new;
+
+ col_new = strtoul(argv[1], &col_ptr, 0);
+ if (*col_ptr != '\0') {
+ error("Failed to convert " + std::string(argv[1]) + " to number");
+ return -1;
+ }
+ off.color = col_new;
+ }
+
service->getSupportedTypes([&](const hidl_vec<Type>& types) {
for (Type type : types) {
Status ret = service->setLight(type, off);
diff --git a/neuralnetworks/1.0/types.hal b/neuralnetworks/1.0/types.hal
index 02db063..ba9d068 100644
--- a/neuralnetworks/1.0/types.hal
+++ b/neuralnetworks/1.0/types.hal
@@ -25,25 +25,24 @@
* with at least one dimension). Types not prefaced by TENSOR_* represent
* scalar values and must have no dimensions.
*
- * Although many types are defined, most operators accept just a few
+ * Although we define many types, most operators accept just a few
* types. Most used are {@link OperandType::TENSOR_FLOAT32},
* {@link OperandType::TENSOR_QUANT8_ASYMM},
* and {@link OperandType::INT32}.
*/
enum OperandType : int32_t {
/** A 32 bit floating point scalar value. */
- FLOAT32 = 0,
+ FLOAT32 = 0,
/** A signed 32 bit integer scalar value. */
- INT32 = 1,
+ INT32 = 1,
/** An unsigned 32 bit integer scalar value. */
- UINT32 = 2,
-
+ UINT32 = 2,
/** A tensor of 32 bit floating point values. */
- TENSOR_FLOAT32 = 3,
+ TENSOR_FLOAT32 = 3,
/** A tensor of 32 bit integer values. */
- TENSOR_INT32 = 4,
+ TENSOR_INT32 = 4,
/**
- * A tensor of 8 bit integers that represent real numbers.
+ * A tensor of 8 bit unsigned integers that represent real numbers.
*
* Attached to this tensor are two numbers that can be used to convert the
* 8 bit integer to the real value and vice versa. These two numbers are:
@@ -51,21 +50,21 @@
* - zeroPoint: a 32 bit integer, in range [0, 255].
*
* The formula is:
- * real_value = (integer_value - zeroPoint) * scale.
+ * real_value = (integer_value - zeroPoint) * scale.
*/
TENSOR_QUANT8_ASYMM = 5,
/**
- * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
- * OEM operation and data types.
+ * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+ * alternative to OEM operation and data types.
*
* OEM specific scalar value.
*/
OEM = 10000,
/**
- * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
- * OEM operation and data types.
+ * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+ * alternative to OEM operation and data types.
*
* A tensor of OEM specific values.
*/
@@ -78,7 +77,6 @@
* The type of an operation in a model.
*/
enum OperationType : int32_t {
-
/**
* Adds two tensors, element-wise.
*
@@ -110,14 +108,16 @@
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandType}, and compatible dimensions
* as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scales and zeroPoint can be different from input0 scale and zeroPoint.
* * 2: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
*
* Outputs:
* * 0: The sum, a tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
ADD = 0,
@@ -187,8 +187,8 @@
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
AVERAGE_POOL_2D = 1,
@@ -206,22 +206,23 @@
*
* Inputs:
* * 0 ~ n-1: The list of n input tensors, of shape
- * [D0, D1, ..., Daxis(i), ..., Dm]. For inputs of
- * {@link OperandType::TENSOR_QUANT8_ASYMM}, all input tensors
- * must have the same scale and zeroPoint.
+ * [D0, D1, ..., Daxis(i), ..., Dm].
+ * All input tensors of
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * must have the same scale and zeroPoint as the output tensor.
* * n: An {@link OperandType::INT32} scalar, specifying the
* concatenation axis.
*
* Outputs:
* * 0: The output, a tensor of the same {@link OperandType} as the input
* tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, the scale and zeroPoint
+ * values must be the same as the input tensors'.
*/
CONCATENATION = 2,
/**
- * Performs an 2-D convolution operation.
+ * Performs a 2-D convolution operation.
*
* The CONV_2D op sweeps a 2-D filter that can mix channels together over a
* batch of images, applying the filter to each window of each image of the
@@ -238,11 +239,17 @@
* filter[channel, di, dj, k]
* ) + bias[channel]
*
- * Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor {@link OperandType} configurations:
+ * * 32 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
*
- * Supported tensor rank: 4, with "NHWC" data layout.
+ * * Quantized:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
+ * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+ * * * input.scale * filter.scale).
+ *
+ * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+ * and Channels) data layout.
*
* Both explicit padding and implicit padding are supported.
*
@@ -252,12 +259,12 @@
* * 1: A 4-D tensor, of shape
* [depth_out, filter_height, filter_width, depth_in], specifying the
* filter.
- * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
- * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the bias
- * should also be of {@link OperandType::TENSOR_FLOAT32}. For input
- * tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias
- * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
- * 0 and bias_scale == input_scale * filter_scale.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+ * tensor of type {@link OperandType::TENSOR_FLOAT32}
+ * the bias must be of the same
+ * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+ * of 0 and bias_scale == input_scale * filter_scale.
* * 3: An {@link OperandType::INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 4: An {@link OperandType::INT32} scalar, specifying the padding on
@@ -281,11 +288,11 @@
* [depth_out, filter_height, filter_width, depth_in], specifying the
* filter.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
- * tensor of {@link OperandType::TENSOR_FLOAT32}, the bias should
- * also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
- * of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
- * of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
- * bias_scale == input_scale * filter_scale.
+ * tensor of type {@link OperandType::TENSOR_FLOAT32}
+ * the bias must be of the same
+ * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+ * of 0 and bias_scale == input_scale * filter_scale.
* * 3: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
@@ -299,11 +306,9 @@
*
* Outputs:
* * 0: The output 4-D tensor, of shape
- * [batches, out_height, out_width, depth_out]. For output tensor of
- * {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition
- * must be satisfied: output_scale > input_scale * filter_scale.
- *
- * Available since API level 27.
+ * [batches, out_height, out_width, depth_out].
+ * For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the following condition must be satisfied: output_scale > input_scale * filter_scale
*/
CONV_2D = 3,
@@ -329,11 +334,17 @@
* filter[1, di, dj, k * channel_multiplier + q]
* ) + bias[k * channel_multiplier + q]
*
- * Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor {@link OperandType} configurations:
+ * * 32 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
*
- * Supported tensor rank: 4, with "NHWC" data layout.
+ * * Quantized:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
+ * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+ * * * input.scale * filter.scale).
+ *
+ * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+ * and Channels) data layout.
*
* Both explicit padding and implicit padding are supported.
*
@@ -343,11 +354,11 @@
* * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
* specifying the filter.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
- * tensor of {@link OperandType::TENSOR_FLOAT32}, the bias should
- * also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
- * of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
- * of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
- * bias_scale == input_scale * filter_scale.
+ * tensor of type {@link OperandType::TENSOR_FLOAT32}
+ * the bias must be of the same
+ * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+ * of 0 and bias_scale == input_scale * filter_scale.
* * 3: An {@link OperandType::INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 4: An {@link OperandType::INT32} scalar, specifying the padding on
@@ -372,11 +383,11 @@
* * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
* specifying the filter.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
- * tensor of {@link OperandType::TENSOR_FLOAT32}, the bias should
- * also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
- * of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
- * of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
- * bias_scale == input_scale * filter_scale.
+ * tensor of type {@link OperandType::TENSOR_FLOAT32}
+ * the bias must be of the same
+ * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+ * of 0 and bias_scale == input_scale * filter_scale.
* * 3: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
@@ -392,11 +403,10 @@
*
* Outputs:
* * 0: The output 4-D tensor, of shape
- * [batches, out_height, out_width, depth_out]. For output tensor of
- * {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition
- * must be satisfied: output_scale > input_scale * filter_scale.
- *
- * Available since API level 27.
+ * [batches, out_height, out_width, depth_out]. For
+ * output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the following condition must be satisfied:
+ * output_scale > input_scale * filter_scale
*/
DEPTHWISE_CONV_2D = 4,
@@ -419,7 +429,8 @@
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
- * Supported tensor rank: 4, with "NHWC" data layout.
+ * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+ * and Channels) data layout.
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
@@ -431,8 +442,8 @@
* Outputs:
* * 0: The output 4-D tensor, of shape [batch, height*block_size,
* width*block_size, depth/(block_size*block_size)].
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
DEPTH_TO_SPACE = 5,
@@ -443,19 +454,19 @@
*
* output = (input - zeroPoint) * scale.
*
- * Supported tensor {@link OperandType}:
+ * Supported input tensor {@link OperandType}:
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
+ * Supported output tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT32}.
+ *
* Supported tensor rank: up to 4
*
* Inputs:
- * * 0: A tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}.
+ * * 0: A tensor.
*
* Outputs:
- * * 0: The output tensor of same shape as input0, but with
- * {@link OperandType::TENSOR_FLOAT32}.
- *
- * Available since API level 27.
+ * * 0: A tensor with the same shape as input0.
*/
DEQUANTIZE = 6,
@@ -479,6 +490,13 @@
* If a value in Lookups is out of bounds, the operation must fail
* and an error must be reported.
*
+ * Supported value tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported value tensor rank: from 2
+ *
* Inputs:
* * 0: Lookups. A 1-D tensor of {@link OperandType::TENSOR_INT32}.
* The values are indices into the first dimension of Values.
@@ -489,8 +507,8 @@
* * 0: A n-D tensor with the same rank and shape as the Values
* tensor, except for the first dimension which has the same size
* as Lookups' only dimension.
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input1.
*/
EMBEDDING_LOOKUP = 7,
@@ -508,8 +526,6 @@
* Outputs:
* * 0: The output tensor, of the same {@link OperandType} and dimensions as
* the input tensor.
- *
- * Available since API level 27.
*/
FLOOR = 8,
@@ -549,12 +565,9 @@
* invoke on the result.
*
* Outputs:
- * * 0: The output tensor, of shape [batch_size, num_units]. For output
- * tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the following
- * condition must be satisfied:
- * output_scale > input_scale * filter_scale.
- *
- * Available since API level 27.
+ * * 0: The output tensor, of shape [batch_size, num_units]. For
+ * output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the following
+ * condition must be satisfied: output_scale > input_scale * filter_scale.
*/
FULLY_CONNECTED = 9,
@@ -585,6 +598,13 @@
* must be selected. If no entry in Keys has 123456, a slice of zeroes
* must be concatenated.
*
+ * Supported value tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported value tensor rank: from 2
+ *
* Inputs:
* * 0: Lookups. A 1-D {@link OperandType::TENSOR_INT32} tensor with
* shape [ k ].
@@ -598,13 +618,13 @@
*
* Outputs:
* * 0: Output. A tensor with shape [ k …].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input2.
* * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup
* hits (True) or not (False).
* Stored as {@link OperandType::TENSOR_QUANT8_ASYMM} with offset 0
* and scale 1.0f.
* A non-zero byte represents True, a hit. A zero indicates otherwise.
- *
- * Available since API level 27.
*/
HASHTABLE_LOOKUP = 10,
@@ -617,9 +637,6 @@
* input[batch, row, col, channel] /
* sqrt(sum_{c} pow(input[batch, row, col, c], 2))
*
- * For input tensor with more dimensions, independently normalizes each 1-D
- * slice along dimension dim.
- *
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
@@ -627,13 +644,10 @@
* Height, Width, and Channels).
*
* Inputs:
- * * 0: A 4-D tensor, of shape [batches, height, width, depth].
+ * * 0: A 4-D tensor, specifying the tensor to be normalized.
*
* Outputs:
- * * 0: The output 4-D tensor, of the same shape as input
- * [batches, height, width, depth].
- *
- * Available since API level 27.
+ * * 0: A tensor of the same {@link OperandType} and same shape as input0.
*/
L2_NORMALIZATION = 11,
@@ -652,7 +666,8 @@
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
- * Supported tensor rank: 4, with "NHWC" data layout.
+ * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+ * and Channels) data layout.
*
* Both explicit padding and implicit padding are supported.
*
@@ -700,8 +715,6 @@
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
- *
- * Available since API level 27.
*/
L2_POOL_2D = 12,
@@ -729,17 +742,18 @@
* the input.
* * 1: An {@link OperandType::INT32} scalar, specifying the radius of
* the normalization window.
- * * 2: An {@link OperandType::FLOAT32} scalar, specifying the bias, must
- * not be zero.
- * * 3: An {@link OperandType::FLOAT32} scalar, specifying the scale
- * factor, alpha.
- * * 4: An {@link OperandType::FLOAT32} scalar, specifying the exponent,
- * beta.
+ * * 2: A scalar, specifying the bias, must not be zero.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the bias
+ * value must be of {@link OperandType::FLOAT32}.
+ * * 3: A scalar, specifying the scale factor, alpha.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the
+ * alpha value must be of {@link OperandType::FLOAT32}.
+ * * 4: A scalar, specifying the exponent, beta.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the beta
+ * value must be of {@link OperandType::FLOAT32}.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 27.
*/
LOCAL_RESPONSE_NORMALIZATION = 13,
@@ -763,45 +777,53 @@
* * 0: The output tensor of same shape as input0.
* For {@link OperandType::TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 256 and the zeroPoint must be 0.
- *
- * Available since API level 27.
*/
LOGISTIC = 14,
/**
* Projects an input to a bit vector via locality senstive hashing.
*
+ * Supported input tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported input tensor rank: from 1
+ *
* Inputs:
* * 0: Hash functions. Dim.size == 2, DataType: Float.
- * Tensor[0].Dim[0]: Number of hash functions.
- * Tensor[0].Dim[1]: Number of seeds per hash functions.
- * Tensor[0].Dim[1] <= 32 in sparse case.
+ * Tensor[0].Dim[0]: Number of hash functions.
+ * Tensor[0].Dim[1]: Number of projected output bits generated by each
+ * hash function.
+ * If the projection type is Sparse:
+ * Tensor[0].Dim[1] + ceil(log2(Tensor[0].Dim[0])) <= 32
*
* * 1: Input. Dim.size >= 1, no restriction on DataType.
* * 2: Weight. Optional. Dim.size == 1, DataType: Float.
- * If not set, each input element is considered to have the same weight
- * of 1.0.
- * Tensor[1].Dim[0] == Tensor[2].Dim[0]
+ * If not set, each input element is considered to have the same weight
+ * of 1.0.
+ * Tensor[1].Dim[0] == Tensor[2].Dim[0]
* * 3: Type:
- * Sparse: Value LSHProjectionType_SPARSE(=1).
+ * Sparse:
+ * Value LSHProjectionType_SPARSE(=1).
* Computed bit vector is considered to be sparse.
* Each output element is an int32 made up of multiple bits
* computed from hash functions.
*
- * Dense: Value LSHProjectionType_DENSE(=2).
+ * Dense:
+ * Value LSHProjectionType_DENSE(=2).
* Computed bit vector is considered to be dense. Each output
* element represents a bit and can take the value of either
* 0 or 1.
*
* Outputs:
- * * 0: If the projection type is sparse:
- * Output.Dim == { Tensor[0].Dim[0] }
- * A tensor of int32 that represents hash signatures.
- * If the projection type is Dense:
- * Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
- * A flattened tensor that represents projected bit vectors.
+ * * 0: If the projection type is Sparse:
+ * Output.Dim == { Tensor[0].Dim[0] }
+ * A tensor of int32 that represents hash signatures.
*
- * Available since API level 27.
+ * If the projection type is Dense:
+ * Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
+ * A flattened tensor that represents projected bit vectors.
*/
LSH_PROJECTION = 15,
@@ -901,71 +923,54 @@
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
+ * All input and output tensors must be of the same type.
+ *
* Inputs:
* * 0: The input (\f$x_t\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, input_size], where “batch_size” corresponds to the
- * batching dimension, and “input_size” is the size of the input.
+ * A 2-D tensor of shape [batch_size, input_size], where “batch_size”
+ * corresponds to the batching dimension, and “input_size” is the size
+ * of the input.
* * 1: The input-to-input weights (\f$W_{xi}\f$). Optional.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, input_size], where “num_units” corresponds to the
- * number of cell units.
+ * A 2-D tensor of shape [num_units, input_size], where “num_units”
+ * corresponds to the number of cell units.
* * 2: The input-to-forget weights (\f$W_{xf}\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, input_size].
+ * A 2-D tensor of shape [num_units, input_size].
* * 3: The input-to-cell weights (\f$W_{xc}\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, input_size].
+ * A 2-D tensor of shape [num_units, input_size].
* * 4: The input-to-output weights (\f$W_{xo}\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, input_size].
+ * A 2-D tensor of shape [num_units, input_size].
* * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, output_size], where “output_size” corresponds to either
- * the number of cell units (i.e., “num_units”), or the second
- * dimension of the “projection_weights”, if defined.
+ * A 2-D tensor of shape [num_units, output_size], where “output_size”
+ * corresponds to either the number of cell units (i.e., “num_units”),
+ * or the second dimension of the “projection_weights”, if defined.
* * 6: The recurrent-to-forget weights (\f$W_{hf}\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, output_size].
+ * A 2-D tensor of shape [num_units, output_size].
* * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, output_size].
+ * A 2-D tensor of shape [num_units, output_size].
* * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, output_size].
+ * A 2-D tensor of shape [num_units, output_size].
* * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
- * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
- * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
- * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 12:The input gate bias (\f$b_i\f$). Optional.
- * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 13:The forget gate bias (\f$b_f\f$).
- * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 14:The cell bias (\f$b_c\f$).
- * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 15:The output gate bias (\f$b_o\f$).
- * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 16:The projection weights (\f$W_{proj}\f$). Optional.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [output_size, num_units].
+ * A 2-D tensor of shape [output_size, num_units].
* * 17:The projection bias (\f$b_{proj}\f$). Optional.
- * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [output_size].
+ * A 1-D tensor of shape [output_size].
* * 18:The output state (in) (\f$h_{t-1}\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, output_size].
+ * A 2-D tensor of shape [batch_size, output_size].
* * 19:The cell state (in) (\f$C_{t-1}\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, num_units].
+ * A 2-D tensor of shape [batch_size, num_units].
* * 20:The activation function (\f$g\f$).
* A value indicating the activation function:
* <ul>
@@ -984,21 +989,15 @@
*
* Outputs:
* * 0: The scratch buffer.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, num_units * 3] with CIFG, or
+ * A 2-D tensor of shape [batch_size, num_units * 3] with CIFG, or
* [batch_size, num_units * 4] without CIFG.
* * 1: The output state (out) (\f$h_t\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, output_size].
+ * A 2-D tensor of shape [batch_size, output_size].
* * 2: The cell state (out) (\f$C_t\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, num_units].
+ * A 2-D tensor of shape [batch_size, num_units].
* * 3: The output (\f$o_t\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, output_size]. This is effectively the same as the
- * current “output state (out)” value.
- *
- * Available since API level 27.
+ * A 2-D tensor of shape [batch_size, output_size]. This is effectively
+ * the same as the current “output state (out)” value.
*/
LSTM = 16,
@@ -1019,7 +1018,8 @@
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
- * Supported tensor rank: 4, with "NHWC" data layout.
+ * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+ * and Channels) data layout.
*
* Both explicit padding and implicit padding are supported.
*
@@ -1067,8 +1067,8 @@
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
MAX_POOL_2D = 17,
@@ -1106,8 +1106,6 @@
* For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the following condition must be satisfied:
* output_scale > input1_scale * input2_scale.
- *
- * Available since API level 27.
*/
MUL = 18,
@@ -1129,8 +1127,8 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
RELU = 19,
@@ -1151,9 +1149,9 @@
* * 0: A tensor, specifying the input.
*
* Outputs:
- * * 0: The output tensor of same shape as input0.
- *
- * Available since API level 27.
+ * * 0: The output tensor of the same shape as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
RELU1 = 20,
@@ -1175,8 +1173,8 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
RELU6 = 21,
@@ -1205,8 +1203,8 @@
*
* Outputs:
* * 0: The output tensor, of shape specified by the input shape.
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
RESHAPE = 22,
@@ -1220,9 +1218,10 @@
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
- * Supported tensor rank: 4, with "NHWC" data layout.
+ * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+ * and Channels) data layout.
*
- * Inputs:
+ * Inputs (resizing by shape):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input.
* * 1: An {@link OperandType::INT32} scalar, specifying the output
@@ -1233,8 +1232,6 @@
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, new_height, new_width, depth].
- *
- * Available since API level 27.
*/
RESIZE_BILINEAR = 23,
@@ -1257,25 +1254,23 @@
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
+ * The input tensors must all be the same type.
+ *
* Inputs:
* * 0: input.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32} of shape
- * [batch_size, input_size], where “batch_size” corresponds to the
- * batching dimension, and “input_size” is the size of the input.
+ * A 2-D tensor of shape [batch_size, input_size], where “batch_size”
+ * corresponds to the batching dimension, and “input_size” is the size
+ * of the input.
* * 1: weights.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, input_size], where “num_units” corresponds to the
- * number of units.
+ * A 2-D tensor of shape [num_units, input_size], where “num_units”
+ * corresponds to the number of units.
* * 2: recurrent_weights.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, num_units], with columns corresponding to the weights
- * from each unit.
+ * A 2-D tensor of shape [num_units, num_units], with columns
+ * corresponding to the weights from each unit.
* * 3: bias.
- * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 4: hidden state (in).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, num_units].
+ * A 2-D tensor of shape [batch_size, num_units].
* * 5: fused_activation_function.
* An optional {@link FusedActivationFunc} value indicating the
* activation function. If “NONE” is specified then it results in a
@@ -1283,15 +1278,11 @@
*
* Outputs:
* * 0: hidden state (out).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, num_units].
+ * A 2-D tensor of shape [batch_size, num_units].
*
* * 1: output.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, num_units]. This is effectively the same as the
- * current state value.
- *
- * Available since API level 27.
+ * A 2-D tensor of shape [batch_size, num_units]. This is effectively
+ * the same as the current state value.
*/
RNN = 24,
@@ -1306,6 +1297,9 @@
* exp((input[batch, i] - max(input[batch, :])) * beta) /
* sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
*
+ * For input tensor with rank other than 2, the activation will be applied
+ * independently on each 1-D slice along specified dimension.
+ *
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
@@ -1314,15 +1308,15 @@
*
* Inputs:
* * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
- * * 1: An {@link OperandType::FLOAT32} scalar, specifying the positive
- * scaling factor for the exponent, beta.
+ * * 1: A scalar, specifying the positive scaling factor for the exponent,
+ * beta. If input0 is of {@link OperandType::TENSOR_FLOAT32} or
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}, the scalar must be of
+ * {@link OperandType::FLOAT32}.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
* For {@link OperandType::TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 256 and the zeroPoint must be 0.
- *
- * Available since API level 27.
*/
SOFTMAX = 25,
@@ -1344,7 +1338,8 @@
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
- * Supported tensor rank: 4, with "NHWC" data layout.
+ * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+ * and Channels) data layout.
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
@@ -1356,8 +1351,8 @@
* Outputs:
* * 0: The output 4-D tensor, of shape [batches, height/block_size,
* width/block_size, depth_in*block_size*block_size].
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
SPACE_TO_DEPTH = 26,
@@ -1403,25 +1398,23 @@
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
+ * All input tensors must be the same type.
+ *
* Inputs:
* * 0: input.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, input_size], where “batch_size” corresponds to the
- * batching dimension, and “input_size” is the size of the input.
+ * A 2-D tensor of shape [batch_size, input_size], where “batch_size”
+ * corresponds to the batching dimension, and “input_size” is the size
+ * of the input.
* * 1: weights_feature.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, input_size], where “num_units” corresponds to the
- * number of units.
+ * A 2-D tensor of shape [num_units, input_size], where “num_units”
+ * corresponds to the number of units.
* * 2: weights_time.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, memory_size], where “memory_size” corresponds to the
- * fixed-size of the memory.
+ * A 2-D tensor of shape [num_units, memory_size], where “memory_size”
+ * corresponds to the fixed-size of the memory.
* * 3: bias.
- * An optional 1-D tensor of {@link OperandType::TENSOR_FLOAT32},
- * of shape [num_units].
+ * An optional 1-D tensor of shape [num_units].
* * 4: state (in).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, (memory_size - 1) * num_units * rank].
+ * A 2-D tensor of shape [batch_size, (memory_size - 1) * num_units * rank].
* * 5: rank.
* The rank of the SVD approximation.
* * 6: fused_activation_function.
@@ -1431,13 +1424,11 @@
*
* Outputs:
* * 0: state (out).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * A 2-D tensor of the same {@link OperandType} as the inputs, with shape
* [batch_size, (memory_size - 1) * num_units * rank].
* * 1: output.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * A 2-D tensor of the same {@link OperandType} as the inputs, with shape
* [batch_size, num_units].
- *
- * Available since API level 27.
*/
SVDF = 27,
@@ -1458,8 +1449,6 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 27.
*/
TANH = 28,
diff --git a/neuralnetworks/1.0/types.t b/neuralnetworks/1.0/types.t
new file mode 100644
index 0000000..d7b26aa
--- /dev/null
+++ b/neuralnetworks/1.0/types.t
@@ -0,0 +1,431 @@
+%% template file for generating types.hal.
+%% see frameworks/ml/nn/tools/api/README.md.
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.neuralnetworks@1.0;
+
+%insert Operand_1.0_Comment
+enum OperandType : int32_t {
+%insert Operand_1.0
+
+ /**
+ * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+ * alternative to OEM operation and data types.
+ *
+ * OEM specific scalar value.
+ */
+ OEM = 10000,
+
+ /**
+ * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+ * alternative to OEM operation and data types.
+ *
+ * A tensor of OEM specific values.
+ */
+ TENSOR_OEM_BYTE = 10001,
+};
+
+%insert Operation_1.0_Comment
+enum OperationType : int32_t {
+%insert Operation_1.0
+
+ /**
+ * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
+ * OEM operation and data types.
+ *
+ * This operation is OEM specific. It should only be used for OEM
+ * applications.
+ */
+ OEM_OPERATION = 10000,
+};
+
+/**
+ * Fused activation function types.
+ */
+enum FusedActivationFunc : int32_t {
+ NONE = 0,
+ RELU = 1,
+ RELU1 = 2,
+ RELU6 = 3,
+};
+
+/**
+ * How an operand is used.
+ */
+enum OperandLifeTime : int32_t {
+ /**
+ * The operand is internal to the model. It's created by an operation and
+ * consumed by other operations. It must be an output operand of
+ * exactly one operation.
+ */
+ TEMPORARY_VARIABLE,
+
+ /**
+ * The operand is an input of the model. It must not be an output
+ * operand of any operation.
+ *
+ * An operand can't be both input and output of a model.
+ */
+ MODEL_INPUT,
+
+ /**
+ * The operand is an output of the model. It must be an output
+ * operand of exactly one operation.
+ *
+ * An operand can't be both input and output of a model.
+ */
+ MODEL_OUTPUT,
+
+ /**
+ * The operand is a constant found in Model.operandValues. It must
+ * not be an output operand of any operation.
+ */
+ CONSTANT_COPY,
+
+ /**
+ * The operand is a constant that was specified via a Memory
+ * object. It must not be an output operand of any operation.
+ */
+ CONSTANT_REFERENCE,
+
+ /**
+ * The operand does not have a value. This is valid only for optional
+ * arguments of operations.
+ */
+ NO_VALUE,
+};
+
+/**
+ * Status of a device.
+ */
+enum DeviceStatus : int32_t {
+ AVAILABLE,
+ BUSY,
+ OFFLINE,
+ UNKNOWN,
+};
+
+/**
+ * Performance information for the reference workload.
+ *
+ * Used by a driver to report its performance characteristics.
+ */
+struct PerformanceInfo {
+ /**
+ * Ratio of the time taken by the driver to execute the
+ * workload compared to the time the CPU would take for the
+ * same workload. A lower number is better.
+ */
+ float execTime;
+
+ /**
+ * Ratio of the energy used by the driver compared to what
+ * the CPU would use for doing the same workload. A lower number
+ * is better.
+ */
+ float powerUsage;
+};
+
+/**
+ * The capabilities of a driver.
+ */
+struct Capabilities {
+ /**
+ * Driver performance when operating on float32 data.
+ */
+ PerformanceInfo float32Performance;
+
+ /**
+ * Driver performance when operating on asymmetric 8-bit quantized data.
+ */
+ PerformanceInfo quantized8Performance;
+};
+
+/**
+ * Describes the location of a data object.
+ */
+struct DataLocation {
+ /**
+ * The index of the memory pool where this location is found.
+ */
+ uint32_t poolIndex;
+
+ /**
+ * Offset in bytes from the start of the pool.
+ */
+ uint32_t offset;
+
+ /**
+ * The length of the data in bytes.
+ */
+ uint32_t length;
+};
+
+/**
+ * Describes one operand of the model's graph.
+ */
+struct Operand {
+ /**
+ * Data type of the operand.
+ */
+ OperandType type;
+
+ /**
+ * Dimensions of the operand.
+ *
+ * For a scalar operand, dimensions.size() must be 0.
+ *
+ * For a tensor operand, dimensions.size() must be at least 1;
+ * however, any of the dimensions may be unspecified.
+ *
+ * A tensor operand with all dimensions specified has "fully
+ * specified" dimensions. Whenever possible (i.e., whenever the
+ * dimensions are known at model construction time), a tensor
+ * operand should have (but is not required to have) fully
+ * specified dimensions, in order to enable the best possible
+ * performance.
+ *
+ * If a tensor operand's dimensions are not fully specified, the
+ * dimensions of the operand are deduced from the operand
+ * dimensions and values of the operation for which that operand
+ * is an output.
+ *
+ * In the following situations, a tensor operand's dimensions must
+ * be fully specified:
+ *
+ * . The operand has lifetime CONSTANT_COPY or
+ * CONSTANT_REFERENCE.
+ *
+ * . The operand has lifetime MODEL_INPUT or MODEL_OUTPUT. Fully
+ * specified dimensions must either be present in the
+ * Operand or they must be provided in the corresponding
+ * RequestArgument.
+ * EXCEPTION: If the input or output is optional and omitted
+ * (by setting the hasNoValue field of the corresponding
+ * RequestArgument to true) then it need not have fully
+ * specified dimensions.
+ *
+ * A tensor operand with some number of unspecified dimensions is
+ * represented by setting each unspecified dimension to 0.
+ */
+ vec<uint32_t> dimensions;
+
+ /**
+ * The number of times this operand appears as an operation input.
+ *
+ * (For example, if this operand appears once in one operation's
+ * input list, and three times in another operation's input list,
+ * then numberOfConsumers = 4.)
+ */
+ uint32_t numberOfConsumers;
+
+ /**
+ * Quantized scale of the operand.
+ *
+ * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM or
+ * TENSOR_INT32.
+ */
+ float scale;
+
+ /**
+ * Quantized zero-point offset of the operand.
+ *
+ * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
+ */
+ int32_t zeroPoint;
+
+ /**
+ * How the operand is used.
+ */
+ OperandLifeTime lifetime;
+
+ /**
+ * Where to find the data for this operand.
+ * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or
+ * NO_VALUE:
+ * - All the fields must be 0.
+ * If the lifetime is CONSTANT_COPY:
+ * - location.poolIndex is 0.
+ * - location.offset is the offset in bytes into Model.operandValues.
+ * - location.length is set.
+ * If the lifetime is CONSTANT_REFERENCE:
+ * - location.poolIndex is set.
+ * - location.offset is the offset in bytes into the specified pool.
+ * - location.length is set.
+ */
+ DataLocation location;
+};
+
+/**
+ * Describes one operation of the model's graph.
+ */
+struct Operation {
+ /**
+ * The operation type.
+ */
+ OperationType type;
+
+ /**
+ * Describes the table that contains the indexes of the inputs of the
+ * operation. The offset is the index in the operandIndexes table.
+ */
+ vec<uint32_t> inputs;
+
+ /**
+ * Describes the table that contains the indexes of the outputs of the
+ * operation. The offset is the index in the operandIndexes table.
+ */
+ vec<uint32_t> outputs;
+};
+
+/**
+ * A Neural Network Model.
+ *
+ * This includes not only the execution graph, but also constant data such as
+ * weights or scalars added at construction time. The only information that
+ * might not be known is the shape of the input tensors.
+ */
+struct Model {
+ /**
+ * All operands included in the model.
+ */
+ vec<Operand> operands;
+
+ /**
+ * All operations included in the model.
+ *
+ * The operations are sorted into execution order. Every operand
+ * with lifetime MODEL_OUTPUT or TEMPORARY_VARIABLE must be
+ * written before it is read.
+ */
+ vec<Operation> operations;
+
+ /**
+ * Input indexes of the model. There must be at least one.
+ *
+ * Each value corresponds to the index of the operand in "operands".
+ */
+ vec<uint32_t> inputIndexes;
+
+ /**
+ * Output indexes of the model. There must be at least one.
+ *
+ * Each value corresponds to the index of the operand in "operands".
+ */
+ vec<uint32_t> outputIndexes;
+
+ /**
+ * A byte buffer containing operand data that were copied into the model.
+ *
+ * An operand's value must be located here if and only if Operand::lifetime
+ * equals OperandLifeTime::CONSTANT_COPY.
+ */
+ vec<uint8_t> operandValues;
+
+ /**
+ * A collection of shared memory pools containing operand values.
+ *
+ * An operand's value must be located here if and only if Operand::lifetime
+ * equals OperandLifeTime::CONSTANT_REFERENCE.
+ */
+ vec<memory> pools;
+};
+
+/**
+ * Metadata information specifying the location of the input or output data and
+ * any updates to the input or output operand.
+ */
+struct RequestArgument {
+ /**
+ * If true, the argument does not have a value. This can be used for
+ * operations that take optional arguments. If true, the fields of location
+ * are set to 0 and the dimensions vector is left empty.
+ */
+ bool hasNoValue;
+
+ /**
+ * The location within one of the memory pools passed in the Request.
+ */
+ DataLocation location;
+
+ /**
+ * Updated dimension information.
+ *
+ * If dimensions.size() > 0, dimension information was provided
+ * along with the argument. This can be the case for models that
+ * accept inputs of varying size. This can't change the rank, just
+ * the value of the dimensions that were unspecified in the
+ * model. If dimensions.size() > 0, then all dimensions must be
+ * specified here; and any dimension that was specified in the
+ * model must have the same value here.
+ *
+ * If the dimensions in the model are not fully specified, then
+ * they must be fully specified here, unless hasNoValue is set to
+ * true. If the dimensions in the model are fully specified, then
+ * either dimensions.size() may be 0, or the dimensions in the
+ * model must be identical to the dimensions here.
+ */
+ vec<uint32_t> dimensions;
+};
+
+/**
+ * Inputs to be sent to and outputs to be retrieved from a prepared model.
+ *
+ * A Request serves two primary tasks:
+ * 1) Provides the input and output data to be used when executing the model.
+ * 2) Specifies any updates to the input operand metadata that were left
+ * unspecified at model preparation time.
+ *
+ * An output must not overlap with any other output, with an input, or
+ * with an operand of lifetime CONSTANT_REFERENCE.
+ */
+struct Request {
+ /**
+ * Input data and information to be used in the execution of a prepared
+ * model.
+ *
+ * The index of the input corresponds to the index in Model.inputIndexes.
+ * E.g., input[i] corresponds to Model.inputIndexes[i].
+ */
+ vec<RequestArgument> inputs;
+
+ /**
+ * Output data and information to be used in the execution of a prepared
+ * model.
+ *
+ * The index of the output corresponds to the index in Model.outputIndexes.
+ * E.g., output[i] corresponds to Model.outputIndexes[i].
+ */
+ vec<RequestArgument> outputs;
+
+ /**
+ * A collection of shared memory pools containing operand data for both the
+ * inputs and the outputs to a model.
+ */
+ vec<memory> pools;
+};
+
+/**
+ * Return status of a function.
+ */
+enum ErrorStatus : int32_t {
+ NONE,
+ DEVICE_UNAVAILABLE,
+ GENERAL_FAILURE,
+ OUTPUT_INSUFFICIENT_SIZE,
+ INVALID_ARGUMENT,
+};
diff --git a/neuralnetworks/1.1/types.hal b/neuralnetworks/1.1/types.hal
index 73705bb..3d78fb6 100644
--- a/neuralnetworks/1.1/types.hal
+++ b/neuralnetworks/1.1/types.hal
@@ -26,7 +26,6 @@
* The type of an operation in a model.
*/
enum OperationType : @1.0::OperationType {
-
/**
* BatchToSpace for N-dimensional tensors.
*
@@ -41,7 +40,8 @@
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
- * Supported tensor rank: 4
+ * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+ * and Channels) data layout.
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be reshaped
@@ -51,8 +51,8 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
BATCH_TO_SPACE_ND = 29,
@@ -91,8 +91,6 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
*/
DIV = 30,
@@ -126,8 +124,8 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be same as input0.
*/
MEAN = 31,
@@ -138,7 +136,8 @@
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM} (the pad value is undefined)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * (the pad value is undefined)
*
* Supported tensor rank: up to 4
*
@@ -160,11 +159,8 @@
* of the padding:
* 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.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
PAD = 32,
@@ -182,8 +178,10 @@
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * (the pad value is undefined)
*
- * Supported tensor rank: 4
+ * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+ * and Channels) data layout.
*
* Inputs:
* * 0: An n-D tensor, specifying the input.
@@ -201,8 +199,8 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
SPACE_TO_BATCH_ND = 33,
@@ -232,8 +230,8 @@
* * 0: A tensor of the same {@link OperandType} as input0. Contains the
* same data as input, but has one or more dimensions of size 1
* removed.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
SQUEEZE = 34,
@@ -278,8 +276,8 @@
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0 and rank (n - k),
* where k is the number of bits set in shrink_axis_mask.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
STRIDED_SLICE = 35,
@@ -318,8 +316,6 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
*/
SUB = 36,
@@ -345,11 +341,10 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
TRANSPOSE = 37,
-
};
/**
diff --git a/neuralnetworks/1.1/types.t b/neuralnetworks/1.1/types.t
new file mode 100644
index 0000000..75ac2e7
--- /dev/null
+++ b/neuralnetworks/1.1/types.t
@@ -0,0 +1,158 @@
+%% template file for generating types.hal.
+%% see frameworks/ml/nn/tools/api/README.md.
+/*
+ * Copyright (C) 2018 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.neuralnetworks@1.1;
+
+import @1.0::Operand;
+import @1.0::OperationType;
+import @1.0::PerformanceInfo;
+
+/**
+ * Operation types.
+ *
+ * The type of an operation in a model.
+ */
+enum OperationType : @1.0::OperationType {
+%insert Operation_1.1
+};
+
+/**
+ * The capabilities of a driver.
+ */
+struct Capabilities {
+ /**
+ * Driver performance when operating on float32 data.
+ */
+ PerformanceInfo float32Performance;
+
+ /**
+ * Driver performance when operating on asymmetric 8-bit quantized data.
+ */
+ PerformanceInfo quantized8Performance;
+
+ /**
+ * Driver performance when operating on float32 data but performing
+ * calculations with range and/or precision as low as that of the IEEE
+ * 754 16-bit floating-point format.
+ */
+ PerformanceInfo relaxedFloat32toFloat16Performance;
+};
+
+/**
+ * Describes one operation of the model's graph.
+ */
+struct Operation {
+ /**
+ * The operation type.
+ */
+ OperationType type;
+
+ /**
+ * Describes the table that contains the indexes of the inputs of the
+ * operation. The offset is the index in the operandIndexes table.
+ */
+ vec<uint32_t> inputs;
+
+ /**
+ * Describes the table that contains the indexes of the outputs of the
+ * operation. The offset is the index in the operandIndexes table.
+ */
+ vec<uint32_t> outputs;
+};
+
+/**
+ * A Neural Network Model.
+ *
+ * This includes not only the execution graph, but also constant data such as
+ * weights or scalars added at construction time. The only information that
+ * may not be known is the shape of the input tensors.
+ */
+struct Model {
+ /**
+ * All operands included in the model.
+ */
+ vec<Operand> operands;
+
+ /**
+ * All operations included in the model.
+ *
+ * The operations are sorted into execution order. Every operand
+ * with lifetime MODEL_OUTPUT or TEMPORARY_VARIABLE must be
+ * written before it is read.
+ */
+ vec<Operation> operations;
+
+ /**
+ * Input indexes of the model. There must be at least one.
+ *
+ * Each value corresponds to the index of the operand in "operands".
+ */
+ vec<uint32_t> inputIndexes;
+
+ /**
+ * Output indexes of the model. There must be at least one.
+ *
+ * Each value corresponds to the index of the operand in "operands".
+ */
+ vec<uint32_t> outputIndexes;
+
+ /**
+ * A byte buffer containing operand data that were copied into the model.
+ *
+ * An operand's value must be located here if and only if Operand::lifetime
+ * equals OperandLifeTime::CONSTANT_COPY.
+ */
+ vec<uint8_t> operandValues;
+
+ /**
+ * A collection of shared memory pools containing operand values.
+ *
+ * An operand's value must be located here if and only if Operand::lifetime
+ * equals OperandLifeTime::CONSTANT_REFERENCE.
+ */
+ vec<memory> pools;
+
+ /**
+ * 'true' indicates TENSOR_FLOAT32 may be calculated with range and/or
+ * precision as low as that of the IEEE 754 16-bit floating-point format.
+ * 'false' indicates TENSOR_FLOAT32 must be calculated using at least the
+ * range and precision of the IEEE 754 32-bit floating-point format.
+ */
+ bool relaxComputationFloat32toFloat16;
+};
+
+/**
+ * Execution preferences.
+ */
+enum ExecutionPreference : int32_t {
+ /**
+ * Prefer executing in a way that minimizes battery drain.
+ * This is desirable for compilations that will be executed often.
+ */
+ LOW_POWER = 0,
+ /**
+ * Prefer returning a single answer as fast as possible, even if this causes
+ * more power consumption.
+ */
+ FAST_SINGLE_ANSWER = 1,
+ /**
+ * Prefer maximizing the throughput of successive frames, for example when
+ * processing successive frames coming from the camera.
+ */
+ SUSTAINED_SPEED = 2,
+};
diff --git a/neuralnetworks/1.2/types.hal b/neuralnetworks/1.2/types.hal
index f368ce2..837ced5 100644
--- a/neuralnetworks/1.2/types.hal
+++ b/neuralnetworks/1.2/types.hal
@@ -43,8 +43,6 @@
*
* Values of this operand type are either true or false. A zero value
* represents false; any other value represents true.
- *
- * Available since API level 29.
*/
BOOL = 6,
/**
@@ -55,14 +53,10 @@
* realValue = integerValue * scale.
*
* scale is a 32 bit floating point with value greater than zero.
- *
- * Available since API level 29.
*/
TENSOR_QUANT16_SYMM = 7,
/**
* A tensor of IEEE 754 16 bit floating point values.
- *
- * Available since API level 29.
*/
TENSOR_FLOAT16 = 8,
/**
@@ -70,14 +64,10 @@
*
* Values of this operand type are either true or false. A zero value
* represents false; any other value represents true.
- *
- * Available since API level 29.
*/
TENSOR_BOOL8 = 9,
/**
* An IEEE 754 16 bit floating point scalar value.
- *
- * Available since API level 29.
*/
FLOAT16 = 10,
/**
@@ -90,14 +80,13 @@
* - scales: an array of positive 32 bit floating point values.
* The size of the scales array must be equal to dimensions[channelDim].
*
+ *{@link SymmPerChannelQuantParams} must hold the parameters for an Operand of this type.
* The channel dimension of this tensor must not be unknown (dimensions[channelDim] != 0).
*
* The formula is:
* realValue[..., C, ...] =
* integerValue[..., C, ...] * scales[C]
* where C is an index in the Channel dimension.
- *
- * Available since API level 29.
*/
TENSOR_QUANT8_SYMM_PER_CHANNEL = 11,
/**
@@ -110,8 +99,6 @@
*
* The formula is:
* real_value = (integer_value - zeroPoint) * scale.
- *
- * Available since API level 29.
*/
TENSOR_QUANT16_ASYMM = 12,
/**
@@ -122,20 +109,19 @@
* realValue = integerValue * scale.
*
* scale is a 32 bit floating point with value greater than zero.
- *
- * Available since API level 29.
*/
TENSOR_QUANT8_SYMM = 13,
+
/*
- * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
- * OEM operation and data types.
+ * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+ * alternative to OEM operation and data types.
*
* OEM specific scalar value.
* OEM = 10000,
*/
/*
- * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
- * OEM operation and data types.
+ * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+ * alternative to OEM operation and data types.
*
* A tensor of OEM specific values.
* TENSOR_OEM_BYTE = 10001,
@@ -166,6 +152,7 @@
* The type of an operation in a model.
*/
enum OperationType : int32_t {
+
/**
* Adds two tensors, element-wise.
*
@@ -187,12 +174,12 @@
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
- * Since API level 29, generic zero-sized input tensor is supported. Zero
+ * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
* dimension is only compatible with 0 or 1. The size of the output
* dimension is zero if either of corresponding input dimension is zero.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -202,14 +189,16 @@
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandType}, and compatible dimensions
* as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scales and zeroPoint can be different from input0 scale and zeroPoint.
* * 2: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
*
* Outputs:
* * 0: The sum, a tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
ADD = @1.1::OperationType:ADD,
@@ -227,7 +216,7 @@
* ) / sum(1)
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -235,13 +224,14 @@
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
*
* Both explicit padding and implicit padding are supported.
*
* Inputs (explicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
- * the input. Since API level 29, zero batches is supported for this
- * tensor.
+ * the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
* * 1: An {@link OperandType::INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 2: An {@link OperandType::INT32} scalar, specifying the padding on
@@ -263,12 +253,12 @@
* invoke on the result.
* * 10: 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.
+ * Available since HAL version 1.2.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
- * the input. Since API level 29, zero batches is supported for this
- * tensor.
+ * the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
* * 1: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
@@ -285,13 +275,13 @@
* invoke on the result.
* * 7: 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.
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
AVERAGE_POOL_2D = @1.1::OperationType:AVERAGE_POOL_2D,
@@ -302,33 +292,34 @@
* dimensions except the dimension along the concatenation axis.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM} (full support since API
- * level 29, see the input section)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * (full support since HAL version 1.2, see the input section)
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0 ~ n-1: The list of n input tensors, of shape
* [D0, D1, ..., Daxis(i), ..., Dm].
- * Before API level 29, all input tensors of
+ * Before HAL version 1.2, all input tensors of
* {@link OperandType::TENSOR_QUANT8_ASYMM}
* must have the same scale and zeroPoint as the output tensor.
- * Since API level 29, zero-sized tensors are supported.
+ * Since HAL version 1.2, zero-sized tensors are supported.
* * n: An {@link OperandType::INT32} scalar, specifying the
* concatenation axis.
*
* Outputs:
* * 0: The output, a tensor of the same {@link OperandType} as the input
* tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
- *
- * Available since API level 27.
+ * Since HAL version 1.2, for a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint values can be different from
+ * input tensors. Before HAL version 1.2 they have to be the same as for the input tensors.
*/
CONCATENATION = @1.1::OperationType:CONCATENATION,
/**
- * Performs an 2-D convolution operation.
+ * Performs a 2-D convolution operation.
*
* The CONV_2D op sweeps a 2-D filter that can mix channels together over a
* batch of images, applying the filter to each window of each image of the
@@ -354,7 +345,7 @@
* * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
* * * input.scale * filter.scale).
*
- * Available since API level 29:
+ * Available since HAL version 1.2:
* * 16 bit floating point:
* * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
*
@@ -368,27 +359,29 @@
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
*
* Both explicit padding and implicit padding are supported.
*
* Inputs (explicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
- * specifying the input. Since API level 29, zero batches is supported
- * for this tensor.
+ * specifying the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
* * 1: A 4-D tensor, of shape
* [depth_out, filter_height, filter_width, depth_in], specifying the
- * filter. For tensor of type
- * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
- * dimension (extraParams.channelQuant.channelDim) must be set to 0.
+ * filter.
+ * For tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+ * the channel dimension (SymmPerChannelQuantParams::channelDim)
+ * must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
- * tensor of type {@link OperandType::TENSOR_FLOAT32} or
- * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
+ * tensor of type {@link OperandType::TENSOR_FLOAT32}
+ * or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same
* type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
- * of 0 and bias_scale == input_scale * filter_scale. For filter tensor
- * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
- * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
- * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * of 0 and bias_scale == input_scale * filter_scale.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+ * and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 3: An {@link OperandType::INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
@@ -407,36 +400,37 @@
* invoke on the result.
* * 10: 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.
+ * Available since HAL version 1.2.
* * 11: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on width dimension. If this input is set,
* input 12 (dilation factor for height) must be specified as well.
- * Available since API level 29.
+ * Available since HAL version 1.2.
* * 12: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on height dimension. If this input is set,
* input 11 (dilation factor for width) must be specified as well.
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
- * specifying the input. Since API level 29, zero batches is supported
- * for this tensor.
+ * specifying the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
* * 1: A 4-D tensor, of shape
* [depth_out, filter_height, filter_width, depth_in], specifying the
- * filter. For tensor of type
- * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
- * dimension (extraParams.channelQuant.channelDim) must be set to 0.
+ * filter.
+ * For tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+ * the channel dimension (SymmPerChannelQuantParams::channelDim)
+ * must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
- * tensor of type {@link OperandType::TENSOR_FLOAT32} or
- * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
+ * tensor of type {@link OperandType::TENSOR_FLOAT32}
+ * or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same
* type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
- * of 0 and bias_scale == input_scale * filter_scale. For filter tensor
- * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
- * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
- * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * of 0 and bias_scale == input_scale * filter_scale.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+ * and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 3: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
@@ -450,26 +444,23 @@
* invoke on the result.
* * 7: 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.
+ * Available since HAL version 1.2.
* * 8: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on width dimension. If this input is set,
* input 9 (dilation factor for height) must be specified as well.
- * Available since API level 29.
+ * Available since HAL version 1.2.
* * 9: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on height dimension. If this input is set,
* input 8 (dilation factor for width) must be specified as well.
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
- * [batches, out_height, out_width, depth_out]. Before API level 29,
- * for output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the
- * following condition must be satisfied:
- * output_scale > input_scale * filter_scale
- *
- * Available since API level 27.
+ * [batches, out_height, out_width, depth_out].
+ * Before HAL version 1.2, for output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the following condition must be satisfied: output_scale > input_scale * filter_scale
*/
CONV_2D = @1.1::OperationType:CONV_2D,
@@ -504,7 +495,7 @@
* * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
* * * input.scale * filter.scale).
*
- * Available since API level 29:
+ * Available since HAL version 1.2:
* * 16 bit floating point:
* * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
*
@@ -518,6 +509,7 @@
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
*
* Both explicit padding and implicit padding are supported.
*
@@ -525,18 +517,19 @@
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
* specifying the input.
* * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
- * specifying the filter. For tensor of type
- * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
- * dimension (extraParams.channelQuant.channelDim) must be set to 3.
+ * specifying the filter.
+ * For tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+ * the channel dimension (SymmPerChannelQuantParams::channelDim)
+ * must be set to 3.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
- * tensor of type {@link OperandType::TENSOR_FLOAT32} or
- * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
+ * tensor of type {@link OperandType::TENSOR_FLOAT32}
+ * or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same
* type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
- * of 0 and bias_scale == input_scale * filter_scale. For filter tensor
- * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
- * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
- * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * of 0 and bias_scale == input_scale * filter_scale.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+ * and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 3: An {@link OperandType::INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
@@ -557,17 +550,17 @@
* invoke on the result.
* * 11: 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.
+ * Available since HAL version 1.2.
* * 12: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on width dimension. If this input is set,
* input 13 (dilation factor for height) must be specified as well.
- * Available since API level 29.
+ * Available since HAL version 1.2.
* * 13: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on height dimension. If this input is set,
* input 12 (dilation factor for width) must be specified as well.
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
@@ -575,14 +568,14 @@
* * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
* specifying the filter.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
- * tensor of type {@link OperandType::TENSOR_FLOAT32} or
- * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
+ * tensor of type {@link OperandType::TENSOR_FLOAT32}
+ * or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same
* type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
- * of 0 and bias_scale == input_scale * filter_scale. For filter tensor
- * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
- * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
- * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * of 0 and bias_scale == input_scale * filter_scale.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+ * and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 3: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
@@ -598,27 +591,24 @@
* invoke on the result.
* * 8: 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.
+ * Available since HAL version 1.2.
* * 9: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on width dimension. If this input is set,
* input 10 (dilation factor for height) must be specified as well.
- * Available since API level 29.
+ * Available since HAL version 1.2.
* * 10: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on height dimension. If this input is set,
* input 9 (dilation factor for width) must be specified as well.
- * Available since API level 29.
-
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
- * [batches, out_height, out_width, depth_out]. Before API level 29,
- * for output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the
- * following condition must be satisfied:
+ * [batches, out_height, out_width, depth_out]. Before HAL version 1.2, for
+ * output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the following condition must be satisfied:
* output_scale > input_scale * filter_scale
- *
- * Available since API level 27.
*/
DEPTHWISE_CONV_2D = @1.1::OperationType:DEPTHWISE_CONV_2D,
@@ -638,7 +628,7 @@
* be divisible by block_size * block_size
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -646,6 +636,7 @@
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
@@ -655,13 +646,13 @@
* of the input depth.
* * 2: 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.
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: The output 4-D tensor, of shape [batch, height*block_size,
* width*block_size, depth/(block_size*block_size)].
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
DEPTH_TO_SPACE = @1.1::OperationType:DEPTH_TO_SPACE,
@@ -674,22 +665,21 @@
*
* Supported input tensor {@link OperandType}:
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
- * * {@link OperandType::TENSOR_QUANT8_SYMM} (since API level 29)
- * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} (since API level 29)
+ * * {@link OperandType::TENSOR_QUANT8_SYMM} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} (since HAL version 1.2)
*
* Supported output tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}.
*
* Supported tensor rank: up to 4
*
* Inputs:
- * * 0: A tensor. Since API level 29, this tensor may be zero-sized.
+ * * 0: A tensor.
+ * Since HAL version 1.2, this tensor may be zero-sized.
*
* Outputs:
* * 0: A tensor with the same shape as input0.
- *
- * Available since API level 27.
*/
DEQUANTIZE = @1.1::OperationType:DEQUANTIZE,
@@ -730,8 +720,8 @@
* * 0: A n-D tensor with the same rank and shape as the Values
* tensor, except for the first dimension which has the same size
* as Lookups' only dimension.
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input1.
*/
EMBEDDING_LOOKUP = @1.1::OperationType:EMBEDDING_LOOKUP,
@@ -739,7 +729,7 @@
* Computes element-wise floor() on the input tensor.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
@@ -750,8 +740,6 @@
* Outputs:
* * 0: The output tensor, of the same {@link OperandType} and dimensions as
* the input tensor.
- *
- * Available since API level 27.
*/
FLOOR = @1.1::OperationType:FLOOR,
@@ -764,7 +752,7 @@
* outputs = activation(inputs * weights’ + bias)
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -777,8 +765,8 @@
* [batch_size, input_size], where "input_size" corresponds to the
* number of inputs to the layer, matching the second dimension of
* weights, and "batch_size" is calculated by dividing the number of
- * elements by "input_size". Since API level 29, zero batch_size is
- * supported for this tensor.
+ * elements by "input_size".
+ * Since HAL version 1.2, zero batch_size is supported for this tensor.
* * 1: A 2-D tensor, specifying the weights, of shape
* [num_units, input_size], where "num_units" corresponds to the number
* of output nodes.
@@ -793,12 +781,9 @@
* invoke on the result.
*
* Outputs:
- * * 0: The output tensor, of shape [batch_size, num_units]. Before API
- * level 29, For output tensor of {@link
- * OperandType::TENSOR_QUANT8_ASYMM}, the following condition must be
- * satisfied: output_scale > input_scale * filter_scale.
- *
- * Available since API level 27.
+ * * 0: The output tensor, of shape [batch_size, num_units]. Before HAL version 1.2, for
+ * output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the following
+ * condition must be satisfied: output_scale > input_scale * filter_scale.
*/
FULLY_CONNECTED = @1.1::OperationType:FULLY_CONNECTED,
@@ -849,13 +834,13 @@
*
* Outputs:
* * 0: Output. A tensor with shape [ k …].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input2.
* * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup
* hits (True) or not (False).
* Stored as {@link OperandType::TENSOR_QUANT8_ASYMM} with offset 0
* and scale 1.0f.
* A non-zero byte represents True, a hit. A zero indicates otherwise.
- *
- * Available since API level 27.
*/
HASHTABLE_LOOKUP = @1.1::OperationType:HASHTABLE_LOOKUP,
@@ -872,12 +857,12 @@
* 1-D slice along dimension dim.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since API level 29)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
*
* Supported tensor rank: up to 4
- * Tensors with rank less than 4 are only supported since API level 29.
+ * Tensors with rank less than 4 are only supported since HAL version 1.2.
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be normalized.
@@ -885,14 +870,12 @@
* specifying the dimension normalization would be performed on.
* Negative index is used to specify axis from the end (e.g. -1 for
* the last axis). Must be in the range [-n, n).
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} and same shape as input0.
* For {@link OperandType::TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 128 and the zeroPoint must be 128.
- *
- * Available since API level 27.
*/
L2_NORMALIZATION = @1.1::OperationType:L2_NORMALIZATION,
@@ -909,20 +892,21 @@
* sum(1))
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
*
* Both explicit padding and implicit padding are supported.
*
* Inputs (explicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
- * the input. Since API level 29, zero batches is supported for this
- * tensor.
+ * the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
* * 1: An {@link OperandType::INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 2: An {@link OperandType::INT32} scalar, specifying the padding on
@@ -944,12 +928,12 @@
* invoke on the result.
* * 10: 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.
+ * Available since HAL version 1.2.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
- * the input. Since API level 29, zero batches is supported for this
- * tensor.
+ * the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
* * 1: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
@@ -966,13 +950,11 @@
* invoke on the result.
* * 7: 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.
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
- *
- * Available since API level 27.
*/
L2_POOL_2D = @1.1::OperationType:L2_POOL_2D,
@@ -994,11 +976,11 @@
* 1-D slice along specified dimension.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
- * Tensors with rank less than 4 are only supported since API level 29.
+ * Tensors with rank less than 4 are only supported since HAL version 1.2.
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
@@ -1011,10 +993,10 @@
* For input tensor of {@link OperandType::TENSOR_FLOAT32}, the bias
* value must be of {@link OperandType::FLOAT32}.
* * 3: A scalar, specifying the scale factor, alpha.
- * For input tensor of {@link OperandType::TENSOR_FLOAT16}, the alpha
- * value must be of {@link OperandType::FLOAT16}.
- * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the alpha
- * value must be of {@link OperandType::FLOAT32}.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT16}, the
+ * alpha value must be of {@link OperandType::FLOAT16}.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the
+ * alpha value must be of {@link OperandType::FLOAT32}.
* * 4: A scalar, specifying the exponent, beta.
* For input tensor of {@link OperandType::TENSOR_FLOAT16}, the beta
* value must be of {@link OperandType::FLOAT16}.
@@ -1024,12 +1006,10 @@
* specifying the dimension normalization would be performed on.
* Negative index is used to specify axis from the end (e.g. -1 for
* the last axis). Must be in the range [-n, n).
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 27.
*/
LOCAL_RESPONSE_NORMALIZATION = @1.1::OperationType:LOCAL_RESPONSE_NORMALIZATION,
@@ -1041,22 +1021,20 @@
* output = 1 / (1 + exp(-input))
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
- * * 0: A tensor, specifying the input. Since API level 29, this tensor may
- * be zero-sized.
+ * * 0: A tensor, specifying the input.
+ * Since HAL version 1.2, this tensor may be zero-sized.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
* For {@link OperandType::TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 256 and the zeroPoint must be 0.
- *
- * Available since API level 27.
*/
LOGISTIC = @1.1::OperationType:LOGISTIC,
@@ -1064,7 +1042,7 @@
* Projects an input to a bit vector via locality senstive hashing.
*
* Supported input tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
@@ -1086,7 +1064,7 @@
* Tensor[1].Dim[0] == Tensor[2].Dim[0]
* * 3: Type:
* Sparse:
- * Value LSHProjectionType_SPARSE(=3) (since API level 29).
+ * Value LSHProjectionType_SPARSE(=3) (since HAL version 1.2).
* Computed bit vector is considered to be sparse.
* Each output element is an int32 made up of multiple bits
* computed from hash functions.
@@ -1107,14 +1085,12 @@
* Outputs:
* * 0: If the projection type is Sparse:
* Output.Dim == { Tensor[0].Dim[0] }
- * A tensor of int32 that represents hash signatures,
+ * A tensor of int32 that represents hash signatures.
*
* If the projection type is Dense:
* Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
* A flattened tensor that represents projected bit vectors.
- *
- * Available since API level 27.
- * The offset value for sparse projections was added in API level 29.
+ * The offset value for sparse projections was added in HAL version 1.2.
*/
LSH_PROJECTION = @1.1::OperationType:LSH_PROJECTION,
@@ -1170,7 +1146,7 @@
* matrix, each element of which is the product of the corresponding
* elements of the input matrices.
*
- * Since API level 29 LSTM supports layer normalization.
+ * Since HAL version 1.2 LSTM supports layer normalization.
* In case layer normalization is used, the inputs to internal activation
* functions (sigmoid and \f$g\f$) are normalized, rescaled and recentered
* following an approach from section 3.1 from
@@ -1197,7 +1173,7 @@
* * The projection bias (\f$b_{proj}\f$) may (but not required to) have a
* value if the recurrent projection layer exists, and should otherwise
* have no value.
- * * (API level >= 29) The four layer normalization weights either all have
+ * * (HAL version 1.2 or later) The four layer normalization weights either all have
* values or none of them have values. Additionally, if CIFG is used,
* input layer normalization weights tensor is omitted and the other layer
* normalization weights either all have values or none of them have
@@ -1228,7 +1204,7 @@
* Jimmy Ba et al. "Layer Normalization"
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
*
* All input and output tensors must be of the same type.
@@ -1291,24 +1267,24 @@
* * 21:The clipping threshold (\f$t_{cell}\f$) for the cell state, such
* that values are bound within [-cell_clip, cell_clip]. If set to 0.0
* then clipping is disabled.
- * Until API level 29 this scalar must be of type {@link
- * FLOAT32}. Since API level 29, if all the input
+ * Until HAL version 1.2 this scalar must be of type {@link
+ * OperandType::FLOAT32}. Since HAL version 1.2, if all the input
* tensors have type {@link OperandType::TENSOR_FLOAT32}, this
* scalar must be of the type {@link OperandType::FLOAT32},
* otherwise if all the input tensors have the type {@link
- * TENSOR_FLOAT16}, this scalar must be of type {@link
- * FLOAT16}.
+ * OperandType::TENSOR_FLOAT16}, this scalar must be of type {@link
+ * OperandType::FLOAT16}.
* * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the
* projection layer, such that values are bound within
* [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
- * Until API level 29 this scalar must be of type {@link
- * FLOAT32}. Since API level 29, if all the input
+ * Until HAL version 1.2 this scalar must be of type {@link
+ * OperandType::FLOAT32}. Since HAL version 1.2, if all the input
* tensors have type {@link OperandType::TENSOR_FLOAT32}, this
* scalar must be of the type {@link OperandType::FLOAT32},
* otherwise if all the input tensors have the type {@link
- * TENSOR_FLOAT16}, this scalar must be of type {@link
- * FLOAT16}.
- * Since API level 29 there are additional inputs to this op:
+ * OperandType::TENSOR_FLOAT16}, this scalar must be of type {@link
+ * OperandType::FLOAT16}.
+ * Since HAL version 1.2 there are additional inputs to this op:
* * 23:The input layer normalization weights.
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
* to activation at input gate.
@@ -1333,8 +1309,6 @@
* * 3: The output (\f$o_t\f$).
* A 2-D tensor of shape [batch_size, output_size]. This is effectively
* the same as the current “output state (out)” value.
- *
- * Available since API level 27.
*/
LSTM = @1.1::OperationType:LSTM,
@@ -1352,7 +1326,7 @@
* )
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -1360,13 +1334,14 @@
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
*
* Both explicit padding and implicit padding are supported.
*
* Inputs (explicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
- * the input. Since API level 29, zero batches is supported for this
- * tensor.
+ * the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
* * 1: An {@link OperandType::INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 2: An {@link OperandType::INT32} scalar, specifying the padding on
@@ -1388,12 +1363,12 @@
* invoke on the result.
* * 10: 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.
+ * Available since HAL version 1.2.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
- * the input. Since API level 29, zero batches is supported for this
- * tensor.
+ * the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
* * 1: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
@@ -1410,13 +1385,13 @@
* invoke on the result.
* * 7: 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.
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
MAX_POOL_2D = @1.1::OperationType:MAX_POOL_2D,
@@ -1435,15 +1410,15 @@
* of the input operands. It starts with the trailing dimensions, and works
* its way forward.
*
- * Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
- * * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM}
- *
- * Since API level 29, generic zero-sized input tensor is supported. Zero
+ * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
* dimension is only compatible with 0 or 1. The size of the output
* dimension is zero if either of corresponding input dimension is zero.
*
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: up to 4
*
* Inputs:
@@ -1459,8 +1434,6 @@
* For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the following condition must be satisfied:
* output_scale > input1_scale * input2_scale.
- *
- * Available since API level 27.
*/
MUL = @1.1::OperationType:MUL,
@@ -1472,20 +1445,20 @@
* output = max(0, input)
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
- * * 0: A tensor, specifying the input. Since API level 29, this tensor may
- * be zero-sized.
+ * * 0: A tensor, specifying the input.
+ * Since HAL version 1.2, this tensor may be zero-sized.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
RELU = @1.1::OperationType:RELU,
@@ -1497,20 +1470,20 @@
* output = min(1.f, max(-1.f, input))
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
- * * 0: A tensor, specifying the input. Since API level 29, this tensor may
- * be zero-sized.
+ * * 0: A tensor, specifying the input.
+ * Since HAL version 1.2, this tensor may be zero-sized.
*
* Outputs:
- * * 0: The output tensor of same shape as input0.
- *
- * Available since API level 27.
+ * * 0: The output tensor of the same shape as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
RELU1 = @1.1::OperationType:RELU1,
@@ -1522,20 +1495,20 @@
* output = min(6, max(0, input))
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
- * * 0: A tensor, specifying the input. Since API level 29, this tensor may
- * be zero-sized.
+ * * 0: A tensor, specifying the input.
+ * Since HAL version 1.2, this tensor may be zero-sized.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
RELU6 = @1.1::OperationType:RELU6,
@@ -1546,7 +1519,7 @@
* tensor, but with a newly specified shape.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -1565,8 +1538,8 @@
*
* Outputs:
* * 0: The output tensor, of shape specified by the input shape.
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
RESHAPE = @1.1::OperationType:RESHAPE,
@@ -1578,30 +1551,31 @@
* same as corner pixels of input.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since API level 29)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
*
* Both resizing by shape and resizing by scale are supported.
*
* Inputs (resizing by shape):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
- * the input. Since API level 29, zero batches is supported for this
- * tensor.
+ * the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
* * 1: 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.
+ * Available since HAL version 1.2.
*
- * Inputs (resizing by scale, since API level 29):
+ * Inputs (resizing by scale, since HAL version 1.2):
* * 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 width_scale, the scaling factor of the width
@@ -1622,8 +1596,8 @@
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, new_height, new_width, depth].
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
RESIZE_BILINEAR = @1.1::OperationType:RESIZE_BILINEAR,
@@ -1644,7 +1618,7 @@
* argument (if not “NONE”).
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
*
* The input tensors must all be the same type.
@@ -1676,8 +1650,6 @@
* * 1: output.
* A 2-D tensor of shape [batch_size, num_units]. This is effectively
* the same as the current state value.
- *
- * Available since API level 27.
*/
RNN = @1.1::OperationType:RNN,
@@ -1696,34 +1668,32 @@
* independently on each 1-D slice along specified dimension.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
- * Tensors with rank other than 2 or 4 are only supported since API level 29.
+ * Tensors with rank other than 2 or 4 are only supported since HAL version 1.2.
*
* Inputs:
- * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped. Since
- * API level 29, this tensor may be zero-sized.
+ * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
+ * Since HAL version 1.2, this tensor may be zero-sized.
* * 1: A scalar, specifying the positive scaling factor for the exponent,
* beta. If input0 is of {@link OperandType::TENSOR_FLOAT32} or
* {@link OperandType::TENSOR_QUANT8_ASYMM}, the scalar must be of
- * {@link OperandType::FLOAT32}. If input0 is of {@link
- * OperandType::TENSOR_FLOAT16}, then the scalar must be of {@link
- * OperandType::FLOAT16}.
+ * {@link OperandType::FLOAT32}.
+ * If input0 is of {@link OperandType::TENSOR_FLOAT16}, then the
+ * scalar must be of {@link OperandType::FLOAT16}.
* * 2: An optional {@link OperandType::INT32} scalar, default to -1,
* specifying the dimension the activation would be performed on.
* Negative index is used to specify axis from the end (e.g. -1 for
* the last axis). Must be in the range [-n, n).
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
* For {@link OperandType::TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 256 and the zeroPoint must be 0.
- *
- * Available since API level 27.
*/
SOFTMAX = @1.1::OperationType:SOFTMAX,
@@ -1742,7 +1712,7 @@
* The input tensor's height and width must be divisible by block_size.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -1750,6 +1720,7 @@
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
@@ -1759,13 +1730,13 @@
* input height and width.
* * 2: 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.
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: The output 4-D tensor, of shape [batches, height/block_size,
* width/block_size, depth_in*block_size*block_size].
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
SPACE_TO_DEPTH = @1.1::OperationType:SPACE_TO_DEPTH,
@@ -1809,7 +1780,7 @@
* the filters.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
*
* All input tensors must be the same type.
@@ -1843,8 +1814,6 @@
* * 1: output.
* A 2-D tensor of the same {@link OperandType} as the inputs, with shape
* [batch_size, num_units].
- *
- * Available since API level 27.
*/
SVDF = @1.1::OperationType:SVDF,
@@ -1856,22 +1825,20 @@
* output = tanh(input)
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since API level 29)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
*
* Supported tensor rank: up to 4.
*
* Inputs:
- * * 0: A tensor, specifying the input. Since API level 29, this tensor may
- * be zero-sized.
+ * * 0: A tensor, specifying the input.
+ * Since HAL version 1.2, this tensor may be zero-sized.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
* For {@link OperandType::TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 128 and the zeroPoint must be 128.
- *
- * Available since API level 27.
*/
TANH = @1.1::OperationType:TANH,
@@ -1886,7 +1853,7 @@
* This is the reverse of SpaceToBatch.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -1894,6 +1861,7 @@
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be reshaped
@@ -1906,8 +1874,8 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
BATCH_TO_SPACE_ND = @1.1::OperationType:BATCH_TO_SPACE_ND,
@@ -1931,12 +1899,12 @@
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
- * Since API level 29, generic zero-sized input tensor is supported. Zero
+ * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
* dimension is only compatible with 0 or 1. The size of the output
* dimension is zero if either of corresponding input dimension is zero.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
@@ -1951,8 +1919,6 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
*/
DIV = @1.1::OperationType:DIV,
@@ -1965,7 +1931,7 @@
* length 1.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -1987,21 +1953,21 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be same as input0.
*/
MEAN = @1.1::OperationType:MEAN,
/**
- * Pads a tensor with zeros.
+ * Pads a tensor.
*
* This operation pads a tensor according to the specified paddings.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM} (full support since API
- * level 29, see the output section)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * (full support since HAL version 1.2, see the output section)
*
* Supported tensor rank: up to 4
*
@@ -2023,12 +1989,12 @@
* of the padding:
* output0.dimension[i] =
* padding[i, 0] + input0.dimension[i] + padding[i, 1]
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*
- * 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.
+ * NOTE: Before HAL version 1.2, the pad value for
+ * {@link OperandType::TENSOR_QUANT8_ASYMM} is undefined.
+ * Since HAL version 1.2, the pad value is always the logical zero.
*/
PAD = @1.1::OperationType:PAD,
@@ -2044,14 +2010,16 @@
* dimensions of the input are optionally zero padded according to paddings.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * (full support since HAL version 1.2, see the output section)
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
*
* Inputs:
* * 0: An n-D tensor, specifying the input.
@@ -2068,12 +2036,16 @@
* end of dimension i.
* * 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.
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*
- * Available since API level 28.
+ * NOTE: Before HAL version 1.2, the pad value for
+ * {@link OperandType::TENSOR_QUANT8_ASYMM} is undefined.
+ * Since HAL version 1.2, the pad value is always the logical zero.
*/
SPACE_TO_BATCH_ND = @1.1::OperationType:SPACE_TO_BATCH_ND,
@@ -2086,7 +2058,7 @@
* dimensions by specifying the axes (input1).
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -2104,8 +2076,8 @@
* * 0: A tensor of the same {@link OperandType} as input0. Contains the
* same data as input, but has one or more dimensions of size 1
* removed.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
SQUEEZE = @1.1::OperationType:SQUEEZE,
@@ -2119,7 +2091,7 @@
* reverse slice.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -2151,8 +2123,8 @@
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0 and rank (n - k),
* where k is the number of bits set in shrink_axis_mask.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
STRIDED_SLICE = @1.1::OperationType:STRIDED_SLICE,
@@ -2176,14 +2148,14 @@
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
- * Since API level 29, generic zero-sized input tensor is supported. Zero
+ * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
* dimension is only compatible with 0 or 1. The size of the output
* dimension is zero if either of corresponding input dimension is zero.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since API level 29)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
*
* Supported tensor rank: up to 4
*
@@ -2197,8 +2169,8 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
SUB = @1.1::OperationType:SUB,
@@ -2212,7 +2184,7 @@
* regular matrix transpose on 2-D input Tensors.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -2220,14 +2192,14 @@
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be transposed.
- * Since API level 29, this tensor may be zero-sized.
+ * Since HAL version 1.2, this tensor may be zero-sized.
* * 1: An optional 1-D Tensor of {@link OperandType::TENSOR_INT32},
* the permutation of the dimensions of the input tensor.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
TRANSPOSE = @1.1::OperationType:TRANSPOSE,
@@ -2245,8 +2217,6 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 29.
*/
ABS = 38,
@@ -2269,8 +2239,6 @@
*
* Outputs:
* * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor.
- *
- * Available since API level 29.
*/
// There is no underscore in ARG_MAX to avoid name conflict with
// the macro defined in libc/kernel/uapi/linux/limits.h.
@@ -2295,8 +2263,6 @@
*
* Outputs:
* * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor.
- *
- * Available since API level 29.
*/
ARGMIN = 40, // See ARGMAX for naming discussion.
@@ -2341,8 +2307,8 @@
* * 0: A tensor of the same {@link OperandType} as input0, with shape
* [num_rois, num_classes * 4], specifying the coordinates of each
* output bounding box for each class, with format [x1, y1, x2, y2].
- *
- * Available since API level 29.
+ * For type of {@link OperandType::TENSOR_QUANT16_ASYMM}, the
+ * scale must be 0.125 and the zero point must be 0.
*/
AXIS_ALIGNED_BBOX_TRANSFORM = 41,
@@ -2482,17 +2448,15 @@
* then clipping is disabled.
* If all the input tensors have type {@link OperandType::TENSOR_FLOAT32},
* this scalar must be of the type {@link OperandType::FLOAT32},
- * otherwise if all the input tensors have the type {@link
- * TENSOR_FLOAT16}, this scalar must be of type {@link
- * FLOAT16}.
+ * otherwise if all the input tensors have the type {@link OperandType::TENSOR_FLOAT16},
+ * this scalar must be of type {@link OperandType::FLOAT16}.
* * 50: The clipping threshold for the output from the
* projection layer, such that values are bound within
* [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
* If all the input tensors have type {@link OperandType::TENSOR_FLOAT32},
* this scalar must be of the type {@link OperandType::FLOAT32},
- * otherwise if all the input tensors have the type {@link
- * TENSOR_FLOAT16}, this scalar must be of type {@link
- * FLOAT16}.
+ * otherwise if all the input tensors have the type {@link OperandType::TENSOR_FLOAT16},
+ * this scalar must be of type {@link OperandType::FLOAT16}.
* * 51: merge_outputs
* An {@link OperandType::BOOL} scalar specifying if the outputs
* from forward and backward cells should be merged.
@@ -2539,8 +2503,6 @@
* A 3-D tensor of shape:
* If time-major: [max_time, batch_size, bw_output_size]
* If batch-major: [batch_size, max_time, bw_output_size]
- *
- * Available since API level 29.
*/
BIDIRECTIONAL_SEQUENCE_LSTM = 42,
@@ -2658,8 +2620,6 @@
* (timeMajor). If it is set to true, then the shape is set to
* [maxTime, batchSize, bwNumUnits], otherwise the shape is set to
* [batchSize, maxTime, bwNumUnits].
- *
- * Available since API level 29.
*/
BIDIRECTIONAL_SEQUENCE_RNN = 43,
@@ -2737,8 +2697,6 @@
* * 3: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
* [num_output_rois], specifying the batch index of each box. Boxes
* with the same batch index are grouped together.
- *
- * Available since API level 29.
*/
BOX_WITH_NMS_LIMIT = 44,
@@ -2762,8 +2720,6 @@
*
* Outputs:
* * 0: A tensor with the same shape as input0.
- *
- * Available since API level 29.
*/
CAST = 45,
@@ -2800,8 +2756,8 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} and same shape as input0.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
CHANNEL_SHUFFLE = 46,
@@ -2856,14 +2812,14 @@
* * 11: A scalar, score_threshold. Boxes with scores lower than the
* threshold are filtered before sending to the NMS algorithm. The
* scalar must be of {@link OperandType::FLOAT16} if input0 is of
- * {@link OperandType::TENSOR_FLOAT16} and of {@link
- * OperandType::FLOAT32} if input0 is of {@link
- * OperandType::TENSOR_FLOAT32}.
+ * {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} if input0 is of
+ * {@link OperandType::TENSOR_FLOAT32}.
* * 12: A scalar, specifying the IoU threshold for hard NMS. The scalar
- * must be of {@link OperandType::FLOAT16} if input0 is of {@link
- * OperandType::TENSOR_FLOAT16} and of {@link
- * OperandType::FLOAT32} if input0 is of {@link
- * OperandType::TENSOR_FLOAT32}.
+ * must be of {@link OperandType::FLOAT16} if input0 is of
+ * {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} if input0 is of
+ * {@link OperandType::TENSOR_FLOAT32}.
* * 13: An {@link OperandType::BOOL} scalar, set to true to include
* background class in the list of label map for the output, set
* to false to not include the background. When the background
@@ -2882,8 +2838,6 @@
* output detection.
* * 3: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape [batches],
* specifying the number of valid output detections for each batch.
- *
- * Available since API level 29.
*/
DETECTION_POSTPROCESSING = 47,
@@ -2908,8 +2862,6 @@
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
- *
- * Available since API level 29.
*/
EQUAL = 48,
@@ -2927,8 +2879,6 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 29.
*/
EXP = 49,
@@ -2956,8 +2906,8 @@
* Outputs:
* * 0: An (n + 1)-D tensor with the same {@link OperandType} and data as
* input0.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
EXPAND_DIMS = 50,
@@ -2994,8 +2944,8 @@
*
* Outputs:
* * 0: An (n + k - 1)-D tensor with the same {@link OperandType} as input0.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
GATHER = 51,
@@ -3074,8 +3024,6 @@
* * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
* [num_output_rois], specifying the batch index of each box. Boxes
* with the same batch index are grouped together.
- *
- * Available since API level 29.
*/
GENERATE_PROPOSALS = 52,
@@ -3100,8 +3048,6 @@
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
- *
- * Available since API level 29.
*/
GREATER = 53,
/**
@@ -3125,8 +3071,6 @@
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
- *
- * Available since API level 29.
*/
GREATER_EQUAL = 54,
@@ -3191,7 +3135,8 @@
* [depth_out, filter_height, filter_width, depth_group], specifying
* the filter, where depth_out must be divisible by num_groups. For
* tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
- * the channel dimension must be set to 0.
+ * the channel dimension (channelDim at
+ * {@link SymmPerChannelQuantParams}) must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link OperandType::TENSOR_FLOAT32} or
* {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
@@ -3229,7 +3174,8 @@
* [depth_out, filter_height, filter_width, depth_group], specifying
* the filter, where depth_out must be divisible by num_groups. For
* tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
- * the channel dimension must be set to 0.
+ * the channel dimension (SymmPerChannelQuantParams::channelDim)
+ * must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link OperandType::TENSOR_FLOAT32} or
* {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
@@ -3258,8 +3204,8 @@
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth_out].
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
GROUPED_CONV_2D = 55,
@@ -3300,12 +3246,14 @@
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0, with shape
* [num_boxes, num_keypoints], specifying score of the keypoints.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from input0 scale and zeroPoint.
* * 1: A tensor of the same {@link OperandType} as input1, with shape
* [num_boxes, num_keypoints, 2], specifying the location of
* the keypoints, the second dimension is organized as
* [keypoint_x, keypoint_y].
- *
- * Available since API level 29.
+ * For type of {@link OperandType::TENSOR_QUANT16_ASYMM}, the
+ * scale must be 0.125 and the zero point must be 0.
*/
HEATMAP_MAX_KEYPOINT = 56,
@@ -3339,26 +3287,24 @@
* * 0: An n-D tensor, specifying the tensor to be normalized.
* * 1: A scalar, specifying gamma, the scale applied to the normalized
* tensor. The scalar must be of {@link OperandType::FLOAT16} if
- * input0 is of {@link OperandType::TENSOR_FLOAT16} and of {@link
- * OperandType::FLOAT32} if input0 is of {@link
- * OperandType::TENSOR_FLOAT32}.
+ * input0 is of {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} if input0 is of
+ * {@link OperandType::TENSOR_FLOAT32}.
* * 2: A scalar, specifying beta, the offset applied to the normalized
* tensor. The scalar must be of {@link OperandType::FLOAT16} if
- * input0 is of {@link OperandType::TENSOR_FLOAT16} and of {@link
- * OperandType::FLOAT32} if input0 is of {@link
- * OperandType::TENSOR_FLOAT32}.
+ * input0 is of {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} if input0 is of
+ * {@link OperandType::TENSOR_FLOAT32}.
* * 3: A scalar, specifying epsilon, the small value added to variance to
* avoid dividing by zero. The scalar must be of {@link OperandType::FLOAT16} if
- * input0 is of {@link OperandType::TENSOR_FLOAT16} and of {@link
- * OperandType::FLOAT32} if input0 is of {@link
- * OperandType::TENSOR_FLOAT32}.
+ * input0 is of {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} if input0 is of
+ * {@link OperandType::TENSOR_FLOAT32}.
* * 4: An {@link OperandType::BOOL} scalar, set to true to specify
* NCHW data layout for input0 and output0. Set to false for NHWC.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} and same shape as input0.
- *
- * Available since API level 29.
*/
INSTANCE_NORMALIZATION = 57,
@@ -3383,8 +3329,6 @@
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
- *
- * Available since API level 29.
*/
LESS = 58,
@@ -3409,8 +3353,6 @@
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
- *
- * Available since API level 29.
*/
LESS_EQUAL = 59,
@@ -3428,8 +3370,6 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 29.
*/
LOG = 60,
@@ -3450,8 +3390,6 @@
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
- *
- * Available since API level 29.
*/
LOGICAL_AND = 61,
@@ -3468,8 +3406,6 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 29.
*/
LOGICAL_NOT = 62,
@@ -3490,8 +3426,6 @@
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
- *
- * Available since API level 29.
*/
LOGICAL_OR = 63,
@@ -3523,8 +3457,6 @@
* Outputs:
* * 0: The output tensor of the same {@link OperandType} and shape as
* input0.
- *
- * Available since API level 29.
*/
LOG_SOFTMAX = 64,
@@ -3543,11 +3475,13 @@
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandType} and compatible dimensions
* with input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scales and zeroPoint can be different from input0 scale and zeroPoint.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
MAXIMUM = 65,
@@ -3566,11 +3500,13 @@
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandType} and compatible dimensions
* with input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scales and zeroPoint can be different from input0 scale and zeroPoint.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
MINIMUM = 66,
@@ -3589,8 +3525,6 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 29.
*/
NEG = 67,
@@ -3615,8 +3549,6 @@
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
- *
- * Available since API level 29.
*/
NOT_EQUAL = 68,
@@ -3657,8 +3589,8 @@
* of the padding:
* output0.dimension[i] =
* padding[i, 0] + input0.dimension[i] + padding[i, 1]
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
PAD_V2 = 69,
@@ -3689,8 +3621,6 @@
*
* Outputs:
* * 0: An output tensor.
- *
- * Available since API level 29.
*/
POW = 70,
@@ -3728,8 +3658,8 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be diffent from the input0 scale and zeroPoint.
*/
PRELU = 71,
@@ -3752,8 +3682,6 @@
* Outputs:
* * 0: The output tensor of same shape as input0, but with
* {@link OperandType::TENSOR_QUANT8_ASYMM}.
- *
- * Available since API level 29.
*/
QUANTIZE = 72,
@@ -3879,8 +3807,6 @@
* Outputs:
* * 0: A 2-D {@link OperandType::TENSOR_INT32} tensor with shape
* [batches, samples], containing the drawn samples.
- *
- * Available since API level 29.
*/
RANDOM_MULTINOMIAL = 74,
@@ -3906,8 +3832,6 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 29.
*/
REDUCE_ALL = 75,
@@ -3933,8 +3857,6 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 29.
*/
REDUCE_ANY = 76,
@@ -3962,8 +3884,8 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
REDUCE_MAX = 77,
@@ -3991,8 +3913,8 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
REDUCE_MIN = 78,
@@ -4018,8 +3940,6 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 29.
*/
REDUCE_PROD = 79,
@@ -4045,8 +3965,6 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 29.
*/
REDUCE_SUM = 80,
@@ -4064,7 +3982,7 @@
* interpolation.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -4105,8 +4023,8 @@
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0. The output
* shape is [num_rois, out_height, out_width, depth].
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from the input0 scale and zeroPoint.
*/
ROI_ALIGN = 81,
@@ -4156,8 +4074,8 @@
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0. The output
* shape is [num_rois, out_height, out_width, depth].
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
ROI_POOLING = 82,
@@ -4175,8 +4093,6 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 29.
*/
RSQRT = 83,
@@ -4201,9 +4117,13 @@
* true) or input2 (if false).
* * 1: An input tensor of the same shape as input0.
* * 2: An input tensor of the same shape and type as input1.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scales and zeroPoint can be different from input1 scale and zeroPoint.
*
* Outputs:
* * 0: A tensor of the same type and shape as input1 and input2.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*
*/
SELECT = 84,
@@ -4222,8 +4142,6 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 29.
*/
SIN = 85,
@@ -4235,7 +4153,6 @@
* for each dimension. The size is specified as a 1-D tensor containing
* either size of a slice along corresponding dimension or -1. In the latter
* case, all the remaining elements in dimension are included in the slice.
- * Slice size in each dimension cannot be zero.
*
* A sum of begin offset and a size of a slice must not exceed size of a
* corresponding dimension.
@@ -4257,8 +4174,8 @@
*
* Outputs:
* * 0: An n-D tensor of the same type as the input containing the slice.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * its scale and zeroPoint has to be same as the input0 scale and zeroPoint.
*/
SLICE = 86,
@@ -4282,8 +4199,8 @@
*
* Outputs:
* * 0 ~ (num_splits - 1): Resulting subtensors.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
SPLIT = 87,
@@ -4301,8 +4218,6 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 29.
*/
SQRT = 88,
@@ -4330,8 +4245,8 @@
*
* Outputs:
* * 0: A tiled tensor of the same {@link OperandType} and rank as `input`.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
TILE = 89,
@@ -4357,10 +4272,10 @@
* Outputs:
* * 0: An n-D tensor of the same type as the input, containing the k
* largest elements along each last dimensional slice.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
* * 1: An n-D tensor of type {@link OperandType::TENSOR_INT32}
* containing the indices of values within the last dimension of input.
- *
- * Available since API level 29.
*/
TOPK_V2 = 90,
@@ -4374,7 +4289,7 @@
* The output dimensions are functions of the filter dimensions, stride, and
* padding.
*
- * Supported tensor {@link OperandCode} configurations:
+ * Supported tensor {@link OperandType} configurations:
* * 16 bit floating point:
* * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
*
@@ -4406,7 +4321,7 @@
* [depth_out, filter_height, filter_width, depth_in], specifying the
* filter. For tensor of type
* {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
- * dimension (extraParams.channelQuant.channelDim) must be set to 0.
+ * dimension (SymmPerChannelQuantParams::channelDim) must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link OperandType::TENSOR_FLOAT32} or
* {@link OperandType::TENSOR_FLOAT16}, the bias should be of the
@@ -4443,7 +4358,7 @@
* [depth_out, filter_height, filter_width, depth_in], specifying the
* filter. For tensor of type
* {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
- * dimension (extraParams.channelQuant.channelDim) must be set to 0.
+ * dimension (SymmPerChannelQuantParams::channelDim) must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link OperandType::TENSOR_FLOAT32} or
* {@link OperandType::TENSOR_FLOAT16}, the bias should be of the
@@ -4473,8 +4388,8 @@
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth_out].
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
TRANSPOSE_CONV_2D = 91,
@@ -4584,8 +4499,6 @@
* A 3-D tensor of shape:
* If time-major: [max_time, batch_size, output_size]
* If batch-major: [batch_size, max_time, output_size]
- *
- * Available since API level 29.
*/
UNIDIRECTIONAL_SEQUENCE_LSTM = 92,
@@ -4641,8 +4554,6 @@
* it is set to 1, then the output has a shape [maxTime, batchSize,
* numUnits], otherwise the output has a shape [batchSize, maxTime,
* numUnits].
- *
- * Available since API level 29.
*/
UNIDIRECTIONAL_SEQUENCE_RNN = 93,
@@ -4696,8 +4607,8 @@
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, new_height, new_width, depth].
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
RESIZE_NEAREST_NEIGHBOR = 94,
diff --git a/neuralnetworks/1.2/types.t b/neuralnetworks/1.2/types.t
new file mode 100644
index 0000000..d197f6b
--- /dev/null
+++ b/neuralnetworks/1.2/types.t
@@ -0,0 +1,725 @@
+%% template file for generating types.hal.
+%% see frameworks/ml/nn/tools/api/README.md.
+/*
+ * Copyright (C) 2018 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.neuralnetworks@1.2;
+
+import @1.0::DataLocation;
+import @1.0::ErrorStatus;
+import @1.0::OperandLifeTime;
+import @1.0::OperandType;
+import @1.0::PerformanceInfo;
+import @1.1::OperationType;
+
+import android.hidl.safe_union@1.0::Monostate;
+
+enum Constant : uint32_t {
+ /**
+ * The byte size of the cache token.
+ */
+ BYTE_SIZE_OF_CACHE_TOKEN = 32,
+
+ /**
+ * The maximum number of files for each type of cache in compilation caching.
+ */
+ MAX_NUMBER_OF_CACHE_FILES = 32,
+};
+
+enum OperandType : @1.0::OperandType {
+%insert Operand_1.2
+%insert OEMDeprecationAndOperandTypeRangeMaxComment
+};
+
+/**
+ * The range of operand values in the OperandType enum.
+ */
+enum OperandTypeRange : uint32_t {
+ BASE_MIN = 0,
+ FUNDAMENTAL_MIN = 0,
+%insert Operand_1.2_MAX
+ OEM_MIN = 10000,
+ OEM_MAX = 10001,
+ BASE_MAX = 0xFFFF,
+};
+
+/**
+ * Operation types.
+ *
+ * The type of an operation in a model.
+ */
+enum OperationType : int32_t {
+
+%insert Operation_1.0
+
+%insert Operation_1.1
+
+%insert Operation_1.2
+
+ /**
+ * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
+ * OEM operation and data types.
+ *
+ * This operation is OEM specific. It should only be used for OEM
+ * applications.
+ */
+ OEM_OPERATION = @1.1::OperationType:OEM_OPERATION,
+ /* ADDING A NEW FUNDAMENTAL OPERATION REQUIRES UPDATING THE VALUE OF
+ * OperationTypeRange::FUNDAMENTAL_MAX.
+ */
+ /* ADDING A NEW OEM OPERATION REQUIRES UPDATING THE VALUE OF
+ * OperationTypeRange::OEM_MAX.
+ */
+};
+
+/**
+ * The range of values in the OperationType enum.
+ */
+enum OperationTypeRange : uint32_t {
+ BASE_MIN = 0,
+ FUNDAMENTAL_MIN = 0,
+%insert Operation_1.2_MAX
+ OEM_MIN = 10000,
+ OEM_MAX = 10000,
+ BASE_MAX = 0xFFFF,
+};
+
+/**
+ * Device types.
+ *
+ * The type of NNAPI device.
+ */
+enum DeviceType : int32_t {
+ // Leaving 0 unused as it means unknown type in NDK NNAPI. There is no
+ // HAL equivalent of unknown type and a 1.2 HAL implementation must belong
+ // to one of the categories below.
+ /** The device does not fall into any category below. */
+ OTHER = 1,
+ /** The device runs NNAPI models on single or multi-core CPU. */
+ CPU = 2,
+ /** The device can run NNAPI models and also accelerate graphics APIs such
+ * as OpenGL ES and Vulkan. */
+ GPU = 3,
+ /** Dedicated accelerator for Machine Learning workloads. */
+ ACCELERATOR = 4,
+};
+
+/**
+ * The capabilities of a driver.
+ *
+ * Performance of an operation comes from the type of its first operand.
+ * This represents performance for non extension operand types.
+ */
+struct Capabilities {
+ /**
+ * Driver performance when operating on float32 data but performing
+ * calculations with range and/or precision as low as that of the IEEE
+ * 754 16-bit floating-point format.
+ */
+ PerformanceInfo relaxedFloat32toFloat16PerformanceScalar;
+ PerformanceInfo relaxedFloat32toFloat16PerformanceTensor;
+
+ /**
+ * Driver performance when operating on a particular data type.
+ * In the case of float32 data, this is used when the calculations
+ * are not relaxed.
+ */
+ struct OperandPerformance {
+ OperandType type;
+ PerformanceInfo info;
+ };
+
+ /**
+ * Performance by operand type. Must be sorted by OperandType.
+ * If a particular OperandType is not present in operandPerformance,
+ * its performance is treated as { .execTime = FLT_MAX, .powerUsage = FLT_MAX }.
+ */
+ vec<OperandPerformance> operandPerformance;
+};
+
+/**
+ * Describes one operation of the model's graph.
+ */
+struct Operation {
+ /**
+ * The operation type.
+ *
+ * Besides the values listed in {@link OperationType}, any value above
+ * {@link OperationTypeRange::BASE_MAX} is possible and should be interpreted
+ * as an extension type according to {@link Model::extensionNameToPrefix}.
+ */
+ OperationType type;
+
+ /**
+ * Describes the table that contains the indexes of the inputs of the
+ * operation. The offset is the index in the operandIndexes table.
+ */
+ vec<uint32_t> inputs;
+
+ /**
+ * Describes the table that contains the indexes of the outputs of the
+ * operation. The offset is the index in the operandIndexes table.
+ */
+ vec<uint32_t> outputs;
+};
+
+/**
+ * Parameters for TENSOR_QUANT8_SYMM_PER_CHANNEL operand.
+ */
+struct SymmPerChannelQuantParams {
+ /** Array of scaling values for each channel. Each value must be greater than zero. */
+ vec<float> scales;
+ /** Index of the channel dimension */
+ uint32_t channelDim;
+};
+
+/**
+ * Describes one operand of the model's graph.
+ */
+struct Operand {
+ /**
+ * The data type.
+ *
+ * Besides the values listed in {@link OperandType}, any value above
+ * {@link OperandTypeRange::BASE_MAX} is possible and should be interpreted
+ * as an extension type according to {@link Model::extensionNameToPrefix}.
+ */
+ OperandType type;
+
+ /**
+ * Dimensions of the operand.
+ *
+ * For a scalar operand, dimensions.size() must be 0.
+ *
+ * A tensor operand with all dimensions specified has "fully
+ * specified" dimensions. Whenever possible (i.e., whenever the
+ * dimensions are known at model construction time), a tensor
+ * operand should have (but is not required to have) fully
+ * specified dimensions, in order to enable the best possible
+ * performance.
+ *
+ * If a tensor operand's dimensions are not fully specified, the
+ * dimensions of the operand are deduced from the operand
+ * dimensions and values of the operation for which that operand
+ * is an output.
+ *
+ * In the following situations, a tensor operand's dimensions must
+ * be fully specified:
+ *
+ * . The operand has lifetime CONSTANT_COPY or
+ * CONSTANT_REFERENCE.
+ *
+ * . The operand has lifetime MODEL_INPUT. Fully
+ * specified dimensions must either be present in the
+ * Operand or they must be provided in the corresponding
+ * RequestArgument.
+ * EXCEPTION: If the input is optional and omitted
+ * (by setting the hasNoValue field of the corresponding
+ * RequestArgument to true) then it need not have fully
+ * specified dimensions.
+ *
+ * A tensor operand with some number of unspecified dimensions is
+ * represented by setting each unspecified dimension to 0.
+ *
+ * A tensor operand with unspecified rank is represented by providing
+ * an empty dimensions vector.
+ */
+ vec<uint32_t> dimensions;
+
+ /**
+ * The number of times this operand appears as an operation input.
+ *
+ * (For example, if this operand appears once in one operation's
+ * input list, and three times in another operation's input list,
+ * then numberOfConsumers = 4.)
+ */
+ uint32_t numberOfConsumers;
+
+ /**
+ * Quantized scale of the operand.
+ *
+ * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM or
+ * TENSOR_INT32.
+ */
+ float scale;
+
+ /**
+ * Quantized zero-point offset of the operand.
+ *
+ * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
+ */
+ int32_t zeroPoint;
+
+ /**
+ * How the operand is used.
+ */
+ OperandLifeTime lifetime;
+
+ /**
+ * Where to find the data for this operand.
+ * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or
+ * NO_VALUE:
+ * - All the fields must be 0.
+ * If the lifetime is CONSTANT_COPY:
+ * - location.poolIndex is 0.
+ * - location.offset is the offset in bytes into Model.operandValues.
+ * - location.length is set.
+ * If the lifetime is CONSTANT_REFERENCE:
+ * - location.poolIndex is set.
+ * - location.offset is the offset in bytes into the specified pool.
+ * - location.length is set.
+ */
+ DataLocation location;
+
+ /**
+ * Additional parameters specific to a particular operand type.
+ */
+ safe_union ExtraParams {
+ /**
+ * No additional parameters.
+ */
+ Monostate none;
+
+ /**
+ * Symmetric per-channel quantization parameters.
+ *
+ * Only applicable to operands of type TENSOR_QUANT8_SYMM_PER_CHANNEL.
+ */
+ SymmPerChannelQuantParams channelQuant;
+
+ /**
+ * Extension operand parameters.
+ *
+ * The framework treats this as an opaque data blob.
+ * The format is up to individual extensions.
+ */
+ vec<uint8_t> extension;
+ } extraParams;
+};
+
+/**
+ * A Neural Network Model.
+ *
+ * This includes not only the execution graph, but also constant data such as
+ * weights or scalars added at construction time. The only information that
+ * may not be known is the shape of the input tensors.
+ */
+struct Model {
+ /**
+ * All operands included in the model.
+ */
+ vec<Operand> operands;
+
+ /**
+ * All operations included in the model.
+ *
+ * The operations are sorted into execution order. Every operand
+ * with lifetime MODEL_OUTPUT or TEMPORARY_VARIABLE must be
+ * written before it is read.
+ */
+ vec<Operation> operations;
+
+ /**
+ * Input indexes of the model. There must be at least one.
+ *
+ * Each value corresponds to the index of the operand in "operands".
+ */
+ vec<uint32_t> inputIndexes;
+
+ /**
+ * Output indexes of the model. There must be at least one.
+ *
+ * Each value corresponds to the index of the operand in "operands".
+ */
+ vec<uint32_t> outputIndexes;
+
+ /**
+ * A byte buffer containing operand data that were copied into the model.
+ *
+ * An operand's value must be located here if and only if Operand::lifetime
+ * equals OperandLifeTime::CONSTANT_COPY.
+ */
+ vec<uint8_t> operandValues;
+
+ /**
+ * A collection of shared memory pools containing operand values.
+ *
+ * An operand's value must be located here if and only if Operand::lifetime
+ * equals OperandLifeTime::CONSTANT_REFERENCE.
+ */
+ vec<memory> pools;
+
+ /**
+ * 'true' indicates TENSOR_FLOAT32 may be calculated with range and/or
+ * precision as low as that of the IEEE 754 16-bit floating-point format.
+ * 'false' indicates TENSOR_FLOAT32 must be calculated using at least the
+ * range and precision of the IEEE 754 32-bit floating-point format.
+ */
+ bool relaxComputationFloat32toFloat16;
+
+ /**
+ * The mapping between extension names and prefixes of operand and
+ * operation type values.
+ *
+ * An operand or operation whose numeric type value is above
+ * {@link OperandTypeRange::BASE_MAX} or
+ * {@link OperationTypeRange::BASE_MAX} respectively should be interpreted
+ * as an extension operand. The low
+ * {@link Model::ExtensionTypeEncoding::LOW_BITS_TYPE} bits of the value
+ * correspond to the type ID within the extension and the high
+ * {@link Model::ExtensionTypeEncoding::HIGH_BITS_PREFIX} bits encode
+ * the "prefix", which maps uniquely to the extension name.
+ *
+ * For example, if a model contains an operation whose value is
+ * 0xAAAABBBB and extensionNameToPrefix contains an entry with
+ * prefix=0xAAAA and name="vendor.test.test_extension", then
+ * the operation should be interpreted as the operation 0xBBBB
+ * of the extension named vendor.test.test_extension.
+ *
+ * This is a one-to-one correspondence. That is, there must be at most one
+ * prefix corresponding to each extension name and at most one extension
+ * name corresponding to each prefix.
+ */
+ vec<ExtensionNameAndPrefix> extensionNameToPrefix;
+
+ /**
+ * A correspondence between an extension name and a prefix of operand and
+ * operation type values.
+ */
+ struct ExtensionNameAndPrefix {
+ /**
+ * The extension name.
+ *
+ * See {@link Extension::name} for the format specification.
+ */
+ string name;
+
+ /**
+ * The unique extension identifier within the model.
+ *
+ * See {@link Model::extensionNameToPrefix}.
+ */
+ uint16_t prefix;
+ };
+
+ /**
+ * Numeric values of extension operand and operation types have the
+ * following structure:
+ * - 16 high bits represent the "prefix", which corresponds uniquely to the
+ * extension name.
+ * - 16 low bits represent the type ID within the extension.
+ */
+ enum ExtensionTypeEncoding : uint8_t {
+ HIGH_BITS_PREFIX = 16,
+ LOW_BITS_TYPE = 16,
+ };
+};
+
+/**
+ * Describes the shape information of an output operand after execution.
+ */
+struct OutputShape {
+ /**
+ * Dimensions of the operand.
+ */
+ vec<uint32_t> dimensions;
+
+ /**
+ * Whether the provided buffer size is sufficient for the output.
+ */
+ bool isSufficient;
+};
+
+/**
+ * Specifies whether or not to measure timing information during execution.
+ */
+enum MeasureTiming : int32_t {
+ NO = 0,
+ YES = 1,
+};
+
+/**
+
+ * Timing information measured during execution. Each time is a duration from
+ * the beginning of some task to the end of that task, including time when that
+ * task is not active (for example, preempted by some other task, or
+ * waiting for some resource to become available).
+ *
+ * Times are measured in microseconds.
+ * When a time is not available, it must be reported as UINT64_MAX.
+ */
+struct Timing {
+ /** Execution time on device (not driver, which runs on host processor). */
+ uint64_t timeOnDevice;
+ /** Execution time in driver (including time on device). */
+ uint64_t timeInDriver;
+};
+
+/**
+ * FmqRequestDatum is a single element of a serialized representation of an
+ * execution request (a {@link @1.0::Request} object and a {@link MeasureTiming}
+ * value) which is sent across FastMessageQueue.
+ *
+ * The serialized representation for a particular execution is referred to later
+ * in these descriptions as a 'packet'.
+ *
+ * FastMessageQueue can only pass HIDL-defined types that do not involve nested
+ * buffers, handles, or interfaces.
+ *
+ * The request is serialized as follows:
+ * 1) 'packetInformation'
+ * 2) For each input operand:
+ * 2.1) 'inputOperandInformation'
+ * 2.2) For each dimension element of the operand:
+ * 2.2.1) 'inputOperandDimensionValue'
+ * 3) For each output operand:
+ * 3.1) 'outputOperandInformation'
+ * 3.2) For each dimension element of the operand:
+ * 3.2.1) 'outputOperandDimensionValue'
+ * 4) For each pool:
+ * 4.1) 'poolIdentifier'
+ * 5) 'measureTiming'
+ */
+safe_union FmqRequestDatum {
+ /**
+ * Type to describe the high-level layout of the packet.
+ */
+ struct PacketInformation {
+ /**
+ * How many elements the packet contains, including the
+ * "packetInformation" datum.
+ */
+ uint32_t packetSize;
+
+ /**
+ * Number of input operands.
+ */
+ uint32_t numberOfInputOperands;
+
+ /**
+ * Number of output operands.
+ */
+ uint32_t numberOfOutputOperands;
+
+ /**
+ * Number of pool identifiers.
+ */
+ uint32_t numberOfPools;
+ };
+
+ /**
+ * Type representing the information for each operand.
+ */
+ struct OperandInformation {
+ /**
+ * If true, the argument does not have a value. This can be used for
+ * operations that take optional arguments. If true, the fields of
+ * 'location' are set to 0, 'numberOfDimensions' is set to 0, and the
+ * dimensions information is omitted from the serialization.
+ */
+ bool hasNoValue;
+
+ /**
+ * The location within one of the memory pools passed in the Request.
+ */
+ DataLocation location;
+
+ /**
+ * Number of subsequent elements that belong to the dimensions vector.
+ */
+ uint32_t numberOfDimensions;
+ };
+
+ /**
+ * packetInformation is the first element of the packet and describes the
+ * remainder of the packet.
+ */
+ PacketInformation packetInformation;
+
+ /**
+ * Information for each input operand.
+ */
+ OperandInformation inputOperandInformation;
+
+ /**
+ * Element of the dimensions vector.
+ */
+ uint32_t inputOperandDimensionValue;
+
+ /**
+ * Information for each output operand.
+ */
+ OperandInformation outputOperandInformation;
+
+ /**
+ * Element of the dimensions vector.
+ */
+ uint32_t outputOperandDimensionValue;
+
+ /**
+ * Unique identifier for a pool.
+ *
+ * A {@link @1.0::Request} passes across one or more pools of shared memory
+ * for the inputs and outputs of an execution. However, these memory pools
+ * are not able to be sent across FastMessageQueue directly. Instead, the
+ * producing side of the FMQ represents each different pool with a unique
+ * identifier, and sends this identifier across the FMQ. Whenever the
+ * consuming side of the FMQ needs the memory corresponding to this unique
+ * identifier, it can pass the identifier to
+ * {@link IBurstCallback::getMemories} to retreive the memory. Although this
+ * HIDL Binder call is expensive compared to communication across FMQ, it is
+ * only needed in the cases when the consumer does not recognize the unique
+ * identifier.
+ */
+ int32_t poolIdentifier;
+
+ /**
+ * Specifies whether or not to measure duration of the execution. The
+ * duration runs from the time the driver dequeues the request from a
+ * FastMessageQueue to the time the driver enqueues results to a
+ * FastMessageQueue.
+ */
+ MeasureTiming measureTiming;
+};
+
+/**
+ * FmqResultDatum is a single element of a serialized representation of the
+ * values returned from an execution ({@link @1.0::ErrorStatus},
+ * vec<{@link OutputShape}>, and {@link Timing}) which is returned via
+ * FastMessageQueue.
+ *
+ * The serialized representation for a particular execution is referred to later
+ * in these descriptions as a 'packet'.
+ *
+ * FastMessageQueue can only pass HIDL-defined types that do not involve nested
+ * buffers, handles, or interfaces.
+ *
+ * The execution return values ({@link @1.0::ErrorStatus} and
+ * vec<{@link OutputShape}>) are serialized as follows:
+ * 1) 'packetInformation'
+ * 2) For each returned operand:
+ * 2.1) 'operandInformation'
+ * 2.2) For each dimension element of the operand:
+ * 2.2.1) 'operandDimensionValue'
+ * 3) 'executionTiming'
+ */
+safe_union FmqResultDatum {
+ /**
+ * Type to describe the high-level layout of the packet.
+ */
+ struct PacketInformation {
+ /**
+ * How many elements the packet contains, including the
+ * "packetInformation" datum.
+ */
+ uint32_t packetSize;
+
+ /**
+ * Status of the execution.
+ */
+ ErrorStatus errorStatus;
+
+ /**
+ * Number of returned operands.
+ */
+ uint32_t numberOfOperands;
+ };
+
+ /**
+ * Type representing the information for each operand.
+ */
+ struct OperandInformation {
+ /**
+ * Indicates whether the operand's output buffer is large enough to
+ * store the operand's result data.
+ */
+ bool isSufficient;
+
+ /**
+ * Number of subsequent elements that belong to the dimensions vector.
+ */
+ uint32_t numberOfDimensions;
+ };
+
+ /**
+ * packetInformation is the first element of the packet and describes the
+ * remainder of the packet. It additionally includes the status of the
+ * execution.
+ */
+ PacketInformation packetInformation;
+
+ /**
+ * Information for each returned operand.
+ */
+ OperandInformation operandInformation;
+
+ /**
+ * Element of the dimensions vector.
+ */
+ uint32_t operandDimensionValue;
+
+ /**
+ * Duration of execution. Unless measurement was requested and execution
+ * succeeds, all times must be reported as UINT64_MAX. A driver may choose
+ * to report any time as UINT64_MAX, indicating that measurement is not
+ * available.
+ */
+ Timing executionTiming;
+};
+
+/**
+ * Information about an extension.
+ */
+struct Extension {
+ /**
+ * The extension name.
+ *
+ * The name must consist of lowercase latin letters, numbers, periods, and
+ * underscore signs. The name must contain at least one period.
+ *
+ * The name must start with the reverse domain name of the vendor.
+ *
+ * Example: com.google.test_extension
+ */
+ string name;
+
+ /**
+ * Information about an extension operand type.
+ */
+ struct OperandTypeInformation {
+ /**
+ * The extension operand type.
+ */
+ uint16_t type;
+
+ /**
+ * Indicates whether the extension operand type represents a tensor or
+ * a scalar.
+ */
+ bool isTensor;
+
+ /**
+ * The byte size of the operand (if scalar) or of a single element (if
+ * tensor).
+ */
+ uint32_t byteSize;
+ };
+
+ /**
+ * Information about operand types defined by the extension.
+ */
+ vec<OperandTypeInformation> operandTypes;
+};
diff --git a/neuralnetworks/1.2/vts/functional/Android.bp b/neuralnetworks/1.2/vts/functional/Android.bp
index 3ba8879..fc727b7 100644
--- a/neuralnetworks/1.2/vts/functional/Android.bp
+++ b/neuralnetworks/1.2/vts/functional/Android.bp
@@ -14,12 +14,28 @@
// limitations under the License.
//
+cc_library_static {
+ name: "VtsHalNeuralNetworksV1_2Callbacks",
+ defaults: ["VtsHalTargetTestDefaults"],
+ export_include_dirs: ["include"],
+ srcs: [
+ "Callbacks.cpp",
+ ],
+ static_libs: [
+ "android.hardware.neuralnetworks@1.0",
+ "android.hardware.neuralnetworks@1.1",
+ "android.hardware.neuralnetworks@1.2",
+ ],
+ header_libs: [
+ "libbase_headers",
+ ]
+}
+
cc_test {
name: "VtsHalNeuralnetworksV1_2TargetTest",
defaults: ["VtsHalTargetTestDefaults"],
srcs: [
"BasicTests.cpp",
- "Callbacks.cpp",
"CompilationCachingTests.cpp",
"GeneratedTestHarness.cpp",
"TestAssertions.cpp",
@@ -37,6 +53,7 @@
"android.hardware.neuralnetworks@1.0",
"android.hardware.neuralnetworks@1.1",
"android.hardware.neuralnetworks@1.2",
+ "android.hardware.neuralnetworks@1.3",
"android.hidl.allocator@1.0",
"android.hidl.memory@1.0",
"libgmock",
@@ -44,6 +61,7 @@
"libneuralnetworks_generated_test_harness",
"libneuralnetworks_utils",
"VtsHalNeuralNetworksV1_0_utils",
+ "VtsHalNeuralNetworksV1_2Callbacks",
],
whole_static_libs: [
"neuralnetworks_generated_V1_0_example",
diff --git a/neuralnetworks/1.3/Android.bp b/neuralnetworks/1.3/Android.bp
new file mode 100644
index 0000000..0615ec6
--- /dev/null
+++ b/neuralnetworks/1.3/Android.bp
@@ -0,0 +1,21 @@
+// This file is autogenerated by hidl-gen -Landroidbp.
+
+hidl_interface {
+ name: "android.hardware.neuralnetworks@1.3",
+ root: "android.hardware",
+ vndk: {
+ enabled: true,
+ },
+ srcs: [
+ "types.hal",
+ "IDevice.hal",
+ ],
+ interfaces: [
+ "android.hardware.neuralnetworks@1.0",
+ "android.hardware.neuralnetworks@1.1",
+ "android.hardware.neuralnetworks@1.2",
+ "android.hidl.base@1.0",
+ "android.hidl.safe_union@1.0",
+ ],
+ gen_java: false,
+}
diff --git a/neuralnetworks/1.3/IDevice.hal b/neuralnetworks/1.3/IDevice.hal
new file mode 100644
index 0000000..ee36fb4
--- /dev/null
+++ b/neuralnetworks/1.3/IDevice.hal
@@ -0,0 +1,171 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.neuralnetworks@1.3;
+
+import @1.0::ErrorStatus;
+import @1.1::ExecutionPreference;
+import @1.2::Constant;
+import @1.2::DeviceType;
+import @1.2::Extension;
+import @1.2::IDevice;
+import @1.2::IPreparedModelCallback;
+
+/**
+ * This interface represents a device driver.
+ */
+interface IDevice extends @1.2::IDevice {
+ /**
+ * Gets the capabilities of a driver.
+ *
+ * @return status Error status of the call, must be:
+ * - NONE if successful
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ * @return capabilities Capabilities of the driver.
+ */
+ getCapabilities_1_3() generates (ErrorStatus status, Capabilities capabilities);
+
+ /**
+ * Gets the supported operations in a model.
+ *
+ * getSupportedOperations indicates which operations of a model are fully
+ * supported by the vendor driver. If an operation may not be supported for
+ * any reason, getSupportedOperations must return false for that operation.
+ *
+ * @param model A model whose operations--and their corresponding operands--
+ * are to be verified by the driver.
+ * @return status Error status of the call, must be:
+ * - NONE if successful
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ * - INVALID_ARGUMENT if provided model is invalid
+ * @return supportedOperations A list of supported operations, where true
+ * indicates the operation is supported and false indicates the
+ * operation is not supported. The index of "supported" corresponds with
+ * the index of the operation it is describing.
+ */
+ getSupportedOperations_1_3(Model model)
+ generates (ErrorStatus status, vec<bool> supportedOperations);
+
+ /**
+ * Asynchronously creates a prepared model for execution and optionally
+ * saves it into cache files.
+ *
+ * prepareModel is used to make any necessary transformations to or
+ * alternative representations to a model for execution, possibly including
+ * transformations on the constant data, optimization on the model's graph,
+ * or compilation into the device's native binary format. The model itself
+ * is not changed.
+ *
+ * Optionally, caching information may be provided for the driver to save
+ * the prepared model to cache files for faster model compilation time when
+ * the same model preparation is requested in the future. There are two
+ * types of cache file handles provided to the driver: model cache and data
+ * cache. For more information on the two types of cache handles, refer to
+ * getNumberOfCacheFilesNeeded.
+ *
+ * The file descriptors must be opened with read and write permission. A
+ * file may have any size, and the corresponding file descriptor may have
+ * any offset. The driver must truncate a file to zero size before writing
+ * to that file. The file descriptors may be closed by the client once the
+ * asynchronous preparation has finished. The driver must dup a file
+ * descriptor if it wants to get access to the cache file later.
+ *
+ * The model is prepared asynchronously with respect to the caller. The
+ * prepareModel function must verify the inputs to the preparedModel
+ * function related to preparing the model (as opposed to saving the
+ * prepared model to cache) are correct. If there is an error, prepareModel
+ * must immediately invoke the callback with the appropriate ErrorStatus
+ * value and nullptr for the IPreparedModel, then return with the same
+ * ErrorStatus. If the inputs to the prepareModel function that are related
+ * to preparing the model are valid and there is no error, prepareModel must
+ * launch an asynchronous task to prepare the model in the background, and
+ * immediately return from prepareModel with ErrorStatus::NONE. If the
+ * asynchronous task fails to launch, prepareModel must immediately invoke
+ * the callback with ErrorStatus::GENERAL_FAILURE and nullptr for the
+ * IPreparedModel, then return with ErrorStatus::GENERAL_FAILURE.
+ *
+ * When the asynchronous task has finished preparing the model, it must
+ * immediately invoke the callback function provided as an input to
+ * prepareModel. If the model was prepared successfully, the callback object
+ * must be invoked with an error status of ErrorStatus::NONE and the
+ * produced IPreparedModel object. If an error occurred preparing the model,
+ * the callback object must be invoked with the appropriate ErrorStatus
+ * value and nullptr for the IPreparedModel.
+ *
+ * Optionally, the driver may save the prepared model to cache during the
+ * asynchronous preparation. Any error that occurs when saving to cache must
+ * not affect the status of preparing the model. Even if the input arguments
+ * related to the cache may be invalid, or the driver may fail to save to
+ * cache, the prepareModel function must finish preparing the model. The
+ * driver may choose not to save to cache even if the caching information is
+ * provided and valid.
+ *
+ * The only information that may be unknown to the model at this stage is
+ * the shape of the tensors, which may only be known at execution time. As
+ * such, some driver services may return partially prepared models, where
+ * the prepared model may only be finished when it is paired with a set of
+ * inputs to the model. Note that the same prepared model object may be used
+ * with different shapes of inputs on different (possibly concurrent)
+ * executions.
+ *
+ * Multiple threads may call prepareModel on the same model concurrently.
+ *
+ * @param model The model to be prepared for execution.
+ * @param preference Indicates the intended execution behavior of a prepared
+ * model.
+ * @param modelCache A vector of handles with each entry holding exactly one
+ * cache file descriptor for the security-sensitive cache. The length of
+ * the vector must either be 0 indicating that caching information is
+ * not provided, or match the numModelCache returned from
+ * getNumberOfCacheFilesNeeded. The cache handles will be provided in
+ * the same order when retrieving the preparedModel from cache files
+ * with prepareModelFromCache.
+ * @param dataCache A vector of handles with each entry holding exactly one
+ * cache file descriptor for the constants' cache. The length of the
+ * vector must either be 0 indicating that caching information is not
+ * provided, or match the numDataCache returned from
+ * getNumberOfCacheFilesNeeded. The cache handles will be provided in
+ * the same order when retrieving the preparedModel from cache files
+ * with prepareModelFromCache.
+ * @param token A caching token of length Constant::BYTE_SIZE_OF_CACHE_TOKEN
+ * identifying the prepared model. The same token will be provided when
+ * retrieving the prepared model from the cache files with
+ * prepareModelFromCache. Tokens should be chosen to have a low rate of
+ * collision for a particular application. The driver cannot detect a
+ * collision; a collision will result in a failed execution or in a
+ * successful execution that produces incorrect output values. If both
+ * modelCache and dataCache are empty indicating that caching
+ * information is not provided, this token must be ignored.
+ * @param callback A callback object used to return the error status of
+ * preparing the model for execution and the prepared model if
+ * successful, nullptr otherwise. The callback object's notify function
+ * must be called exactly once, even if the model could not be prepared.
+ * @return status Error status of launching a task which prepares the model
+ * in the background; must be:
+ * - NONE if preparation task is successfully launched
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ * - INVALID_ARGUMENT if one of the input arguments related to preparing
+ * the model is invalid
+ */
+ prepareModel_1_3(Model model, ExecutionPreference preference,
+ vec<handle> modelCache, vec<handle> dataCache,
+ uint8_t[Constant:BYTE_SIZE_OF_CACHE_TOKEN] token,
+ IPreparedModelCallback callback)
+ generates (ErrorStatus status);
+};
diff --git a/neuralnetworks/1.3/types.hal b/neuralnetworks/1.3/types.hal
new file mode 100644
index 0000000..86ab287
--- /dev/null
+++ b/neuralnetworks/1.3/types.hal
@@ -0,0 +1,373 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.neuralnetworks@1.3;
+
+import @1.0::DataLocation;
+import @1.0::OperandLifeTime;
+import @1.0::PerformanceInfo;
+import @1.2::OperandType;
+import @1.2::OperationType;
+import @1.2::SymmPerChannelQuantParams;
+
+import android.hidl.safe_union@1.0::Monostate;
+
+enum OperandType : @1.2::OperandType {
+ /**
+ * A tensor of 8 bit signed integers that represent real numbers.
+ *
+ * Attached to this tensor are two numbers that can be used to convert the
+ * 8 bit integer to the real value and vice versa. These two numbers are:
+ * - scale: a 32 bit floating point value greater than zero.
+ * - zeroPoint: a 32 bit integer, in range [-128, 127].
+ *
+ * The formula is:
+ * real_value = (integer_value - zeroPoint) * scale.
+ */
+ TENSOR_QUANT8_ASYMM_SIGNED = 14,
+
+ /*
+ * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+ * alternative to OEM operation and data types.
+ *
+ * OEM specific scalar value.
+ * OEM = 10000,
+ */
+ /*
+ * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+ * alternative to OEM operation and data types.
+ *
+ * A tensor of OEM specific values.
+ * TENSOR_OEM_BYTE = 10001,
+ */
+ /* ADDING A NEW FUNDAMENTAL TYPE REQUIRES UPDATING THE VALUE OF
+ * OperandTypeRange::FUNDAMENTAL_MAX.
+ */
+ /* ADDING A NEW OEM TYPE REQUIRES UPDATING THE VALUE OF
+ * OperandTypeRange::OEM_MAX.
+ */
+};
+
+/**
+ * The range of operand values in the OperandType enum.
+ */
+enum OperandTypeRange : uint32_t {
+ BASE_MIN = 0,
+ FUNDAMENTAL_MIN = 0,
+ FUNDAMENTAL_MAX = 14,
+ OEM_MIN = 10000,
+ OEM_MAX = 10001,
+ BASE_MAX = 0xFFFF,
+};
+
+
+/**
+ * The capabilities of a driver.
+ *
+ * Performance of an operation comes from the type of its first operand.
+ * This represents performance for non extension operand types.
+ */
+struct Capabilities {
+ /**
+ * Driver performance when operating on float32 data but performing
+ * calculations with range and/or precision as low as that of the IEEE
+ * 754 16-bit floating-point format.
+ */
+ PerformanceInfo relaxedFloat32toFloat16PerformanceScalar;
+ PerformanceInfo relaxedFloat32toFloat16PerformanceTensor;
+
+ /**
+ * Driver performance when operating on a particular data type.
+ * In the case of float32 data, this is used when the calculations
+ * are not relaxed.
+ */
+ struct OperandPerformance {
+ OperandType type;
+ PerformanceInfo info;
+ };
+
+ /**
+ * Performance by operand type. Must be sorted by OperandType.
+ * If a particular OperandType is not present in operandPerformance,
+ * its performance is treated as
+ * { .execTime = FLT_MAX, .powerUsage = FLT_MAX }.
+ */
+ vec<OperandPerformance> operandPerformance;
+};
+
+/**
+ * Describes one operand of the model's graph.
+ */
+struct Operand {
+ /**
+ * The data type.
+ *
+ * Besides the values listed in {@link OperandType}, any value above
+ * {@link OperandTypeRange::BASE_MAX} is possible and should be interpreted
+ * as an extension type according to {@link Model::extensionNameToPrefix}.
+ */
+ OperandType type;
+
+ /**
+ * Dimensions of the operand.
+ *
+ * For a scalar operand, dimensions.size() must be 0.
+ *
+ * A tensor operand with all dimensions specified has "fully
+ * specified" dimensions. Whenever possible (i.e., whenever the
+ * dimensions are known at model construction time), a tensor
+ * operand should have (but is not required to have) fully
+ * specified dimensions, in order to enable the best possible
+ * performance.
+ *
+ * If a tensor operand's dimensions are not fully specified, the
+ * dimensions of the operand are deduced from the operand
+ * dimensions and values of the operation for which that operand
+ * is an output.
+ *
+ * In the following situations, a tensor operand's dimensions must
+ * be fully specified:
+ *
+ * . The operand has lifetime CONSTANT_COPY or
+ * CONSTANT_REFERENCE.
+ *
+ * . The operand has lifetime MODEL_INPUT. Fully
+ * specified dimensions must either be present in the
+ * Operand or they must be provided in the corresponding
+ * RequestArgument.
+ * EXCEPTION: If the input is optional and omitted
+ * (by setting the hasNoValue field of the corresponding
+ * RequestArgument to true) then it need not have fully
+ * specified dimensions.
+ *
+ * A tensor operand with some number of unspecified dimensions is
+ * represented by setting each unspecified dimension to 0.
+ *
+ * A tensor operand with unspecified rank is represented by providing
+ * an empty dimensions vector.
+ */
+ vec<uint32_t> dimensions;
+
+ /**
+ * The number of times this operand appears as an operation input.
+ *
+ * (For example, if this operand appears once in one operation's
+ * input list, and three times in another operation's input list,
+ * then numberOfConsumers = 4.)
+ */
+ uint32_t numberOfConsumers;
+
+ /**
+ * Quantized scale of the operand.
+ *
+ * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM or
+ * TENSOR_INT32.
+ */
+ float scale;
+
+ /**
+ * Quantized zero-point offset of the operand.
+ *
+ * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
+ */
+ int32_t zeroPoint;
+
+ /**
+ * How the operand is used.
+ */
+ OperandLifeTime lifetime;
+
+ /**
+ * Where to find the data for this operand.
+ * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or
+ * NO_VALUE:
+ * - All the fields must be 0.
+ * If the lifetime is CONSTANT_COPY:
+ * - location.poolIndex is 0.
+ * - location.offset is the offset in bytes into Model.operandValues.
+ * - location.length is set.
+ * If the lifetime is CONSTANT_REFERENCE:
+ * - location.poolIndex is set.
+ * - location.offset is the offset in bytes into the specified pool.
+ * - location.length is set.
+ */
+ DataLocation location;
+
+ /**
+ * Additional parameters specific to a particular operand type.
+ */
+ safe_union ExtraParams {
+ /**
+ * No additional parameters.
+ */
+ Monostate none;
+
+ /**
+ * Symmetric per-channel quantization parameters.
+ *
+ * Only applicable to operands of type TENSOR_QUANT8_SYMM_PER_CHANNEL.
+ */
+ SymmPerChannelQuantParams channelQuant;
+
+ /**
+ * Extension operand parameters.
+ *
+ * The framework treats this as an opaque data blob.
+ * The format is up to individual extensions.
+ */
+ vec<uint8_t> extension;
+ } extraParams;
+};
+
+/**
+ * Describes one operation of the model's graph.
+ */
+struct Operation {
+ /**
+ * The operation type.
+ */
+ OperationType type;
+
+ /**
+ * Describes the table that contains the indexes of the inputs of the
+ * operation. The offset is the index in the operandIndexes table.
+ */
+ vec<uint32_t> inputs;
+
+ /**
+ * Describes the table that contains the indexes of the outputs of the
+ * operation. The offset is the index in the operandIndexes table.
+ */
+ vec<uint32_t> outputs;
+};
+
+/**
+ * A Neural Network Model.
+ *
+ * This includes not only the execution graph, but also constant data such as
+ * weights or scalars added at construction time. The only information that
+ * may not be known is the shape of the input tensors.
+ */
+struct Model {
+ /**
+ * All operands included in the model.
+ */
+ vec<Operand> operands;
+
+ /**
+ * All operations included in the model.
+ *
+ * The operations are sorted into execution order. Every operand
+ * with lifetime MODEL_OUTPUT or TEMPORARY_VARIABLE must be
+ * written before it is read.
+ */
+ vec<Operation> operations;
+
+ /**
+ * Input indexes of the model. There must be at least one.
+ *
+ * Each value corresponds to the index of the operand in "operands".
+ */
+ vec<uint32_t> inputIndexes;
+
+ /**
+ * Output indexes of the model. There must be at least one.
+ *
+ * Each value corresponds to the index of the operand in "operands".
+ */
+ vec<uint32_t> outputIndexes;
+
+ /**
+ * A byte buffer containing operand data that were copied into the model.
+ *
+ * An operand's value must be located here if and only if Operand::lifetime
+ * equals OperandLifeTime::CONSTANT_COPY.
+ */
+ vec<uint8_t> operandValues;
+
+ /**
+ * A collection of shared memory pools containing operand values.
+ *
+ * An operand's value must be located here if and only if Operand::lifetime
+ * equals OperandLifeTime::CONSTANT_REFERENCE.
+ */
+ vec<memory> pools;
+
+ /**
+ * 'true' indicates TENSOR_FLOAT32 may be calculated with range and/or
+ * precision as low as that of the IEEE 754 16-bit floating-point format.
+ * 'false' indicates TENSOR_FLOAT32 must be calculated using at least the
+ * range and precision of the IEEE 754 32-bit floating-point format.
+ */
+ bool relaxComputationFloat32toFloat16;
+
+ /**
+ * The mapping between extension names and prefixes of operand and
+ * operation type values.
+ *
+ * An operand or operation whose numeric type value is above
+ * {@link OperandTypeRange::BASE_MAX} or
+ * {@link OperationTypeRange::BASE_MAX} respectively should be interpreted
+ * as an extension operand. The low
+ * {@link Model::ExtensionTypeEncoding::LOW_BITS_TYPE} bits of the value
+ * correspond to the type ID within the extension and the high
+ * {@link Model::ExtensionTypeEncoding::HIGH_BITS_PREFIX} bits encode
+ * the "prefix", which maps uniquely to the extension name.
+ *
+ * For example, if a model contains an operation whose value is
+ * 0xAAAABBBB and extensionNameToPrefix contains an entry with
+ * prefix=0xAAAA and name="vendor.test.test_extension", then
+ * the operation should be interpreted as the operation 0xBBBB
+ * of the extension named vendor.test.test_extension.
+ *
+ * This is a one-to-one correspondence. That is, there must be at most one
+ * prefix corresponding to each extension name and at most one extension
+ * name corresponding to each prefix.
+ */
+ vec<ExtensionNameAndPrefix> extensionNameToPrefix;
+
+ /**
+ * A correspondence between an extension name and a prefix of operand and
+ * operation type values.
+ */
+ struct ExtensionNameAndPrefix {
+ /**
+ * The extension name.
+ *
+ * See {@link Extension::name} for the format specification.
+ */
+ string name;
+
+ /**
+ * The unique extension identifier within the model.
+ *
+ * See {@link Model::extensionNameToPrefix}.
+ */
+ uint16_t prefix;
+ };
+
+ /**
+ * Numeric values of extension operand and operation types have the
+ * following structure:
+ * - 16 high bits represent the "prefix", which corresponds uniquely to the
+ * extension name.
+ * - 16 low bits represent the type ID within the extension.
+ */
+ enum ExtensionTypeEncoding : uint8_t {
+ HIGH_BITS_PREFIX = 16,
+ LOW_BITS_TYPE = 16,
+ };
+};
diff --git a/neuralnetworks/1.3/types.t b/neuralnetworks/1.3/types.t
new file mode 100644
index 0000000..d41cfd2
--- /dev/null
+++ b/neuralnetworks/1.3/types.t
@@ -0,0 +1,344 @@
+%% template file for generating types.hal.
+%% see frameworks/ml/nn/tools/api/README.md.
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.neuralnetworks@1.3;
+
+import @1.0::DataLocation;
+import @1.0::OperandLifeTime;
+import @1.0::PerformanceInfo;
+import @1.2::OperandType;
+import @1.2::OperationType;
+import @1.2::SymmPerChannelQuantParams;
+
+import android.hidl.safe_union@1.0::Monostate;
+
+enum OperandType : @1.2::OperandType {
+%insert Operand_1.3
+%insert OEMDeprecationAndOperandTypeRangeMaxComment
+};
+
+/**
+ * The range of operand values in the OperandType enum.
+ */
+enum OperandTypeRange : uint32_t {
+ BASE_MIN = 0,
+ FUNDAMENTAL_MIN = 0,
+%insert Operand_1.3_MAX
+ OEM_MIN = 10000,
+ OEM_MAX = 10001,
+ BASE_MAX = 0xFFFF,
+};
+
+
+/**
+ * The capabilities of a driver.
+ *
+ * Performance of an operation comes from the type of its first operand.
+ * This represents performance for non extension operand types.
+ */
+struct Capabilities {
+ /**
+ * Driver performance when operating on float32 data but performing
+ * calculations with range and/or precision as low as that of the IEEE
+ * 754 16-bit floating-point format.
+ */
+ PerformanceInfo relaxedFloat32toFloat16PerformanceScalar;
+ PerformanceInfo relaxedFloat32toFloat16PerformanceTensor;
+
+ /**
+ * Driver performance when operating on a particular data type.
+ * In the case of float32 data, this is used when the calculations
+ * are not relaxed.
+ */
+ struct OperandPerformance {
+ OperandType type;
+ PerformanceInfo info;
+ };
+
+ /**
+ * Performance by operand type. Must be sorted by OperandType.
+ * If a particular OperandType is not present in operandPerformance,
+ * its performance is treated as
+ * { .execTime = FLT_MAX, .powerUsage = FLT_MAX }.
+ */
+ vec<OperandPerformance> operandPerformance;
+};
+
+/**
+ * Describes one operand of the model's graph.
+ */
+struct Operand {
+ /**
+ * The data type.
+ *
+ * Besides the values listed in {@link OperandType}, any value above
+ * {@link OperandTypeRange::BASE_MAX} is possible and should be interpreted
+ * as an extension type according to {@link Model::extensionNameToPrefix}.
+ */
+ OperandType type;
+
+ /**
+ * Dimensions of the operand.
+ *
+ * For a scalar operand, dimensions.size() must be 0.
+ *
+ * A tensor operand with all dimensions specified has "fully
+ * specified" dimensions. Whenever possible (i.e., whenever the
+ * dimensions are known at model construction time), a tensor
+ * operand should have (but is not required to have) fully
+ * specified dimensions, in order to enable the best possible
+ * performance.
+ *
+ * If a tensor operand's dimensions are not fully specified, the
+ * dimensions of the operand are deduced from the operand
+ * dimensions and values of the operation for which that operand
+ * is an output.
+ *
+ * In the following situations, a tensor operand's dimensions must
+ * be fully specified:
+ *
+ * . The operand has lifetime CONSTANT_COPY or
+ * CONSTANT_REFERENCE.
+ *
+ * . The operand has lifetime MODEL_INPUT. Fully
+ * specified dimensions must either be present in the
+ * Operand or they must be provided in the corresponding
+ * RequestArgument.
+ * EXCEPTION: If the input is optional and omitted
+ * (by setting the hasNoValue field of the corresponding
+ * RequestArgument to true) then it need not have fully
+ * specified dimensions.
+ *
+ * A tensor operand with some number of unspecified dimensions is
+ * represented by setting each unspecified dimension to 0.
+ *
+ * A tensor operand with unspecified rank is represented by providing
+ * an empty dimensions vector.
+ */
+ vec<uint32_t> dimensions;
+
+ /**
+ * The number of times this operand appears as an operation input.
+ *
+ * (For example, if this operand appears once in one operation's
+ * input list, and three times in another operation's input list,
+ * then numberOfConsumers = 4.)
+ */
+ uint32_t numberOfConsumers;
+
+ /**
+ * Quantized scale of the operand.
+ *
+ * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM or
+ * TENSOR_INT32.
+ */
+ float scale;
+
+ /**
+ * Quantized zero-point offset of the operand.
+ *
+ * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
+ */
+ int32_t zeroPoint;
+
+ /**
+ * How the operand is used.
+ */
+ OperandLifeTime lifetime;
+
+ /**
+ * Where to find the data for this operand.
+ * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or
+ * NO_VALUE:
+ * - All the fields must be 0.
+ * If the lifetime is CONSTANT_COPY:
+ * - location.poolIndex is 0.
+ * - location.offset is the offset in bytes into Model.operandValues.
+ * - location.length is set.
+ * If the lifetime is CONSTANT_REFERENCE:
+ * - location.poolIndex is set.
+ * - location.offset is the offset in bytes into the specified pool.
+ * - location.length is set.
+ */
+ DataLocation location;
+
+ /**
+ * Additional parameters specific to a particular operand type.
+ */
+ safe_union ExtraParams {
+ /**
+ * No additional parameters.
+ */
+ Monostate none;
+
+ /**
+ * Symmetric per-channel quantization parameters.
+ *
+ * Only applicable to operands of type TENSOR_QUANT8_SYMM_PER_CHANNEL.
+ */
+ SymmPerChannelQuantParams channelQuant;
+
+ /**
+ * Extension operand parameters.
+ *
+ * The framework treats this as an opaque data blob.
+ * The format is up to individual extensions.
+ */
+ vec<uint8_t> extension;
+ } extraParams;
+};
+
+/**
+ * Describes one operation of the model's graph.
+ */
+struct Operation {
+ /**
+ * The operation type.
+ */
+ OperationType type;
+
+ /**
+ * Describes the table that contains the indexes of the inputs of the
+ * operation. The offset is the index in the operandIndexes table.
+ */
+ vec<uint32_t> inputs;
+
+ /**
+ * Describes the table that contains the indexes of the outputs of the
+ * operation. The offset is the index in the operandIndexes table.
+ */
+ vec<uint32_t> outputs;
+};
+
+/**
+ * A Neural Network Model.
+ *
+ * This includes not only the execution graph, but also constant data such as
+ * weights or scalars added at construction time. The only information that
+ * may not be known is the shape of the input tensors.
+ */
+struct Model {
+ /**
+ * All operands included in the model.
+ */
+ vec<Operand> operands;
+
+ /**
+ * All operations included in the model.
+ *
+ * The operations are sorted into execution order. Every operand
+ * with lifetime MODEL_OUTPUT or TEMPORARY_VARIABLE must be
+ * written before it is read.
+ */
+ vec<Operation> operations;
+
+ /**
+ * Input indexes of the model. There must be at least one.
+ *
+ * Each value corresponds to the index of the operand in "operands".
+ */
+ vec<uint32_t> inputIndexes;
+
+ /**
+ * Output indexes of the model. There must be at least one.
+ *
+ * Each value corresponds to the index of the operand in "operands".
+ */
+ vec<uint32_t> outputIndexes;
+
+ /**
+ * A byte buffer containing operand data that were copied into the model.
+ *
+ * An operand's value must be located here if and only if Operand::lifetime
+ * equals OperandLifeTime::CONSTANT_COPY.
+ */
+ vec<uint8_t> operandValues;
+
+ /**
+ * A collection of shared memory pools containing operand values.
+ *
+ * An operand's value must be located here if and only if Operand::lifetime
+ * equals OperandLifeTime::CONSTANT_REFERENCE.
+ */
+ vec<memory> pools;
+
+ /**
+ * 'true' indicates TENSOR_FLOAT32 may be calculated with range and/or
+ * precision as low as that of the IEEE 754 16-bit floating-point format.
+ * 'false' indicates TENSOR_FLOAT32 must be calculated using at least the
+ * range and precision of the IEEE 754 32-bit floating-point format.
+ */
+ bool relaxComputationFloat32toFloat16;
+
+ /**
+ * The mapping between extension names and prefixes of operand and
+ * operation type values.
+ *
+ * An operand or operation whose numeric type value is above
+ * {@link OperandTypeRange::BASE_MAX} or
+ * {@link OperationTypeRange::BASE_MAX} respectively should be interpreted
+ * as an extension operand. The low
+ * {@link Model::ExtensionTypeEncoding::LOW_BITS_TYPE} bits of the value
+ * correspond to the type ID within the extension and the high
+ * {@link Model::ExtensionTypeEncoding::HIGH_BITS_PREFIX} bits encode
+ * the "prefix", which maps uniquely to the extension name.
+ *
+ * For example, if a model contains an operation whose value is
+ * 0xAAAABBBB and extensionNameToPrefix contains an entry with
+ * prefix=0xAAAA and name="vendor.test.test_extension", then
+ * the operation should be interpreted as the operation 0xBBBB
+ * of the extension named vendor.test.test_extension.
+ *
+ * This is a one-to-one correspondence. That is, there must be at most one
+ * prefix corresponding to each extension name and at most one extension
+ * name corresponding to each prefix.
+ */
+ vec<ExtensionNameAndPrefix> extensionNameToPrefix;
+
+ /**
+ * A correspondence between an extension name and a prefix of operand and
+ * operation type values.
+ */
+ struct ExtensionNameAndPrefix {
+ /**
+ * The extension name.
+ *
+ * See {@link Extension::name} for the format specification.
+ */
+ string name;
+
+ /**
+ * The unique extension identifier within the model.
+ *
+ * See {@link Model::extensionNameToPrefix}.
+ */
+ uint16_t prefix;
+ };
+
+ /**
+ * Numeric values of extension operand and operation types have the
+ * following structure:
+ * - 16 high bits represent the "prefix", which corresponds uniquely to the
+ * extension name.
+ * - 16 low bits represent the type ID within the extension.
+ */
+ enum ExtensionTypeEncoding : uint8_t {
+ HIGH_BITS_PREFIX = 16,
+ LOW_BITS_TYPE = 16,
+ };
+};
diff --git a/neuralnetworks/1.3/vts/OWNERS b/neuralnetworks/1.3/vts/OWNERS
new file mode 100644
index 0000000..b5a8e1f
--- /dev/null
+++ b/neuralnetworks/1.3/vts/OWNERS
@@ -0,0 +1,16 @@
+# Neuralnetworks team
+butlermichael@google.com
+dgross@google.com
+jeanluc@google.com
+levp@google.com
+miaowang@google.com
+mikie@google.com
+mks@google.com
+pszczepaniak@google.com
+slavash@google.com
+vddang@google.com
+xusongw@google.com
+
+# VTS team
+yim@google.com
+yuexima@google.com
diff --git a/neuralnetworks/1.3/vts/functional/Android.bp b/neuralnetworks/1.3/vts/functional/Android.bp
new file mode 100644
index 0000000..90ce852
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/Android.bp
@@ -0,0 +1,58 @@
+//
+// Copyright (C) 2019 The Android Open Source Project
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+cc_test {
+ name: "VtsHalNeuralNetworksV1_3TargetTest",
+ defaults: ["VtsHalTargetTestDefaults"],
+ srcs: [
+ "BasicTests.cpp",
+ "CompilationCachingTests.cpp",
+ "GeneratedTestHarness.cpp",
+ "TestAssertions.cpp",
+ "ValidateBurst.cpp",
+ "ValidateModel.cpp",
+ "ValidateRequest.cpp",
+ "VtsHalNeuralnetworks.cpp",
+ ],
+ shared_libs: [
+ "libfmq",
+ "libnativewindow",
+ ],
+ static_libs: [
+ "android.hardware.neuralnetworks@1.0",
+ "android.hardware.neuralnetworks@1.1",
+ "android.hardware.neuralnetworks@1.2",
+ "android.hardware.neuralnetworks@1.3",
+ "android.hidl.allocator@1.0",
+ "android.hidl.memory@1.0",
+ "libgmock",
+ "libhidlmemory",
+ "libneuralnetworks_generated_test_harness",
+ "libneuralnetworks_utils",
+ "VtsHalNeuralNetworksV1_0_utils",
+ "VtsHalNeuralNetworksV1_2Callbacks",
+ ],
+ whole_static_libs: [
+ "neuralnetworks_generated_V1_0_example",
+ "neuralnetworks_generated_V1_1_example",
+ "neuralnetworks_generated_V1_2_example",
+ "neuralnetworks_generated_V1_3_example",
+ ],
+ header_libs: [
+ "libneuralnetworks_headers",
+ ],
+ test_suites: ["general-tests"],
+}
diff --git a/neuralnetworks/1.3/vts/functional/BasicTests.cpp b/neuralnetworks/1.3/vts/functional/BasicTests.cpp
new file mode 100644
index 0000000..b64dc2f
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/BasicTests.cpp
@@ -0,0 +1,64 @@
+/*
+ * Copyright (C) 2018 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include "VtsHalNeuralnetworks.h"
+
+namespace android::hardware::neuralnetworks::V1_3::vts::functional {
+
+using V1_0::DeviceStatus;
+using V1_0::ErrorStatus;
+using V1_0::PerformanceInfo;
+using V1_2::Constant;
+using V1_2::DeviceType;
+using V1_2::Extension;
+
+// create device test
+TEST_P(NeuralnetworksHidlTest, CreateDevice) {}
+
+// status test
+TEST_P(NeuralnetworksHidlTest, StatusTest) {
+ Return<DeviceStatus> status = kDevice->getStatus();
+ ASSERT_TRUE(status.isOk());
+ EXPECT_EQ(DeviceStatus::AVAILABLE, static_cast<DeviceStatus>(status));
+}
+
+// initialization
+TEST_P(NeuralnetworksHidlTest, GetCapabilitiesTest) {
+ using OperandPerformance = Capabilities::OperandPerformance;
+ Return<void> ret = kDevice->getCapabilities_1_3([](ErrorStatus status,
+ const Capabilities& capabilities) {
+ EXPECT_EQ(ErrorStatus::NONE, status);
+
+ auto isPositive = [](const PerformanceInfo& perf) {
+ return perf.execTime > 0.0f && perf.powerUsage > 0.0f;
+ };
+
+ EXPECT_TRUE(isPositive(capabilities.relaxedFloat32toFloat16PerformanceScalar));
+ EXPECT_TRUE(isPositive(capabilities.relaxedFloat32toFloat16PerformanceTensor));
+ const auto& opPerf = capabilities.operandPerformance;
+ EXPECT_TRUE(std::all_of(
+ opPerf.begin(), opPerf.end(),
+ [isPositive](const OperandPerformance& a) { return isPositive(a.info); }));
+ EXPECT_TRUE(std::is_sorted(opPerf.begin(), opPerf.end(),
+ [](const OperandPerformance& a, const OperandPerformance& b) {
+ return a.type < b.type;
+ }));
+ });
+ EXPECT_TRUE(ret.isOk());
+}
+} // namespace android::hardware::neuralnetworks::V1_3::vts::functional
diff --git a/neuralnetworks/1.3/vts/functional/CompilationCachingTests.cpp b/neuralnetworks/1.3/vts/functional/CompilationCachingTests.cpp
new file mode 100644
index 0000000..0ac4738
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/CompilationCachingTests.cpp
@@ -0,0 +1,1377 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include <android-base/logging.h>
+#include <fcntl.h>
+#include <ftw.h>
+#include <gtest/gtest.h>
+#include <hidlmemory/mapping.h>
+#include <unistd.h>
+
+#include <cstdio>
+#include <cstdlib>
+#include <random>
+#include <thread>
+
+#include "1.2/Callbacks.h"
+#include "GeneratedTestHarness.h"
+#include "MemoryUtils.h"
+#include "TestHarness.h"
+#include "Utils.h"
+#include "VtsHalNeuralnetworks.h"
+
+// Forward declaration of the mobilenet generated test models in
+// frameworks/ml/nn/runtime/test/generated/.
+namespace generated_tests::mobilenet_224_gender_basic_fixed {
+const test_helper::TestModel& get_test_model();
+} // namespace generated_tests::mobilenet_224_gender_basic_fixed
+
+namespace generated_tests::mobilenet_quantized {
+const test_helper::TestModel& get_test_model();
+} // namespace generated_tests::mobilenet_quantized
+
+namespace android::hardware::neuralnetworks::V1_3::vts::functional {
+
+using namespace test_helper;
+using V1_0::ErrorStatus;
+using V1_1::ExecutionPreference;
+using V1_2::Constant;
+using V1_2::IPreparedModel;
+using V1_2::OperationType;
+using V1_2::implementation::PreparedModelCallback;
+
+namespace float32_model {
+
+constexpr auto get_test_model = generated_tests::mobilenet_224_gender_basic_fixed::get_test_model;
+
+} // namespace float32_model
+
+namespace quant8_model {
+
+constexpr auto get_test_model = generated_tests::mobilenet_quantized::get_test_model;
+
+} // namespace quant8_model
+
+namespace {
+
+enum class AccessMode { READ_WRITE, READ_ONLY, WRITE_ONLY };
+
+// Creates cache handles based on provided file groups.
+// The outer vector corresponds to handles and the inner vector is for fds held by each handle.
+void createCacheHandles(const std::vector<std::vector<std::string>>& fileGroups,
+ const std::vector<AccessMode>& mode, hidl_vec<hidl_handle>* handles) {
+ handles->resize(fileGroups.size());
+ for (uint32_t i = 0; i < fileGroups.size(); i++) {
+ std::vector<int> fds;
+ for (const auto& file : fileGroups[i]) {
+ int fd;
+ if (mode[i] == AccessMode::READ_ONLY) {
+ fd = open(file.c_str(), O_RDONLY);
+ } else if (mode[i] == AccessMode::WRITE_ONLY) {
+ fd = open(file.c_str(), O_WRONLY | O_CREAT, S_IRUSR | S_IWUSR);
+ } else if (mode[i] == AccessMode::READ_WRITE) {
+ fd = open(file.c_str(), O_RDWR | O_CREAT, S_IRUSR | S_IWUSR);
+ } else {
+ FAIL();
+ }
+ ASSERT_GE(fd, 0);
+ fds.push_back(fd);
+ }
+ native_handle_t* cacheNativeHandle = native_handle_create(fds.size(), 0);
+ ASSERT_NE(cacheNativeHandle, nullptr);
+ std::copy(fds.begin(), fds.end(), &cacheNativeHandle->data[0]);
+ (*handles)[i].setTo(cacheNativeHandle, /*shouldOwn=*/true);
+ }
+}
+
+void createCacheHandles(const std::vector<std::vector<std::string>>& fileGroups, AccessMode mode,
+ hidl_vec<hidl_handle>* handles) {
+ createCacheHandles(fileGroups, std::vector<AccessMode>(fileGroups.size(), mode), handles);
+}
+
+// Create a chain of broadcast operations. The second operand is always constant tensor [1].
+// For simplicity, activation scalar is shared. The second operand is not shared
+// in the model to let driver maintain a non-trivial size of constant data and the corresponding
+// data locations in cache.
+//
+// --------- activation --------
+// ↓ ↓ ↓ ↓
+// E.g. input -> ADD -> ADD -> ADD -> ... -> ADD -> output
+// ↑ ↑ ↑ ↑
+// [1] [1] [1] [1]
+//
+// This function assumes the operation is either ADD or MUL.
+template <typename CppType, TestOperandType operandType>
+TestModel createLargeTestModelImpl(TestOperationType op, uint32_t len) {
+ EXPECT_TRUE(op == TestOperationType::ADD || op == TestOperationType::MUL);
+
+ // Model operations and operands.
+ std::vector<TestOperation> operations(len);
+ std::vector<TestOperand> operands(len * 2 + 2);
+
+ // The activation scalar, value = 0.
+ operands[0] = {
+ .type = TestOperandType::INT32,
+ .dimensions = {},
+ .numberOfConsumers = len,
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = TestOperandLifeTime::CONSTANT_COPY,
+ .data = TestBuffer::createFromVector<int32_t>({0}),
+ };
+
+ // The buffer value of the constant second operand. The logical value is always 1.0f.
+ CppType bufferValue;
+ // The scale of the first and second operand.
+ float scale1, scale2;
+ if (operandType == TestOperandType::TENSOR_FLOAT32) {
+ bufferValue = 1.0f;
+ scale1 = 0.0f;
+ scale2 = 0.0f;
+ } else if (op == TestOperationType::ADD) {
+ bufferValue = 1;
+ scale1 = 1.0f;
+ scale2 = 1.0f;
+ } else {
+ // To satisfy the constraint on quant8 MUL: input0.scale * input1.scale < output.scale,
+ // set input1 to have scale = 0.5f and bufferValue = 2, i.e. 1.0f in floating point.
+ bufferValue = 2;
+ scale1 = 1.0f;
+ scale2 = 0.5f;
+ }
+
+ for (uint32_t i = 0; i < len; i++) {
+ const uint32_t firstInputIndex = i * 2 + 1;
+ const uint32_t secondInputIndex = firstInputIndex + 1;
+ const uint32_t outputIndex = secondInputIndex + 1;
+
+ // The first operation input.
+ operands[firstInputIndex] = {
+ .type = operandType,
+ .dimensions = {1},
+ .numberOfConsumers = 1,
+ .scale = scale1,
+ .zeroPoint = 0,
+ .lifetime = (i == 0 ? TestOperandLifeTime::MODEL_INPUT
+ : TestOperandLifeTime::TEMPORARY_VARIABLE),
+ .data = (i == 0 ? TestBuffer::createFromVector<CppType>({1}) : TestBuffer()),
+ };
+
+ // The second operation input, value = 1.
+ operands[secondInputIndex] = {
+ .type = operandType,
+ .dimensions = {1},
+ .numberOfConsumers = 1,
+ .scale = scale2,
+ .zeroPoint = 0,
+ .lifetime = TestOperandLifeTime::CONSTANT_COPY,
+ .data = TestBuffer::createFromVector<CppType>({bufferValue}),
+ };
+
+ // The operation. All operations share the same activation scalar.
+ // The output operand is created as an input in the next iteration of the loop, in the case
+ // of all but the last member of the chain; and after the loop as a model output, in the
+ // case of the last member of the chain.
+ operations[i] = {
+ .type = op,
+ .inputs = {firstInputIndex, secondInputIndex, /*activation scalar*/ 0},
+ .outputs = {outputIndex},
+ };
+ }
+
+ // For TestOperationType::ADD, output = 1 + 1 * len = len + 1
+ // For TestOperationType::MUL, output = 1 * 1 ^ len = 1
+ CppType outputResult = static_cast<CppType>(op == TestOperationType::ADD ? len + 1u : 1u);
+
+ // The model output.
+ operands.back() = {
+ .type = operandType,
+ .dimensions = {1},
+ .numberOfConsumers = 0,
+ .scale = scale1,
+ .zeroPoint = 0,
+ .lifetime = TestOperandLifeTime::MODEL_OUTPUT,
+ .data = TestBuffer::createFromVector<CppType>({outputResult}),
+ };
+
+ return {
+ .operands = std::move(operands),
+ .operations = std::move(operations),
+ .inputIndexes = {1},
+ .outputIndexes = {len * 2 + 1},
+ .isRelaxed = false,
+ };
+}
+
+} // namespace
+
+// Tag for the compilation caching tests.
+class CompilationCachingTestBase : public testing::Test {
+ protected:
+ CompilationCachingTestBase(sp<IDevice> device, OperandType type)
+ : kDevice(std::move(device)), kOperandType(type) {}
+
+ void SetUp() override {
+ testing::Test::SetUp();
+ ASSERT_NE(kDevice.get(), nullptr);
+
+ // Create cache directory. The cache directory and a temporary cache file is always created
+ // to test the behavior of prepareModelFromCache, even when caching is not supported.
+ char cacheDirTemp[] = "/data/local/tmp/TestCompilationCachingXXXXXX";
+ char* cacheDir = mkdtemp(cacheDirTemp);
+ ASSERT_NE(cacheDir, nullptr);
+ mCacheDir = cacheDir;
+ mCacheDir.push_back('/');
+
+ Return<void> ret = kDevice->getNumberOfCacheFilesNeeded(
+ [this](ErrorStatus status, uint32_t numModelCache, uint32_t numDataCache) {
+ EXPECT_EQ(ErrorStatus::NONE, status);
+ mNumModelCache = numModelCache;
+ mNumDataCache = numDataCache;
+ });
+ EXPECT_TRUE(ret.isOk());
+ mIsCachingSupported = mNumModelCache > 0 || mNumDataCache > 0;
+
+ // Create empty cache files.
+ mTmpCache = mCacheDir + "tmp";
+ for (uint32_t i = 0; i < mNumModelCache; i++) {
+ mModelCache.push_back({mCacheDir + "model" + std::to_string(i)});
+ }
+ for (uint32_t i = 0; i < mNumDataCache; i++) {
+ mDataCache.push_back({mCacheDir + "data" + std::to_string(i)});
+ }
+ // Dummy handles, use AccessMode::WRITE_ONLY for createCacheHandles to create files.
+ hidl_vec<hidl_handle> modelHandle, dataHandle, tmpHandle;
+ createCacheHandles(mModelCache, AccessMode::WRITE_ONLY, &modelHandle);
+ createCacheHandles(mDataCache, AccessMode::WRITE_ONLY, &dataHandle);
+ createCacheHandles({{mTmpCache}}, AccessMode::WRITE_ONLY, &tmpHandle);
+
+ if (!mIsCachingSupported) {
+ LOG(INFO) << "NN VTS: Early termination of test because vendor service does not "
+ "support compilation caching.";
+ std::cout << "[ ] Early termination of test because vendor service does not "
+ "support compilation caching."
+ << std::endl;
+ }
+ }
+
+ void TearDown() override {
+ // If the test passes, remove the tmp directory. Otherwise, keep it for debugging purposes.
+ if (!testing::Test::HasFailure()) {
+ // Recursively remove the cache directory specified by mCacheDir.
+ auto callback = [](const char* entry, const struct stat*, int, struct FTW*) {
+ return remove(entry);
+ };
+ nftw(mCacheDir.c_str(), callback, 128, FTW_DEPTH | FTW_MOUNT | FTW_PHYS);
+ }
+ testing::Test::TearDown();
+ }
+
+ // Model and examples creators. According to kOperandType, the following methods will return
+ // either float32 model/examples or the quant8 variant.
+ TestModel createTestModel() {
+ if (kOperandType == OperandType::TENSOR_FLOAT32) {
+ return float32_model::get_test_model();
+ } else {
+ return quant8_model::get_test_model();
+ }
+ }
+
+ TestModel createLargeTestModel(OperationType op, uint32_t len) {
+ if (kOperandType == OperandType::TENSOR_FLOAT32) {
+ return createLargeTestModelImpl<float, TestOperandType::TENSOR_FLOAT32>(
+ static_cast<TestOperationType>(op), len);
+ } else {
+ return createLargeTestModelImpl<uint8_t, TestOperandType::TENSOR_QUANT8_ASYMM>(
+ static_cast<TestOperationType>(op), len);
+ }
+ }
+
+ // See if the service can handle the model.
+ bool isModelFullySupported(const Model& model) {
+ bool fullySupportsModel = false;
+ Return<void> supportedCall = kDevice->getSupportedOperations_1_3(
+ model,
+ [&fullySupportsModel, &model](ErrorStatus status, const hidl_vec<bool>& supported) {
+ ASSERT_EQ(ErrorStatus::NONE, status);
+ ASSERT_EQ(supported.size(), model.operations.size());
+ fullySupportsModel = std::all_of(supported.begin(), supported.end(),
+ [](bool valid) { return valid; });
+ });
+ EXPECT_TRUE(supportedCall.isOk());
+ return fullySupportsModel;
+ }
+
+ void saveModelToCache(const Model& model, const hidl_vec<hidl_handle>& modelCache,
+ const hidl_vec<hidl_handle>& dataCache,
+ sp<IPreparedModel>* preparedModel = nullptr) {
+ if (preparedModel != nullptr) *preparedModel = nullptr;
+
+ // Launch prepare model.
+ sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+ hidl_array<uint8_t, sizeof(mToken)> cacheToken(mToken);
+ Return<ErrorStatus> prepareLaunchStatus =
+ kDevice->prepareModel_1_3(model, ExecutionPreference::FAST_SINGLE_ANSWER,
+ modelCache, dataCache, cacheToken, preparedModelCallback);
+ ASSERT_TRUE(prepareLaunchStatus.isOk());
+ ASSERT_EQ(static_cast<ErrorStatus>(prepareLaunchStatus), ErrorStatus::NONE);
+
+ // Retrieve prepared model.
+ preparedModelCallback->wait();
+ ASSERT_EQ(preparedModelCallback->getStatus(), ErrorStatus::NONE);
+ if (preparedModel != nullptr) {
+ *preparedModel = IPreparedModel::castFrom(preparedModelCallback->getPreparedModel())
+ .withDefault(nullptr);
+ }
+ }
+
+ bool checkEarlyTermination(ErrorStatus status) {
+ if (status == ErrorStatus::GENERAL_FAILURE) {
+ LOG(INFO) << "NN VTS: Early termination of test because vendor service cannot "
+ "save the prepared model that it does not support.";
+ std::cout << "[ ] Early termination of test because vendor service cannot "
+ "save the prepared model that it does not support."
+ << std::endl;
+ return true;
+ }
+ return false;
+ }
+
+ bool checkEarlyTermination(const Model& model) {
+ if (!isModelFullySupported(model)) {
+ LOG(INFO) << "NN VTS: Early termination of test because vendor service cannot "
+ "prepare model that it does not support.";
+ std::cout << "[ ] Early termination of test because vendor service cannot "
+ "prepare model that it does not support."
+ << std::endl;
+ return true;
+ }
+ return false;
+ }
+
+ void prepareModelFromCache(const hidl_vec<hidl_handle>& modelCache,
+ const hidl_vec<hidl_handle>& dataCache,
+ sp<IPreparedModel>* preparedModel, ErrorStatus* status) {
+ // Launch prepare model from cache.
+ sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+ hidl_array<uint8_t, sizeof(mToken)> cacheToken(mToken);
+ Return<ErrorStatus> prepareLaunchStatus = kDevice->prepareModelFromCache(
+ modelCache, dataCache, cacheToken, preparedModelCallback);
+ ASSERT_TRUE(prepareLaunchStatus.isOk());
+ if (static_cast<ErrorStatus>(prepareLaunchStatus) != ErrorStatus::NONE) {
+ *preparedModel = nullptr;
+ *status = static_cast<ErrorStatus>(prepareLaunchStatus);
+ return;
+ }
+
+ // Retrieve prepared model.
+ preparedModelCallback->wait();
+ *status = preparedModelCallback->getStatus();
+ *preparedModel = IPreparedModel::castFrom(preparedModelCallback->getPreparedModel())
+ .withDefault(nullptr);
+ }
+
+ // Absolute path to the temporary cache directory.
+ std::string mCacheDir;
+
+ // Groups of file paths for model and data cache in the tmp cache directory, initialized with
+ // outer_size = mNum{Model|Data}Cache, inner_size = 1. The outer vector corresponds to handles
+ // and the inner vector is for fds held by each handle.
+ std::vector<std::vector<std::string>> mModelCache;
+ std::vector<std::vector<std::string>> mDataCache;
+
+ // A separate temporary file path in the tmp cache directory.
+ std::string mTmpCache;
+
+ uint8_t mToken[static_cast<uint32_t>(Constant::BYTE_SIZE_OF_CACHE_TOKEN)] = {};
+ uint32_t mNumModelCache;
+ uint32_t mNumDataCache;
+ uint32_t mIsCachingSupported;
+
+ const sp<IDevice> kDevice;
+ // The primary data type of the testModel.
+ const OperandType kOperandType;
+};
+
+using CompilationCachingTestParam = std::tuple<NamedDevice, OperandType>;
+
+// A parameterized fixture of CompilationCachingTestBase. Every test will run twice, with the first
+// pass running with float32 models and the second pass running with quant8 models.
+class CompilationCachingTest : public CompilationCachingTestBase,
+ public testing::WithParamInterface<CompilationCachingTestParam> {
+ protected:
+ CompilationCachingTest()
+ : CompilationCachingTestBase(getData(std::get<NamedDevice>(GetParam())),
+ std::get<OperandType>(GetParam())) {}
+};
+
+TEST_P(CompilationCachingTest, CacheSavingAndRetrieval) {
+ // Create test HIDL model and compile.
+ const TestModel& testModel = createTestModel();
+ const Model model = createModel(testModel);
+ if (checkEarlyTermination(model)) return;
+ sp<IPreparedModel> preparedModel = nullptr;
+
+ // Save the compilation to cache.
+ {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ saveModelToCache(model, modelCache, dataCache);
+ }
+
+ // Retrieve preparedModel from cache.
+ {
+ preparedModel = nullptr;
+ ErrorStatus status;
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ if (!mIsCachingSupported) {
+ ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+ ASSERT_EQ(preparedModel, nullptr);
+ return;
+ } else if (checkEarlyTermination(status)) {
+ ASSERT_EQ(preparedModel, nullptr);
+ return;
+ } else {
+ ASSERT_EQ(status, ErrorStatus::NONE);
+ ASSERT_NE(preparedModel, nullptr);
+ }
+ }
+
+ // Execute and verify results.
+ EvaluatePreparedModel(preparedModel, testModel,
+ /*testDynamicOutputShape=*/false);
+}
+
+TEST_P(CompilationCachingTest, CacheSavingAndRetrievalNonZeroOffset) {
+ // Create test HIDL model and compile.
+ const TestModel& testModel = createTestModel();
+ const Model model = createModel(testModel);
+ if (checkEarlyTermination(model)) return;
+ sp<IPreparedModel> preparedModel = nullptr;
+
+ // Save the compilation to cache.
+ {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ uint8_t dummyBytes[] = {0, 0};
+ // Write a dummy integer to the cache.
+ // The driver should be able to handle non-empty cache and non-zero fd offset.
+ for (uint32_t i = 0; i < modelCache.size(); i++) {
+ ASSERT_EQ(write(modelCache[i].getNativeHandle()->data[0], &dummyBytes,
+ sizeof(dummyBytes)),
+ sizeof(dummyBytes));
+ }
+ for (uint32_t i = 0; i < dataCache.size(); i++) {
+ ASSERT_EQ(
+ write(dataCache[i].getNativeHandle()->data[0], &dummyBytes, sizeof(dummyBytes)),
+ sizeof(dummyBytes));
+ }
+ saveModelToCache(model, modelCache, dataCache);
+ }
+
+ // Retrieve preparedModel from cache.
+ {
+ preparedModel = nullptr;
+ ErrorStatus status;
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ uint8_t dummyByte = 0;
+ // Advance the offset of each handle by one byte.
+ // The driver should be able to handle non-zero fd offset.
+ for (uint32_t i = 0; i < modelCache.size(); i++) {
+ ASSERT_GE(read(modelCache[i].getNativeHandle()->data[0], &dummyByte, 1), 0);
+ }
+ for (uint32_t i = 0; i < dataCache.size(); i++) {
+ ASSERT_GE(read(dataCache[i].getNativeHandle()->data[0], &dummyByte, 1), 0);
+ }
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ if (!mIsCachingSupported) {
+ ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+ ASSERT_EQ(preparedModel, nullptr);
+ return;
+ } else if (checkEarlyTermination(status)) {
+ ASSERT_EQ(preparedModel, nullptr);
+ return;
+ } else {
+ ASSERT_EQ(status, ErrorStatus::NONE);
+ ASSERT_NE(preparedModel, nullptr);
+ }
+ }
+
+ // Execute and verify results.
+ EvaluatePreparedModel(preparedModel, testModel,
+ /*testDynamicOutputShape=*/false);
+}
+
+TEST_P(CompilationCachingTest, SaveToCacheInvalidNumCache) {
+ // Create test HIDL model and compile.
+ const TestModel& testModel = createTestModel();
+ const Model model = createModel(testModel);
+ if (checkEarlyTermination(model)) return;
+
+ // Test with number of model cache files greater than mNumModelCache.
+ {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ // Pass an additional cache file for model cache.
+ mModelCache.push_back({mTmpCache});
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ mModelCache.pop_back();
+ sp<IPreparedModel> preparedModel = nullptr;
+ saveModelToCache(model, modelCache, dataCache, &preparedModel);
+ ASSERT_NE(preparedModel, nullptr);
+ // Execute and verify results.
+ EvaluatePreparedModel(preparedModel, testModel,
+ /*testDynamicOutputShape=*/false);
+ // Check if prepareModelFromCache fails.
+ preparedModel = nullptr;
+ ErrorStatus status;
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ if (status != ErrorStatus::INVALID_ARGUMENT) {
+ ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+ }
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+
+ // Test with number of model cache files smaller than mNumModelCache.
+ if (mModelCache.size() > 0) {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ // Pop out the last cache file.
+ auto tmp = mModelCache.back();
+ mModelCache.pop_back();
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ mModelCache.push_back(tmp);
+ sp<IPreparedModel> preparedModel = nullptr;
+ saveModelToCache(model, modelCache, dataCache, &preparedModel);
+ ASSERT_NE(preparedModel, nullptr);
+ // Execute and verify results.
+ EvaluatePreparedModel(preparedModel, testModel,
+ /*testDynamicOutputShape=*/false);
+ // Check if prepareModelFromCache fails.
+ preparedModel = nullptr;
+ ErrorStatus status;
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ if (status != ErrorStatus::INVALID_ARGUMENT) {
+ ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+ }
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+
+ // Test with number of data cache files greater than mNumDataCache.
+ {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ // Pass an additional cache file for data cache.
+ mDataCache.push_back({mTmpCache});
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ mDataCache.pop_back();
+ sp<IPreparedModel> preparedModel = nullptr;
+ saveModelToCache(model, modelCache, dataCache, &preparedModel);
+ ASSERT_NE(preparedModel, nullptr);
+ // Execute and verify results.
+ EvaluatePreparedModel(preparedModel, testModel,
+ /*testDynamicOutputShape=*/false);
+ // Check if prepareModelFromCache fails.
+ preparedModel = nullptr;
+ ErrorStatus status;
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ if (status != ErrorStatus::INVALID_ARGUMENT) {
+ ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+ }
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+
+ // Test with number of data cache files smaller than mNumDataCache.
+ if (mDataCache.size() > 0) {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ // Pop out the last cache file.
+ auto tmp = mDataCache.back();
+ mDataCache.pop_back();
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ mDataCache.push_back(tmp);
+ sp<IPreparedModel> preparedModel = nullptr;
+ saveModelToCache(model, modelCache, dataCache, &preparedModel);
+ ASSERT_NE(preparedModel, nullptr);
+ // Execute and verify results.
+ EvaluatePreparedModel(preparedModel, testModel,
+ /*testDynamicOutputShape=*/false);
+ // Check if prepareModelFromCache fails.
+ preparedModel = nullptr;
+ ErrorStatus status;
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ if (status != ErrorStatus::INVALID_ARGUMENT) {
+ ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+ }
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+}
+
+TEST_P(CompilationCachingTest, PrepareModelFromCacheInvalidNumCache) {
+ // Create test HIDL model and compile.
+ const TestModel& testModel = createTestModel();
+ const Model model = createModel(testModel);
+ if (checkEarlyTermination(model)) return;
+
+ // Save the compilation to cache.
+ {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ saveModelToCache(model, modelCache, dataCache);
+ }
+
+ // Test with number of model cache files greater than mNumModelCache.
+ {
+ sp<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ mModelCache.push_back({mTmpCache});
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ mModelCache.pop_back();
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ if (status != ErrorStatus::GENERAL_FAILURE) {
+ ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+ }
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+
+ // Test with number of model cache files smaller than mNumModelCache.
+ if (mModelCache.size() > 0) {
+ sp<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ auto tmp = mModelCache.back();
+ mModelCache.pop_back();
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ mModelCache.push_back(tmp);
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ if (status != ErrorStatus::GENERAL_FAILURE) {
+ ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+ }
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+
+ // Test with number of data cache files greater than mNumDataCache.
+ {
+ sp<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ mDataCache.push_back({mTmpCache});
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ mDataCache.pop_back();
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ if (status != ErrorStatus::GENERAL_FAILURE) {
+ ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+ }
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+
+ // Test with number of data cache files smaller than mNumDataCache.
+ if (mDataCache.size() > 0) {
+ sp<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ auto tmp = mDataCache.back();
+ mDataCache.pop_back();
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ mDataCache.push_back(tmp);
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ if (status != ErrorStatus::GENERAL_FAILURE) {
+ ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+ }
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+}
+
+TEST_P(CompilationCachingTest, SaveToCacheInvalidNumFd) {
+ // Create test HIDL model and compile.
+ const TestModel& testModel = createTestModel();
+ const Model model = createModel(testModel);
+ if (checkEarlyTermination(model)) return;
+
+ // Go through each handle in model cache, test with NumFd greater than 1.
+ for (uint32_t i = 0; i < mNumModelCache; i++) {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ // Pass an invalid number of fds for handle i.
+ mModelCache[i].push_back(mTmpCache);
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ mModelCache[i].pop_back();
+ sp<IPreparedModel> preparedModel = nullptr;
+ saveModelToCache(model, modelCache, dataCache, &preparedModel);
+ ASSERT_NE(preparedModel, nullptr);
+ // Execute and verify results.
+ EvaluatePreparedModel(preparedModel, testModel,
+ /*testDynamicOutputShape=*/false);
+ // Check if prepareModelFromCache fails.
+ preparedModel = nullptr;
+ ErrorStatus status;
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ if (status != ErrorStatus::INVALID_ARGUMENT) {
+ ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+ }
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+
+ // Go through each handle in model cache, test with NumFd equal to 0.
+ for (uint32_t i = 0; i < mNumModelCache; i++) {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ // Pass an invalid number of fds for handle i.
+ auto tmp = mModelCache[i].back();
+ mModelCache[i].pop_back();
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ mModelCache[i].push_back(tmp);
+ sp<IPreparedModel> preparedModel = nullptr;
+ saveModelToCache(model, modelCache, dataCache, &preparedModel);
+ ASSERT_NE(preparedModel, nullptr);
+ // Execute and verify results.
+ EvaluatePreparedModel(preparedModel, testModel,
+ /*testDynamicOutputShape=*/false);
+ // Check if prepareModelFromCache fails.
+ preparedModel = nullptr;
+ ErrorStatus status;
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ if (status != ErrorStatus::INVALID_ARGUMENT) {
+ ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+ }
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+
+ // Go through each handle in data cache, test with NumFd greater than 1.
+ for (uint32_t i = 0; i < mNumDataCache; i++) {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ // Pass an invalid number of fds for handle i.
+ mDataCache[i].push_back(mTmpCache);
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ mDataCache[i].pop_back();
+ sp<IPreparedModel> preparedModel = nullptr;
+ saveModelToCache(model, modelCache, dataCache, &preparedModel);
+ ASSERT_NE(preparedModel, nullptr);
+ // Execute and verify results.
+ EvaluatePreparedModel(preparedModel, testModel,
+ /*testDynamicOutputShape=*/false);
+ // Check if prepareModelFromCache fails.
+ preparedModel = nullptr;
+ ErrorStatus status;
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ if (status != ErrorStatus::INVALID_ARGUMENT) {
+ ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+ }
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+
+ // Go through each handle in data cache, test with NumFd equal to 0.
+ for (uint32_t i = 0; i < mNumDataCache; i++) {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ // Pass an invalid number of fds for handle i.
+ auto tmp = mDataCache[i].back();
+ mDataCache[i].pop_back();
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ mDataCache[i].push_back(tmp);
+ sp<IPreparedModel> preparedModel = nullptr;
+ saveModelToCache(model, modelCache, dataCache, &preparedModel);
+ ASSERT_NE(preparedModel, nullptr);
+ // Execute and verify results.
+ EvaluatePreparedModel(preparedModel, testModel,
+ /*testDynamicOutputShape=*/false);
+ // Check if prepareModelFromCache fails.
+ preparedModel = nullptr;
+ ErrorStatus status;
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ if (status != ErrorStatus::INVALID_ARGUMENT) {
+ ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+ }
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+}
+
+TEST_P(CompilationCachingTest, PrepareModelFromCacheInvalidNumFd) {
+ // Create test HIDL model and compile.
+ const TestModel& testModel = createTestModel();
+ const Model model = createModel(testModel);
+ if (checkEarlyTermination(model)) return;
+
+ // Save the compilation to cache.
+ {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ saveModelToCache(model, modelCache, dataCache);
+ }
+
+ // Go through each handle in model cache, test with NumFd greater than 1.
+ for (uint32_t i = 0; i < mNumModelCache; i++) {
+ sp<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ mModelCache[i].push_back(mTmpCache);
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ mModelCache[i].pop_back();
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ if (status != ErrorStatus::GENERAL_FAILURE) {
+ ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+ }
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+
+ // Go through each handle in model cache, test with NumFd equal to 0.
+ for (uint32_t i = 0; i < mNumModelCache; i++) {
+ sp<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ auto tmp = mModelCache[i].back();
+ mModelCache[i].pop_back();
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ mModelCache[i].push_back(tmp);
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ if (status != ErrorStatus::GENERAL_FAILURE) {
+ ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+ }
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+
+ // Go through each handle in data cache, test with NumFd greater than 1.
+ for (uint32_t i = 0; i < mNumDataCache; i++) {
+ sp<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ mDataCache[i].push_back(mTmpCache);
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ mDataCache[i].pop_back();
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ if (status != ErrorStatus::GENERAL_FAILURE) {
+ ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+ }
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+
+ // Go through each handle in data cache, test with NumFd equal to 0.
+ for (uint32_t i = 0; i < mNumDataCache; i++) {
+ sp<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ auto tmp = mDataCache[i].back();
+ mDataCache[i].pop_back();
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ mDataCache[i].push_back(tmp);
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ if (status != ErrorStatus::GENERAL_FAILURE) {
+ ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+ }
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+}
+
+TEST_P(CompilationCachingTest, SaveToCacheInvalidAccessMode) {
+ // Create test HIDL model and compile.
+ const TestModel& testModel = createTestModel();
+ const Model model = createModel(testModel);
+ if (checkEarlyTermination(model)) return;
+ std::vector<AccessMode> modelCacheMode(mNumModelCache, AccessMode::READ_WRITE);
+ std::vector<AccessMode> dataCacheMode(mNumDataCache, AccessMode::READ_WRITE);
+
+ // Go through each handle in model cache, test with invalid access mode.
+ for (uint32_t i = 0; i < mNumModelCache; i++) {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ modelCacheMode[i] = AccessMode::READ_ONLY;
+ createCacheHandles(mModelCache, modelCacheMode, &modelCache);
+ createCacheHandles(mDataCache, dataCacheMode, &dataCache);
+ modelCacheMode[i] = AccessMode::READ_WRITE;
+ sp<IPreparedModel> preparedModel = nullptr;
+ saveModelToCache(model, modelCache, dataCache, &preparedModel);
+ ASSERT_NE(preparedModel, nullptr);
+ // Execute and verify results.
+ EvaluatePreparedModel(preparedModel, testModel,
+ /*testDynamicOutputShape=*/false);
+ // Check if prepareModelFromCache fails.
+ preparedModel = nullptr;
+ ErrorStatus status;
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ if (status != ErrorStatus::INVALID_ARGUMENT) {
+ ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+ }
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+
+ // Go through each handle in data cache, test with invalid access mode.
+ for (uint32_t i = 0; i < mNumDataCache; i++) {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ dataCacheMode[i] = AccessMode::READ_ONLY;
+ createCacheHandles(mModelCache, modelCacheMode, &modelCache);
+ createCacheHandles(mDataCache, dataCacheMode, &dataCache);
+ dataCacheMode[i] = AccessMode::READ_WRITE;
+ sp<IPreparedModel> preparedModel = nullptr;
+ saveModelToCache(model, modelCache, dataCache, &preparedModel);
+ ASSERT_NE(preparedModel, nullptr);
+ // Execute and verify results.
+ EvaluatePreparedModel(preparedModel, testModel,
+ /*testDynamicOutputShape=*/false);
+ // Check if prepareModelFromCache fails.
+ preparedModel = nullptr;
+ ErrorStatus status;
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ if (status != ErrorStatus::INVALID_ARGUMENT) {
+ ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+ }
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+}
+
+TEST_P(CompilationCachingTest, PrepareModelFromCacheInvalidAccessMode) {
+ // Create test HIDL model and compile.
+ const TestModel& testModel = createTestModel();
+ const Model model = createModel(testModel);
+ if (checkEarlyTermination(model)) return;
+ std::vector<AccessMode> modelCacheMode(mNumModelCache, AccessMode::READ_WRITE);
+ std::vector<AccessMode> dataCacheMode(mNumDataCache, AccessMode::READ_WRITE);
+
+ // Save the compilation to cache.
+ {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ saveModelToCache(model, modelCache, dataCache);
+ }
+
+ // Go through each handle in model cache, test with invalid access mode.
+ for (uint32_t i = 0; i < mNumModelCache; i++) {
+ sp<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ modelCacheMode[i] = AccessMode::WRITE_ONLY;
+ createCacheHandles(mModelCache, modelCacheMode, &modelCache);
+ createCacheHandles(mDataCache, dataCacheMode, &dataCache);
+ modelCacheMode[i] = AccessMode::READ_WRITE;
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+
+ // Go through each handle in data cache, test with invalid access mode.
+ for (uint32_t i = 0; i < mNumDataCache; i++) {
+ sp<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ dataCacheMode[i] = AccessMode::WRITE_ONLY;
+ createCacheHandles(mModelCache, modelCacheMode, &modelCache);
+ createCacheHandles(mDataCache, dataCacheMode, &dataCache);
+ dataCacheMode[i] = AccessMode::READ_WRITE;
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+}
+
+// Copy file contents between file groups.
+// The outer vector corresponds to handles and the inner vector is for fds held by each handle.
+// The outer vector sizes must match and the inner vectors must have size = 1.
+static void copyCacheFiles(const std::vector<std::vector<std::string>>& from,
+ const std::vector<std::vector<std::string>>& to) {
+ constexpr size_t kBufferSize = 1000000;
+ uint8_t buffer[kBufferSize];
+
+ ASSERT_EQ(from.size(), to.size());
+ for (uint32_t i = 0; i < from.size(); i++) {
+ ASSERT_EQ(from[i].size(), 1u);
+ ASSERT_EQ(to[i].size(), 1u);
+ int fromFd = open(from[i][0].c_str(), O_RDONLY);
+ int toFd = open(to[i][0].c_str(), O_WRONLY | O_CREAT, S_IRUSR | S_IWUSR);
+ ASSERT_GE(fromFd, 0);
+ ASSERT_GE(toFd, 0);
+
+ ssize_t readBytes;
+ while ((readBytes = read(fromFd, &buffer, kBufferSize)) > 0) {
+ ASSERT_EQ(write(toFd, &buffer, readBytes), readBytes);
+ }
+ ASSERT_GE(readBytes, 0);
+
+ close(fromFd);
+ close(toFd);
+ }
+}
+
+// Number of operations in the large test model.
+constexpr uint32_t kLargeModelSize = 100;
+constexpr uint32_t kNumIterationsTOCTOU = 100;
+
+TEST_P(CompilationCachingTest, SaveToCache_TOCTOU) {
+ if (!mIsCachingSupported) return;
+
+ // Create test models and check if fully supported by the service.
+ const TestModel testModelMul = createLargeTestModel(OperationType::MUL, kLargeModelSize);
+ const Model modelMul = createModel(testModelMul);
+ if (checkEarlyTermination(modelMul)) return;
+ const TestModel testModelAdd = createLargeTestModel(OperationType::ADD, kLargeModelSize);
+ const Model modelAdd = createModel(testModelAdd);
+ if (checkEarlyTermination(modelAdd)) return;
+
+ // Save the modelMul compilation to cache.
+ auto modelCacheMul = mModelCache;
+ for (auto& cache : modelCacheMul) {
+ cache[0].append("_mul");
+ }
+ {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ createCacheHandles(modelCacheMul, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ saveModelToCache(modelMul, modelCache, dataCache);
+ }
+
+ // Use a different token for modelAdd.
+ mToken[0]++;
+
+ // This test is probabilistic, so we run it multiple times.
+ for (uint32_t i = 0; i < kNumIterationsTOCTOU; i++) {
+ // Save the modelAdd compilation to cache.
+ {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+
+ // Spawn a thread to copy the cache content concurrently while saving to cache.
+ std::thread thread(copyCacheFiles, std::cref(modelCacheMul), std::cref(mModelCache));
+ saveModelToCache(modelAdd, modelCache, dataCache);
+ thread.join();
+ }
+
+ // Retrieve preparedModel from cache.
+ {
+ sp<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+
+ // The preparation may fail or succeed, but must not crash. If the preparation succeeds,
+ // the prepared model must be executed with the correct result and not crash.
+ if (status != ErrorStatus::NONE) {
+ ASSERT_EQ(preparedModel, nullptr);
+ } else {
+ ASSERT_NE(preparedModel, nullptr);
+ EvaluatePreparedModel(preparedModel, testModelAdd,
+ /*testDynamicOutputShape=*/false);
+ }
+ }
+ }
+}
+
+TEST_P(CompilationCachingTest, PrepareFromCache_TOCTOU) {
+ if (!mIsCachingSupported) return;
+
+ // Create test models and check if fully supported by the service.
+ const TestModel testModelMul = createLargeTestModel(OperationType::MUL, kLargeModelSize);
+ const Model modelMul = createModel(testModelMul);
+ if (checkEarlyTermination(modelMul)) return;
+ const TestModel testModelAdd = createLargeTestModel(OperationType::ADD, kLargeModelSize);
+ const Model modelAdd = createModel(testModelAdd);
+ if (checkEarlyTermination(modelAdd)) return;
+
+ // Save the modelMul compilation to cache.
+ auto modelCacheMul = mModelCache;
+ for (auto& cache : modelCacheMul) {
+ cache[0].append("_mul");
+ }
+ {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ createCacheHandles(modelCacheMul, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ saveModelToCache(modelMul, modelCache, dataCache);
+ }
+
+ // Use a different token for modelAdd.
+ mToken[0]++;
+
+ // This test is probabilistic, so we run it multiple times.
+ for (uint32_t i = 0; i < kNumIterationsTOCTOU; i++) {
+ // Save the modelAdd compilation to cache.
+ {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ saveModelToCache(modelAdd, modelCache, dataCache);
+ }
+
+ // Retrieve preparedModel from cache.
+ {
+ sp<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+
+ // Spawn a thread to copy the cache content concurrently while preparing from cache.
+ std::thread thread(copyCacheFiles, std::cref(modelCacheMul), std::cref(mModelCache));
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ thread.join();
+
+ // The preparation may fail or succeed, but must not crash. If the preparation succeeds,
+ // the prepared model must be executed with the correct result and not crash.
+ if (status != ErrorStatus::NONE) {
+ ASSERT_EQ(preparedModel, nullptr);
+ } else {
+ ASSERT_NE(preparedModel, nullptr);
+ EvaluatePreparedModel(preparedModel, testModelAdd,
+ /*testDynamicOutputShape=*/false);
+ }
+ }
+ }
+}
+
+TEST_P(CompilationCachingTest, ReplaceSecuritySensitiveCache) {
+ if (!mIsCachingSupported) return;
+
+ // Create test models and check if fully supported by the service.
+ const TestModel testModelMul = createLargeTestModel(OperationType::MUL, kLargeModelSize);
+ const Model modelMul = createModel(testModelMul);
+ if (checkEarlyTermination(modelMul)) return;
+ const TestModel testModelAdd = createLargeTestModel(OperationType::ADD, kLargeModelSize);
+ const Model modelAdd = createModel(testModelAdd);
+ if (checkEarlyTermination(modelAdd)) return;
+
+ // Save the modelMul compilation to cache.
+ auto modelCacheMul = mModelCache;
+ for (auto& cache : modelCacheMul) {
+ cache[0].append("_mul");
+ }
+ {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ createCacheHandles(modelCacheMul, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ saveModelToCache(modelMul, modelCache, dataCache);
+ }
+
+ // Use a different token for modelAdd.
+ mToken[0]++;
+
+ // Save the modelAdd compilation to cache.
+ {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ saveModelToCache(modelAdd, modelCache, dataCache);
+ }
+
+ // Replace the model cache of modelAdd with modelMul.
+ copyCacheFiles(modelCacheMul, mModelCache);
+
+ // Retrieve the preparedModel from cache, expect failure.
+ {
+ sp<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+}
+
+static const auto kNamedDeviceChoices = testing::ValuesIn(getNamedDevices());
+static const auto kOperandTypeChoices =
+ testing::Values(OperandType::TENSOR_FLOAT32, OperandType::TENSOR_QUANT8_ASYMM);
+
+std::string printCompilationCachingTest(
+ const testing::TestParamInfo<CompilationCachingTestParam>& info) {
+ const auto& [namedDevice, operandType] = info.param;
+ const std::string type = (operandType == OperandType::TENSOR_FLOAT32 ? "float32" : "quant8");
+ return gtestCompliantName(getName(namedDevice) + "_" + type);
+}
+
+INSTANTIATE_TEST_CASE_P(TestCompilationCaching, CompilationCachingTest,
+ testing::Combine(kNamedDeviceChoices, kOperandTypeChoices),
+ printCompilationCachingTest);
+
+using CompilationCachingSecurityTestParam = std::tuple<NamedDevice, OperandType, uint32_t>;
+
+class CompilationCachingSecurityTest
+ : public CompilationCachingTestBase,
+ public testing::WithParamInterface<CompilationCachingSecurityTestParam> {
+ protected:
+ CompilationCachingSecurityTest()
+ : CompilationCachingTestBase(getData(std::get<NamedDevice>(GetParam())),
+ std::get<OperandType>(GetParam())) {}
+
+ void SetUp() {
+ CompilationCachingTestBase::SetUp();
+ generator.seed(kSeed);
+ }
+
+ // Get a random integer within a closed range [lower, upper].
+ template <typename T>
+ T getRandomInt(T lower, T upper) {
+ std::uniform_int_distribution<T> dis(lower, upper);
+ return dis(generator);
+ }
+
+ // Randomly flip one single bit of the cache entry.
+ void flipOneBitOfCache(const std::string& filename, bool* skip) {
+ FILE* pFile = fopen(filename.c_str(), "r+");
+ ASSERT_EQ(fseek(pFile, 0, SEEK_END), 0);
+ long int fileSize = ftell(pFile);
+ if (fileSize == 0) {
+ fclose(pFile);
+ *skip = true;
+ return;
+ }
+ ASSERT_EQ(fseek(pFile, getRandomInt(0l, fileSize - 1), SEEK_SET), 0);
+ int readByte = fgetc(pFile);
+ ASSERT_NE(readByte, EOF);
+ ASSERT_EQ(fseek(pFile, -1, SEEK_CUR), 0);
+ ASSERT_NE(fputc(static_cast<uint8_t>(readByte) ^ (1U << getRandomInt(0, 7)), pFile), EOF);
+ fclose(pFile);
+ *skip = false;
+ }
+
+ // Randomly append bytes to the cache entry.
+ void appendBytesToCache(const std::string& filename, bool* skip) {
+ FILE* pFile = fopen(filename.c_str(), "a");
+ uint32_t appendLength = getRandomInt(1, 256);
+ for (uint32_t i = 0; i < appendLength; i++) {
+ ASSERT_NE(fputc(getRandomInt<uint8_t>(0, 255), pFile), EOF);
+ }
+ fclose(pFile);
+ *skip = false;
+ }
+
+ enum class ExpectedResult { GENERAL_FAILURE, NOT_CRASH };
+
+ // Test if the driver behaves as expected when given corrupted cache or token.
+ // The modifier will be invoked after save to cache but before prepare from cache.
+ // The modifier accepts one pointer argument "skip" as the returning value, indicating
+ // whether the test should be skipped or not.
+ void testCorruptedCache(ExpectedResult expected, std::function<void(bool*)> modifier) {
+ const TestModel& testModel = createTestModel();
+ const Model model = createModel(testModel);
+ if (checkEarlyTermination(model)) return;
+
+ // Save the compilation to cache.
+ {
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ saveModelToCache(model, modelCache, dataCache);
+ }
+
+ bool skip = false;
+ modifier(&skip);
+ if (skip) return;
+
+ // Retrieve preparedModel from cache.
+ {
+ sp<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ hidl_vec<hidl_handle> modelCache, dataCache;
+ createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+
+ switch (expected) {
+ case ExpectedResult::GENERAL_FAILURE:
+ ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+ ASSERT_EQ(preparedModel, nullptr);
+ break;
+ case ExpectedResult::NOT_CRASH:
+ ASSERT_EQ(preparedModel == nullptr, status != ErrorStatus::NONE);
+ break;
+ default:
+ FAIL();
+ }
+ }
+ }
+
+ const uint32_t kSeed = std::get<uint32_t>(GetParam());
+ std::mt19937 generator;
+};
+
+TEST_P(CompilationCachingSecurityTest, CorruptedModelCache) {
+ if (!mIsCachingSupported) return;
+ for (uint32_t i = 0; i < mNumModelCache; i++) {
+ testCorruptedCache(ExpectedResult::GENERAL_FAILURE,
+ [this, i](bool* skip) { flipOneBitOfCache(mModelCache[i][0], skip); });
+ }
+}
+
+TEST_P(CompilationCachingSecurityTest, WrongLengthModelCache) {
+ if (!mIsCachingSupported) return;
+ for (uint32_t i = 0; i < mNumModelCache; i++) {
+ testCorruptedCache(ExpectedResult::GENERAL_FAILURE,
+ [this, i](bool* skip) { appendBytesToCache(mModelCache[i][0], skip); });
+ }
+}
+
+TEST_P(CompilationCachingSecurityTest, CorruptedDataCache) {
+ if (!mIsCachingSupported) return;
+ for (uint32_t i = 0; i < mNumDataCache; i++) {
+ testCorruptedCache(ExpectedResult::NOT_CRASH,
+ [this, i](bool* skip) { flipOneBitOfCache(mDataCache[i][0], skip); });
+ }
+}
+
+TEST_P(CompilationCachingSecurityTest, WrongLengthDataCache) {
+ if (!mIsCachingSupported) return;
+ for (uint32_t i = 0; i < mNumDataCache; i++) {
+ testCorruptedCache(ExpectedResult::NOT_CRASH,
+ [this, i](bool* skip) { appendBytesToCache(mDataCache[i][0], skip); });
+ }
+}
+
+TEST_P(CompilationCachingSecurityTest, WrongToken) {
+ if (!mIsCachingSupported) return;
+ testCorruptedCache(ExpectedResult::GENERAL_FAILURE, [this](bool* skip) {
+ // Randomly flip one single bit in mToken.
+ uint32_t ind =
+ getRandomInt(0u, static_cast<uint32_t>(Constant::BYTE_SIZE_OF_CACHE_TOKEN) - 1);
+ mToken[ind] ^= (1U << getRandomInt(0, 7));
+ *skip = false;
+ });
+}
+
+std::string printCompilationCachingSecurityTest(
+ const testing::TestParamInfo<CompilationCachingSecurityTestParam>& info) {
+ const auto& [namedDevice, operandType, seed] = info.param;
+ const std::string type = (operandType == OperandType::TENSOR_FLOAT32 ? "float32" : "quant8");
+ return gtestCompliantName(getName(namedDevice) + "_" + type + "_" + std::to_string(seed));
+}
+
+INSTANTIATE_TEST_CASE_P(TestCompilationCaching, CompilationCachingSecurityTest,
+ testing::Combine(kNamedDeviceChoices, kOperandTypeChoices,
+ testing::Range(0U, 10U)),
+ printCompilationCachingSecurityTest);
+
+} // namespace android::hardware::neuralnetworks::V1_3::vts::functional
diff --git a/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp b/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp
new file mode 100644
index 0000000..16a7d70
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp
@@ -0,0 +1,418 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "GeneratedTestHarness.h"
+
+#include <android-base/logging.h>
+#include <android/hardware/neuralnetworks/1.0/IDevice.h>
+#include <android/hardware/neuralnetworks/1.0/IExecutionCallback.h>
+#include <android/hardware/neuralnetworks/1.0/IPreparedModel.h>
+#include <android/hardware/neuralnetworks/1.0/IPreparedModelCallback.h>
+#include <android/hardware/neuralnetworks/1.0/types.h>
+#include <android/hardware/neuralnetworks/1.1/IDevice.h>
+#include <android/hardware/neuralnetworks/1.2/IDevice.h>
+#include <android/hardware/neuralnetworks/1.2/IExecutionCallback.h>
+#include <android/hardware/neuralnetworks/1.2/IPreparedModel.h>
+#include <android/hardware/neuralnetworks/1.2/IPreparedModelCallback.h>
+#include <android/hardware/neuralnetworks/1.2/types.h>
+#include <android/hardware/neuralnetworks/1.3/IDevice.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <android/hidl/allocator/1.0/IAllocator.h>
+#include <android/hidl/memory/1.0/IMemory.h>
+#include <hidlmemory/mapping.h>
+
+#include <gtest/gtest.h>
+#include <algorithm>
+#include <iostream>
+#include <numeric>
+
+#include "1.0/Utils.h"
+#include "1.2/Callbacks.h"
+#include "ExecutionBurstController.h"
+#include "MemoryUtils.h"
+#include "TestHarness.h"
+#include "Utils.h"
+#include "VtsHalNeuralnetworks.h"
+
+namespace android::hardware::neuralnetworks::V1_3::vts::functional {
+
+using namespace test_helper;
+using hidl::memory::V1_0::IMemory;
+using V1_0::DataLocation;
+using V1_0::ErrorStatus;
+using V1_0::OperandLifeTime;
+using V1_0::Request;
+using V1_1::ExecutionPreference;
+using V1_2::Constant;
+using V1_2::IPreparedModel;
+using V1_2::MeasureTiming;
+using V1_2::OperationType;
+using V1_2::OutputShape;
+using V1_2::SymmPerChannelQuantParams;
+using V1_2::Timing;
+using V1_2::implementation::ExecutionCallback;
+using V1_2::implementation::PreparedModelCallback;
+using HidlToken = hidl_array<uint8_t, static_cast<uint32_t>(Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;
+
+enum class OutputType { FULLY_SPECIFIED, UNSPECIFIED, INSUFFICIENT };
+
+Model createModel(const TestModel& testModel) {
+ // Model operands.
+ hidl_vec<Operand> operands(testModel.operands.size());
+ size_t constCopySize = 0, constRefSize = 0;
+ for (uint32_t i = 0; i < testModel.operands.size(); i++) {
+ const auto& op = testModel.operands[i];
+
+ DataLocation loc = {};
+ if (op.lifetime == TestOperandLifeTime::CONSTANT_COPY) {
+ loc = {.poolIndex = 0,
+ .offset = static_cast<uint32_t>(constCopySize),
+ .length = static_cast<uint32_t>(op.data.size())};
+ constCopySize += op.data.alignedSize();
+ } else if (op.lifetime == TestOperandLifeTime::CONSTANT_REFERENCE) {
+ loc = {.poolIndex = 0,
+ .offset = static_cast<uint32_t>(constRefSize),
+ .length = static_cast<uint32_t>(op.data.size())};
+ constRefSize += op.data.alignedSize();
+ }
+
+ Operand::ExtraParams extraParams;
+ if (op.type == TestOperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) {
+ extraParams.channelQuant(SymmPerChannelQuantParams{
+ .scales = op.channelQuant.scales, .channelDim = op.channelQuant.channelDim});
+ }
+
+ operands[i] = {.type = static_cast<OperandType>(op.type),
+ .dimensions = op.dimensions,
+ .numberOfConsumers = op.numberOfConsumers,
+ .scale = op.scale,
+ .zeroPoint = op.zeroPoint,
+ .lifetime = static_cast<OperandLifeTime>(op.lifetime),
+ .location = loc,
+ .extraParams = std::move(extraParams)};
+ }
+
+ // Model operations.
+ hidl_vec<Operation> operations(testModel.operations.size());
+ std::transform(testModel.operations.begin(), testModel.operations.end(), operations.begin(),
+ [](const TestOperation& op) -> Operation {
+ return {.type = static_cast<OperationType>(op.type),
+ .inputs = op.inputs,
+ .outputs = op.outputs};
+ });
+
+ // Constant copies.
+ hidl_vec<uint8_t> operandValues(constCopySize);
+ for (uint32_t i = 0; i < testModel.operands.size(); i++) {
+ const auto& op = testModel.operands[i];
+ if (op.lifetime == TestOperandLifeTime::CONSTANT_COPY) {
+ const uint8_t* begin = op.data.get<uint8_t>();
+ const uint8_t* end = begin + op.data.size();
+ std::copy(begin, end, operandValues.data() + operands[i].location.offset);
+ }
+ }
+
+ // Shared memory.
+ hidl_vec<hidl_memory> pools = {};
+ if (constRefSize > 0) {
+ hidl_vec_push_back(&pools, nn::allocateSharedMemory(constRefSize));
+ CHECK_NE(pools[0].size(), 0u);
+
+ // load data
+ sp<IMemory> mappedMemory = mapMemory(pools[0]);
+ CHECK(mappedMemory.get() != nullptr);
+ uint8_t* mappedPtr =
+ reinterpret_cast<uint8_t*>(static_cast<void*>(mappedMemory->getPointer()));
+ CHECK(mappedPtr != nullptr);
+
+ for (uint32_t i = 0; i < testModel.operands.size(); i++) {
+ const auto& op = testModel.operands[i];
+ if (op.lifetime == TestOperandLifeTime::CONSTANT_REFERENCE) {
+ const uint8_t* begin = op.data.get<uint8_t>();
+ const uint8_t* end = begin + op.data.size();
+ std::copy(begin, end, mappedPtr + operands[i].location.offset);
+ }
+ }
+ }
+
+ return {.operands = std::move(operands),
+ .operations = std::move(operations),
+ .inputIndexes = testModel.inputIndexes,
+ .outputIndexes = testModel.outputIndexes,
+ .operandValues = std::move(operandValues),
+ .pools = std::move(pools),
+ .relaxComputationFloat32toFloat16 = testModel.isRelaxed};
+}
+
+static bool isOutputSizeGreaterThanOne(const TestModel& testModel, uint32_t index) {
+ const auto byteSize = testModel.operands[testModel.outputIndexes[index]].data.size();
+ return byteSize > 1u;
+}
+
+static void makeOutputInsufficientSize(uint32_t outputIndex, Request* request) {
+ auto& length = request->outputs[outputIndex].location.length;
+ ASSERT_GT(length, 1u);
+ length -= 1u;
+}
+
+static void makeOutputDimensionsUnspecified(Model* model) {
+ for (auto i : model->outputIndexes) {
+ auto& dims = model->operands[i].dimensions;
+ std::fill(dims.begin(), dims.end(), 0);
+ }
+}
+
+static Return<ErrorStatus> ExecutePreparedModel(const sp<IPreparedModel>& preparedModel,
+ const Request& request, MeasureTiming measure,
+ sp<ExecutionCallback>& callback) {
+ return preparedModel->execute_1_2(request, measure, callback);
+}
+static Return<ErrorStatus> ExecutePreparedModel(const sp<IPreparedModel>& preparedModel,
+ const Request& request, MeasureTiming measure,
+ hidl_vec<OutputShape>* outputShapes,
+ Timing* timing) {
+ ErrorStatus result;
+ Return<void> ret = preparedModel->executeSynchronously(
+ request, measure,
+ [&result, outputShapes, timing](ErrorStatus error, const hidl_vec<OutputShape>& shapes,
+ const Timing& time) {
+ result = error;
+ *outputShapes = shapes;
+ *timing = time;
+ });
+ if (!ret.isOk()) {
+ return ErrorStatus::GENERAL_FAILURE;
+ }
+ return result;
+}
+static std::shared_ptr<::android::nn::ExecutionBurstController> CreateBurst(
+ const sp<IPreparedModel>& preparedModel) {
+ return android::nn::ExecutionBurstController::create(preparedModel, /*blocking=*/true);
+}
+enum class Executor { ASYNC, SYNC, BURST };
+
+void EvaluatePreparedModel(const sp<IPreparedModel>& preparedModel, const TestModel& testModel,
+ Executor executor, MeasureTiming measure, OutputType outputType) {
+ // If output0 does not have size larger than one byte, we can not test with insufficient buffer.
+ if (outputType == OutputType::INSUFFICIENT && !isOutputSizeGreaterThanOne(testModel, 0)) {
+ return;
+ }
+
+ Request request = createRequest(testModel);
+ if (outputType == OutputType::INSUFFICIENT) {
+ makeOutputInsufficientSize(/*outputIndex=*/0, &request);
+ }
+
+ ErrorStatus executionStatus;
+ hidl_vec<OutputShape> outputShapes;
+ Timing timing;
+ switch (executor) {
+ case Executor::ASYNC: {
+ SCOPED_TRACE("asynchronous");
+
+ // launch execution
+ sp<ExecutionCallback> executionCallback = new ExecutionCallback();
+ Return<ErrorStatus> executionLaunchStatus =
+ ExecutePreparedModel(preparedModel, request, measure, executionCallback);
+ ASSERT_TRUE(executionLaunchStatus.isOk());
+ EXPECT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(executionLaunchStatus));
+
+ // retrieve execution status
+ executionCallback->wait();
+ executionStatus = executionCallback->getStatus();
+ outputShapes = executionCallback->getOutputShapes();
+ timing = executionCallback->getTiming();
+
+ break;
+ }
+ case Executor::SYNC: {
+ SCOPED_TRACE("synchronous");
+
+ // execute
+ Return<ErrorStatus> executionReturnStatus =
+ ExecutePreparedModel(preparedModel, request, measure, &outputShapes, &timing);
+ ASSERT_TRUE(executionReturnStatus.isOk());
+ executionStatus = static_cast<ErrorStatus>(executionReturnStatus);
+
+ break;
+ }
+ case Executor::BURST: {
+ SCOPED_TRACE("burst");
+
+ // create burst
+ const std::shared_ptr<::android::nn::ExecutionBurstController> controller =
+ CreateBurst(preparedModel);
+ ASSERT_NE(nullptr, controller.get());
+
+ // create memory keys
+ std::vector<intptr_t> keys(request.pools.size());
+ for (size_t i = 0; i < keys.size(); ++i) {
+ keys[i] = reinterpret_cast<intptr_t>(&request.pools[i]);
+ }
+
+ // execute burst
+ std::tie(executionStatus, outputShapes, timing) =
+ controller->compute(request, measure, keys);
+
+ break;
+ }
+ }
+
+ if (outputType != OutputType::FULLY_SPECIFIED &&
+ executionStatus == ErrorStatus::GENERAL_FAILURE) {
+ LOG(INFO) << "NN VTS: Early termination of test because vendor service cannot "
+ "execute model that it does not support.";
+ std::cout << "[ ] Early termination of test because vendor service cannot "
+ "execute model that it does not support."
+ << std::endl;
+ GTEST_SKIP();
+ }
+ if (measure == MeasureTiming::NO) {
+ EXPECT_EQ(UINT64_MAX, timing.timeOnDevice);
+ EXPECT_EQ(UINT64_MAX, timing.timeInDriver);
+ } else {
+ if (timing.timeOnDevice != UINT64_MAX && timing.timeInDriver != UINT64_MAX) {
+ EXPECT_LE(timing.timeOnDevice, timing.timeInDriver);
+ }
+ }
+
+ switch (outputType) {
+ case OutputType::FULLY_SPECIFIED:
+ // If the model output operands are fully specified, outputShapes must be either
+ // either empty, or have the same number of elements as the number of outputs.
+ ASSERT_EQ(ErrorStatus::NONE, executionStatus);
+ ASSERT_TRUE(outputShapes.size() == 0 ||
+ outputShapes.size() == testModel.outputIndexes.size());
+ break;
+ case OutputType::UNSPECIFIED:
+ // If the model output operands are not fully specified, outputShapes must have
+ // the same number of elements as the number of outputs.
+ ASSERT_EQ(ErrorStatus::NONE, executionStatus);
+ ASSERT_EQ(outputShapes.size(), testModel.outputIndexes.size());
+ break;
+ case OutputType::INSUFFICIENT:
+ ASSERT_EQ(ErrorStatus::OUTPUT_INSUFFICIENT_SIZE, executionStatus);
+ ASSERT_EQ(outputShapes.size(), testModel.outputIndexes.size());
+ ASSERT_FALSE(outputShapes[0].isSufficient);
+ return;
+ }
+
+ // Go through all outputs, check returned output shapes.
+ for (uint32_t i = 0; i < outputShapes.size(); i++) {
+ EXPECT_TRUE(outputShapes[i].isSufficient);
+ const auto& expect = testModel.operands[testModel.outputIndexes[i]].dimensions;
+ const std::vector<uint32_t> actual = outputShapes[i].dimensions;
+ EXPECT_EQ(expect, actual);
+ }
+
+ // Retrieve execution results.
+ const std::vector<TestBuffer> outputs = getOutputBuffers(request);
+
+ // We want "close-enough" results.
+ checkResults(testModel, outputs);
+}
+
+void EvaluatePreparedModel(const sp<IPreparedModel>& preparedModel, const TestModel& testModel,
+ bool testDynamicOutputShape) {
+ if (testDynamicOutputShape) {
+ EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::NO,
+ OutputType::UNSPECIFIED);
+ EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::NO,
+ OutputType::UNSPECIFIED);
+ EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::NO,
+ OutputType::UNSPECIFIED);
+ EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::YES,
+ OutputType::UNSPECIFIED);
+ EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::YES,
+ OutputType::UNSPECIFIED);
+ EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::YES,
+ OutputType::UNSPECIFIED);
+ EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::NO,
+ OutputType::INSUFFICIENT);
+ EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::NO,
+ OutputType::INSUFFICIENT);
+ EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::NO,
+ OutputType::INSUFFICIENT);
+ EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::YES,
+ OutputType::INSUFFICIENT);
+ EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::YES,
+ OutputType::INSUFFICIENT);
+ EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::YES,
+ OutputType::INSUFFICIENT);
+ } else {
+ EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::NO,
+ OutputType::FULLY_SPECIFIED);
+ EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::NO,
+ OutputType::FULLY_SPECIFIED);
+ EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::NO,
+ OutputType::FULLY_SPECIFIED);
+ EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::YES,
+ OutputType::FULLY_SPECIFIED);
+ EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::YES,
+ OutputType::FULLY_SPECIFIED);
+ EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::YES,
+ OutputType::FULLY_SPECIFIED);
+ }
+}
+
+void Execute(const sp<IDevice>& device, const TestModel& testModel, bool testDynamicOutputShape) {
+ Model model = createModel(testModel);
+ if (testDynamicOutputShape) {
+ makeOutputDimensionsUnspecified(&model);
+ }
+
+ sp<IPreparedModel> preparedModel;
+ createPreparedModel(device, model, &preparedModel);
+ if (preparedModel == nullptr) return;
+
+ EvaluatePreparedModel(preparedModel, testModel, testDynamicOutputShape);
+}
+
+void GeneratedTestBase::SetUp() {
+ testing::TestWithParam<GeneratedTestParam>::SetUp();
+ ASSERT_NE(kDevice, nullptr);
+}
+
+std::vector<NamedModel> getNamedModels(const FilterFn& filter) {
+ return TestModelManager::get().getTestModels(filter);
+}
+
+std::string printGeneratedTest(const testing::TestParamInfo<GeneratedTestParam>& info) {
+ const auto& [namedDevice, namedModel] = info.param;
+ return gtestCompliantName(getName(namedDevice) + "_" + getName(namedModel));
+}
+
+// Tag for the generated tests
+class GeneratedTest : public GeneratedTestBase {};
+
+// Tag for the dynamic output shape tests
+class DynamicOutputShapeTest : public GeneratedTest {};
+
+TEST_P(GeneratedTest, Test) {
+ Execute(kDevice, kTestModel, /*testDynamicOutputShape=*/false);
+}
+
+TEST_P(DynamicOutputShapeTest, Test) {
+ Execute(kDevice, kTestModel, /*testDynamicOutputShape=*/true);
+}
+
+INSTANTIATE_GENERATED_TEST(GeneratedTest,
+ [](const TestModel& testModel) { return !testModel.expectFailure; });
+
+INSTANTIATE_GENERATED_TEST(DynamicOutputShapeTest,
+ [](const TestModel& testModel) { return !testModel.expectFailure; });
+
+} // namespace android::hardware::neuralnetworks::V1_3::vts::functional
diff --git a/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.h b/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.h
new file mode 100644
index 0000000..b9277cf
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.h
@@ -0,0 +1,66 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef ANDROID_HARDWARE_NEURALNETWORKS_V1_3_GENERATED_TEST_HARNESS_H
+#define ANDROID_HARDWARE_NEURALNETWORKS_V1_3_GENERATED_TEST_HARNESS_H
+
+#include <android/hardware/neuralnetworks/1.2/IPreparedModel.h>
+#include <android/hardware/neuralnetworks/1.3/IDevice.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <functional>
+#include <vector>
+#include "1.0/Utils.h"
+#include "TestHarness.h"
+#include "VtsHalNeuralnetworks.h"
+
+namespace android::hardware::neuralnetworks::V1_3::vts::functional {
+
+using NamedModel = Named<const test_helper::TestModel*>;
+using GeneratedTestParam = std::tuple<NamedDevice, NamedModel>;
+
+class GeneratedTestBase : public testing::TestWithParam<GeneratedTestParam> {
+ protected:
+ void SetUp() override;
+ const sp<IDevice> kDevice = getData(std::get<NamedDevice>(GetParam()));
+ const test_helper::TestModel& kTestModel = *getData(std::get<NamedModel>(GetParam()));
+};
+
+using FilterFn = std::function<bool(const test_helper::TestModel&)>;
+std::vector<NamedModel> getNamedModels(const FilterFn& filter);
+
+std::string printGeneratedTest(const testing::TestParamInfo<GeneratedTestParam>& info);
+
+#define INSTANTIATE_GENERATED_TEST(TestSuite, filter) \
+ INSTANTIATE_TEST_SUITE_P(TestGenerated, TestSuite, \
+ testing::Combine(testing::ValuesIn(getNamedDevices()), \
+ testing::ValuesIn(getNamedModels(filter))), \
+ printGeneratedTest)
+
+// Tag for the validation tests, instantiated in VtsHalNeuralnetworks.cpp.
+// TODO: Clean up the hierarchy for ValidationTest.
+class ValidationTest : public GeneratedTestBase {};
+
+Model createModel(const test_helper::TestModel& testModel);
+
+void PrepareModel(const sp<IDevice>& device, const Model& model,
+ sp<V1_2::IPreparedModel>* preparedModel);
+
+void EvaluatePreparedModel(const sp<V1_2::IPreparedModel>& preparedModel,
+ const test_helper::TestModel& testModel, bool testDynamicOutputShape);
+
+} // namespace android::hardware::neuralnetworks::V1_3::vts::functional
+
+#endif // ANDROID_HARDWARE_NEURALNETWORKS_V1_3_GENERATED_TEST_HARNESS_H
diff --git a/neuralnetworks/1.3/vts/functional/TestAssertions.cpp b/neuralnetworks/1.3/vts/functional/TestAssertions.cpp
new file mode 100644
index 0000000..7361078
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/TestAssertions.cpp
@@ -0,0 +1,144 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include "TestHarness.h"
+
+namespace android::hardware::neuralnetworks::V1_3 {
+
+// Make sure that the HIDL enums are compatible with the values defined in
+// frameworks/ml/nn/tools/test_generator/test_harness/include/TestHarness.h.
+using namespace test_helper;
+#define CHECK_TEST_ENUM(EnumType, enumValue) \
+ static_assert(static_cast<EnumType>(Test##EnumType::enumValue) == EnumType::enumValue)
+
+using V1_2::OperationType;
+
+CHECK_TEST_ENUM(OperandType, FLOAT32);
+CHECK_TEST_ENUM(OperandType, INT32);
+CHECK_TEST_ENUM(OperandType, UINT32);
+CHECK_TEST_ENUM(OperandType, TENSOR_FLOAT32);
+CHECK_TEST_ENUM(OperandType, TENSOR_INT32);
+CHECK_TEST_ENUM(OperandType, TENSOR_QUANT8_ASYMM);
+CHECK_TEST_ENUM(OperandType, BOOL);
+CHECK_TEST_ENUM(OperandType, TENSOR_QUANT16_SYMM);
+CHECK_TEST_ENUM(OperandType, TENSOR_FLOAT16);
+CHECK_TEST_ENUM(OperandType, TENSOR_BOOL8);
+CHECK_TEST_ENUM(OperandType, FLOAT16);
+CHECK_TEST_ENUM(OperandType, TENSOR_QUANT8_SYMM_PER_CHANNEL);
+CHECK_TEST_ENUM(OperandType, TENSOR_QUANT16_ASYMM);
+CHECK_TEST_ENUM(OperandType, TENSOR_QUANT8_SYMM);
+CHECK_TEST_ENUM(OperandType, TENSOR_QUANT8_ASYMM_SIGNED);
+
+CHECK_TEST_ENUM(OperationType, ADD);
+CHECK_TEST_ENUM(OperationType, AVERAGE_POOL_2D);
+CHECK_TEST_ENUM(OperationType, CONCATENATION);
+CHECK_TEST_ENUM(OperationType, CONV_2D);
+CHECK_TEST_ENUM(OperationType, DEPTHWISE_CONV_2D);
+CHECK_TEST_ENUM(OperationType, DEPTH_TO_SPACE);
+CHECK_TEST_ENUM(OperationType, DEQUANTIZE);
+CHECK_TEST_ENUM(OperationType, EMBEDDING_LOOKUP);
+CHECK_TEST_ENUM(OperationType, FLOOR);
+CHECK_TEST_ENUM(OperationType, FULLY_CONNECTED);
+CHECK_TEST_ENUM(OperationType, HASHTABLE_LOOKUP);
+CHECK_TEST_ENUM(OperationType, L2_NORMALIZATION);
+CHECK_TEST_ENUM(OperationType, L2_POOL_2D);
+CHECK_TEST_ENUM(OperationType, LOCAL_RESPONSE_NORMALIZATION);
+CHECK_TEST_ENUM(OperationType, LOGISTIC);
+CHECK_TEST_ENUM(OperationType, LSH_PROJECTION);
+CHECK_TEST_ENUM(OperationType, LSTM);
+CHECK_TEST_ENUM(OperationType, MAX_POOL_2D);
+CHECK_TEST_ENUM(OperationType, MUL);
+CHECK_TEST_ENUM(OperationType, RELU);
+CHECK_TEST_ENUM(OperationType, RELU1);
+CHECK_TEST_ENUM(OperationType, RELU6);
+CHECK_TEST_ENUM(OperationType, RESHAPE);
+CHECK_TEST_ENUM(OperationType, RESIZE_BILINEAR);
+CHECK_TEST_ENUM(OperationType, RNN);
+CHECK_TEST_ENUM(OperationType, SOFTMAX);
+CHECK_TEST_ENUM(OperationType, SPACE_TO_DEPTH);
+CHECK_TEST_ENUM(OperationType, SVDF);
+CHECK_TEST_ENUM(OperationType, TANH);
+CHECK_TEST_ENUM(OperationType, BATCH_TO_SPACE_ND);
+CHECK_TEST_ENUM(OperationType, DIV);
+CHECK_TEST_ENUM(OperationType, MEAN);
+CHECK_TEST_ENUM(OperationType, PAD);
+CHECK_TEST_ENUM(OperationType, SPACE_TO_BATCH_ND);
+CHECK_TEST_ENUM(OperationType, SQUEEZE);
+CHECK_TEST_ENUM(OperationType, STRIDED_SLICE);
+CHECK_TEST_ENUM(OperationType, SUB);
+CHECK_TEST_ENUM(OperationType, TRANSPOSE);
+CHECK_TEST_ENUM(OperationType, ABS);
+CHECK_TEST_ENUM(OperationType, ARGMAX);
+CHECK_TEST_ENUM(OperationType, ARGMIN);
+CHECK_TEST_ENUM(OperationType, AXIS_ALIGNED_BBOX_TRANSFORM);
+CHECK_TEST_ENUM(OperationType, BIDIRECTIONAL_SEQUENCE_LSTM);
+CHECK_TEST_ENUM(OperationType, BIDIRECTIONAL_SEQUENCE_RNN);
+CHECK_TEST_ENUM(OperationType, BOX_WITH_NMS_LIMIT);
+CHECK_TEST_ENUM(OperationType, CAST);
+CHECK_TEST_ENUM(OperationType, CHANNEL_SHUFFLE);
+CHECK_TEST_ENUM(OperationType, DETECTION_POSTPROCESSING);
+CHECK_TEST_ENUM(OperationType, EQUAL);
+CHECK_TEST_ENUM(OperationType, EXP);
+CHECK_TEST_ENUM(OperationType, EXPAND_DIMS);
+CHECK_TEST_ENUM(OperationType, GATHER);
+CHECK_TEST_ENUM(OperationType, GENERATE_PROPOSALS);
+CHECK_TEST_ENUM(OperationType, GREATER);
+CHECK_TEST_ENUM(OperationType, GREATER_EQUAL);
+CHECK_TEST_ENUM(OperationType, GROUPED_CONV_2D);
+CHECK_TEST_ENUM(OperationType, HEATMAP_MAX_KEYPOINT);
+CHECK_TEST_ENUM(OperationType, INSTANCE_NORMALIZATION);
+CHECK_TEST_ENUM(OperationType, LESS);
+CHECK_TEST_ENUM(OperationType, LESS_EQUAL);
+CHECK_TEST_ENUM(OperationType, LOG);
+CHECK_TEST_ENUM(OperationType, LOGICAL_AND);
+CHECK_TEST_ENUM(OperationType, LOGICAL_NOT);
+CHECK_TEST_ENUM(OperationType, LOGICAL_OR);
+CHECK_TEST_ENUM(OperationType, LOG_SOFTMAX);
+CHECK_TEST_ENUM(OperationType, MAXIMUM);
+CHECK_TEST_ENUM(OperationType, MINIMUM);
+CHECK_TEST_ENUM(OperationType, NEG);
+CHECK_TEST_ENUM(OperationType, NOT_EQUAL);
+CHECK_TEST_ENUM(OperationType, PAD_V2);
+CHECK_TEST_ENUM(OperationType, POW);
+CHECK_TEST_ENUM(OperationType, PRELU);
+CHECK_TEST_ENUM(OperationType, QUANTIZE);
+CHECK_TEST_ENUM(OperationType, QUANTIZED_16BIT_LSTM);
+CHECK_TEST_ENUM(OperationType, RANDOM_MULTINOMIAL);
+CHECK_TEST_ENUM(OperationType, REDUCE_ALL);
+CHECK_TEST_ENUM(OperationType, REDUCE_ANY);
+CHECK_TEST_ENUM(OperationType, REDUCE_MAX);
+CHECK_TEST_ENUM(OperationType, REDUCE_MIN);
+CHECK_TEST_ENUM(OperationType, REDUCE_PROD);
+CHECK_TEST_ENUM(OperationType, REDUCE_SUM);
+CHECK_TEST_ENUM(OperationType, ROI_ALIGN);
+CHECK_TEST_ENUM(OperationType, ROI_POOLING);
+CHECK_TEST_ENUM(OperationType, RSQRT);
+CHECK_TEST_ENUM(OperationType, SELECT);
+CHECK_TEST_ENUM(OperationType, SIN);
+CHECK_TEST_ENUM(OperationType, SLICE);
+CHECK_TEST_ENUM(OperationType, SPLIT);
+CHECK_TEST_ENUM(OperationType, SQRT);
+CHECK_TEST_ENUM(OperationType, TILE);
+CHECK_TEST_ENUM(OperationType, TOPK_V2);
+CHECK_TEST_ENUM(OperationType, TRANSPOSE_CONV_2D);
+CHECK_TEST_ENUM(OperationType, UNIDIRECTIONAL_SEQUENCE_LSTM);
+CHECK_TEST_ENUM(OperationType, UNIDIRECTIONAL_SEQUENCE_RNN);
+CHECK_TEST_ENUM(OperationType, RESIZE_NEAREST_NEIGHBOR);
+
+#undef CHECK_TEST_ENUM
+
+} // namespace android::hardware::neuralnetworks::V1_3
diff --git a/neuralnetworks/1.3/vts/functional/ValidateBurst.cpp b/neuralnetworks/1.3/vts/functional/ValidateBurst.cpp
new file mode 100644
index 0000000..95f9f42
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/ValidateBurst.cpp
@@ -0,0 +1,407 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include "VtsHalNeuralnetworks.h"
+
+#include "1.2/Callbacks.h"
+#include "ExecutionBurstController.h"
+#include "ExecutionBurstServer.h"
+#include "GeneratedTestHarness.h"
+#include "TestHarness.h"
+#include "Utils.h"
+
+#include <android-base/logging.h>
+#include <cstring>
+
+namespace android::hardware::neuralnetworks::V1_3::vts::functional {
+
+using nn::ExecutionBurstController;
+using nn::RequestChannelSender;
+using nn::ResultChannelReceiver;
+using V1_0::ErrorStatus;
+using V1_0::Request;
+using V1_2::FmqRequestDatum;
+using V1_2::FmqResultDatum;
+using V1_2::IBurstCallback;
+using V1_2::IBurstContext;
+using V1_2::IPreparedModel;
+using V1_2::MeasureTiming;
+using V1_2::Timing;
+using ExecutionBurstCallback = ExecutionBurstController::ExecutionBurstCallback;
+
+// This constant value represents the length of an FMQ that is large enough to
+// return a result from a burst execution for all of the generated test cases.
+constexpr size_t kExecutionBurstChannelLength = 1024;
+
+// This constant value represents a length of an FMQ that is not large enough
+// to return a result from a burst execution for some of the generated test
+// cases.
+constexpr size_t kExecutionBurstChannelSmallLength = 8;
+
+///////////////////////// UTILITY FUNCTIONS /////////////////////////
+
+static bool badTiming(Timing timing) {
+ return timing.timeOnDevice == UINT64_MAX && timing.timeInDriver == UINT64_MAX;
+}
+
+static void createBurst(const sp<IPreparedModel>& preparedModel, const sp<IBurstCallback>& callback,
+ std::unique_ptr<RequestChannelSender>* sender,
+ std::unique_ptr<ResultChannelReceiver>* receiver,
+ sp<IBurstContext>* context,
+ size_t resultChannelLength = kExecutionBurstChannelLength) {
+ ASSERT_NE(nullptr, preparedModel.get());
+ ASSERT_NE(nullptr, sender);
+ ASSERT_NE(nullptr, receiver);
+ ASSERT_NE(nullptr, context);
+
+ // create FMQ objects
+ auto [fmqRequestChannel, fmqRequestDescriptor] =
+ RequestChannelSender::create(kExecutionBurstChannelLength, /*blocking=*/true);
+ auto [fmqResultChannel, fmqResultDescriptor] =
+ ResultChannelReceiver::create(resultChannelLength, /*blocking=*/true);
+ ASSERT_NE(nullptr, fmqRequestChannel.get());
+ ASSERT_NE(nullptr, fmqResultChannel.get());
+ ASSERT_NE(nullptr, fmqRequestDescriptor);
+ ASSERT_NE(nullptr, fmqResultDescriptor);
+
+ // configure burst
+ ErrorStatus errorStatus;
+ sp<IBurstContext> burstContext;
+ const Return<void> ret = preparedModel->configureExecutionBurst(
+ callback, *fmqRequestDescriptor, *fmqResultDescriptor,
+ [&errorStatus, &burstContext](ErrorStatus status, const sp<IBurstContext>& context) {
+ errorStatus = status;
+ burstContext = context;
+ });
+ ASSERT_TRUE(ret.isOk());
+ ASSERT_EQ(ErrorStatus::NONE, errorStatus);
+ ASSERT_NE(nullptr, burstContext.get());
+
+ // return values
+ *sender = std::move(fmqRequestChannel);
+ *receiver = std::move(fmqResultChannel);
+ *context = burstContext;
+}
+
+static void createBurstWithResultChannelLength(
+ const sp<IPreparedModel>& preparedModel, size_t resultChannelLength,
+ std::shared_ptr<ExecutionBurstController>* controller) {
+ ASSERT_NE(nullptr, preparedModel.get());
+ ASSERT_NE(nullptr, controller);
+
+ // create FMQ objects
+ std::unique_ptr<RequestChannelSender> sender;
+ std::unique_ptr<ResultChannelReceiver> receiver;
+ sp<ExecutionBurstCallback> callback = new ExecutionBurstCallback();
+ sp<IBurstContext> context;
+ ASSERT_NO_FATAL_FAILURE(createBurst(preparedModel, callback, &sender, &receiver, &context,
+ resultChannelLength));
+ ASSERT_NE(nullptr, sender.get());
+ ASSERT_NE(nullptr, receiver.get());
+ ASSERT_NE(nullptr, context.get());
+
+ // return values
+ *controller = std::make_shared<ExecutionBurstController>(std::move(sender), std::move(receiver),
+ context, callback);
+}
+
+// Primary validation function. This function will take a valid serialized
+// request, apply a mutation to it to invalidate the serialized request, then
+// pass it to interface calls that use the serialized request. Note that the
+// serialized request here is passed by value, and any mutation to the
+// serialized request does not leave this function.
+static void validate(RequestChannelSender* sender, ResultChannelReceiver* receiver,
+ const std::string& message, std::vector<FmqRequestDatum> serialized,
+ const std::function<void(std::vector<FmqRequestDatum>*)>& mutation) {
+ mutation(&serialized);
+
+ // skip if packet is too large to send
+ if (serialized.size() > kExecutionBurstChannelLength) {
+ return;
+ }
+
+ SCOPED_TRACE(message);
+
+ // send invalid packet
+ ASSERT_TRUE(sender->sendPacket(serialized));
+
+ // receive error
+ auto results = receiver->getBlocking();
+ ASSERT_TRUE(results.has_value());
+ const auto [status, outputShapes, timing] = std::move(*results);
+ EXPECT_NE(ErrorStatus::NONE, status);
+ EXPECT_EQ(0u, outputShapes.size());
+ EXPECT_TRUE(badTiming(timing));
+}
+
+// For validation, valid packet entries are mutated to invalid packet entries,
+// or invalid packet entries are inserted into valid packets. This function
+// creates pre-set invalid packet entries for convenience.
+static std::vector<FmqRequestDatum> createBadRequestPacketEntries() {
+ const FmqRequestDatum::PacketInformation packetInformation = {
+ /*.packetSize=*/10, /*.numberOfInputOperands=*/10, /*.numberOfOutputOperands=*/10,
+ /*.numberOfPools=*/10};
+ const FmqRequestDatum::OperandInformation operandInformation = {
+ /*.hasNoValue=*/false, /*.location=*/{}, /*.numberOfDimensions=*/10};
+ const int32_t invalidPoolIdentifier = std::numeric_limits<int32_t>::max();
+ std::vector<FmqRequestDatum> bad(7);
+ bad[0].packetInformation(packetInformation);
+ bad[1].inputOperandInformation(operandInformation);
+ bad[2].inputOperandDimensionValue(0);
+ bad[3].outputOperandInformation(operandInformation);
+ bad[4].outputOperandDimensionValue(0);
+ bad[5].poolIdentifier(invalidPoolIdentifier);
+ bad[6].measureTiming(MeasureTiming::YES);
+ return bad;
+}
+
+// For validation, valid packet entries are mutated to invalid packet entries,
+// or invalid packet entries are inserted into valid packets. This function
+// retrieves pre-set invalid packet entries for convenience. This function
+// caches these data so they can be reused on subsequent validation checks.
+static const std::vector<FmqRequestDatum>& getBadRequestPacketEntries() {
+ static const std::vector<FmqRequestDatum> bad = createBadRequestPacketEntries();
+ return bad;
+}
+
+///////////////////////// REMOVE DATUM ////////////////////////////////////
+
+static void removeDatumTest(RequestChannelSender* sender, ResultChannelReceiver* receiver,
+ const std::vector<FmqRequestDatum>& serialized) {
+ for (size_t index = 0; index < serialized.size(); ++index) {
+ const std::string message = "removeDatum: removed datum at index " + std::to_string(index);
+ validate(sender, receiver, message, serialized,
+ [index](std::vector<FmqRequestDatum>* serialized) {
+ serialized->erase(serialized->begin() + index);
+ });
+ }
+}
+
+///////////////////////// ADD DATUM ////////////////////////////////////
+
+static void addDatumTest(RequestChannelSender* sender, ResultChannelReceiver* receiver,
+ const std::vector<FmqRequestDatum>& serialized) {
+ const std::vector<FmqRequestDatum>& extra = getBadRequestPacketEntries();
+ for (size_t index = 0; index <= serialized.size(); ++index) {
+ for (size_t type = 0; type < extra.size(); ++type) {
+ const std::string message = "addDatum: added datum type " + std::to_string(type) +
+ " at index " + std::to_string(index);
+ validate(sender, receiver, message, serialized,
+ [index, type, &extra](std::vector<FmqRequestDatum>* serialized) {
+ serialized->insert(serialized->begin() + index, extra[type]);
+ });
+ }
+ }
+}
+
+///////////////////////// MUTATE DATUM ////////////////////////////////////
+
+static bool interestingCase(const FmqRequestDatum& lhs, const FmqRequestDatum& rhs) {
+ using Discriminator = FmqRequestDatum::hidl_discriminator;
+
+ const bool differentValues = (lhs != rhs);
+ const bool sameDiscriminator = (lhs.getDiscriminator() == rhs.getDiscriminator());
+ const auto discriminator = rhs.getDiscriminator();
+ const bool isDimensionValue = (discriminator == Discriminator::inputOperandDimensionValue ||
+ discriminator == Discriminator::outputOperandDimensionValue);
+
+ return differentValues && !(sameDiscriminator && isDimensionValue);
+}
+
+static void mutateDatumTest(RequestChannelSender* sender, ResultChannelReceiver* receiver,
+ const std::vector<FmqRequestDatum>& serialized) {
+ const std::vector<FmqRequestDatum>& change = getBadRequestPacketEntries();
+ for (size_t index = 0; index < serialized.size(); ++index) {
+ for (size_t type = 0; type < change.size(); ++type) {
+ if (interestingCase(serialized[index], change[type])) {
+ const std::string message = "mutateDatum: changed datum at index " +
+ std::to_string(index) + " to datum type " +
+ std::to_string(type);
+ validate(sender, receiver, message, serialized,
+ [index, type, &change](std::vector<FmqRequestDatum>* serialized) {
+ (*serialized)[index] = change[type];
+ });
+ }
+ }
+ }
+}
+
+///////////////////////// BURST VALIATION TESTS ////////////////////////////////////
+
+static void validateBurstSerialization(const sp<IPreparedModel>& preparedModel,
+ const Request& request) {
+ // create burst
+ std::unique_ptr<RequestChannelSender> sender;
+ std::unique_ptr<ResultChannelReceiver> receiver;
+ sp<ExecutionBurstCallback> callback = new ExecutionBurstCallback();
+ sp<IBurstContext> context;
+ ASSERT_NO_FATAL_FAILURE(createBurst(preparedModel, callback, &sender, &receiver, &context));
+ ASSERT_NE(nullptr, sender.get());
+ ASSERT_NE(nullptr, receiver.get());
+ ASSERT_NE(nullptr, context.get());
+
+ // load memory into callback slots
+ std::vector<intptr_t> keys;
+ keys.reserve(request.pools.size());
+ std::transform(request.pools.begin(), request.pools.end(), std::back_inserter(keys),
+ [](const auto& pool) { return reinterpret_cast<intptr_t>(&pool); });
+ const std::vector<int32_t> slots = callback->getSlots(request.pools, keys);
+
+ // ensure slot std::numeric_limits<int32_t>::max() doesn't exist (for
+ // subsequent slot validation testing)
+ ASSERT_TRUE(std::all_of(slots.begin(), slots.end(), [](int32_t slot) {
+ return slot != std::numeric_limits<int32_t>::max();
+ }));
+
+ // serialize the request
+ const auto serialized = android::nn::serialize(request, MeasureTiming::YES, slots);
+
+ // validations
+ removeDatumTest(sender.get(), receiver.get(), serialized);
+ addDatumTest(sender.get(), receiver.get(), serialized);
+ mutateDatumTest(sender.get(), receiver.get(), serialized);
+}
+
+// This test validates that when the Result message size exceeds length of the
+// result FMQ, the service instance gracefully fails and returns an error.
+static void validateBurstFmqLength(const sp<IPreparedModel>& preparedModel,
+ const Request& request) {
+ // create regular burst
+ std::shared_ptr<ExecutionBurstController> controllerRegular;
+ ASSERT_NO_FATAL_FAILURE(createBurstWithResultChannelLength(
+ preparedModel, kExecutionBurstChannelLength, &controllerRegular));
+ ASSERT_NE(nullptr, controllerRegular.get());
+
+ // create burst with small output channel
+ std::shared_ptr<ExecutionBurstController> controllerSmall;
+ ASSERT_NO_FATAL_FAILURE(createBurstWithResultChannelLength(
+ preparedModel, kExecutionBurstChannelSmallLength, &controllerSmall));
+ ASSERT_NE(nullptr, controllerSmall.get());
+
+ // load memory into callback slots
+ std::vector<intptr_t> keys(request.pools.size());
+ for (size_t i = 0; i < keys.size(); ++i) {
+ keys[i] = reinterpret_cast<intptr_t>(&request.pools[i]);
+ }
+
+ // collect serialized result by running regular burst
+ const auto [statusRegular, outputShapesRegular, timingRegular] =
+ controllerRegular->compute(request, MeasureTiming::NO, keys);
+
+ // skip test if regular burst output isn't useful for testing a failure
+ // caused by having too small of a length for the result FMQ
+ const std::vector<FmqResultDatum> serialized =
+ android::nn::serialize(statusRegular, outputShapesRegular, timingRegular);
+ if (statusRegular != ErrorStatus::NONE ||
+ serialized.size() <= kExecutionBurstChannelSmallLength) {
+ return;
+ }
+
+ // by this point, execution should fail because the result channel isn't
+ // large enough to return the serialized result
+ const auto [statusSmall, outputShapesSmall, timingSmall] =
+ controllerSmall->compute(request, MeasureTiming::NO, keys);
+ EXPECT_NE(ErrorStatus::NONE, statusSmall);
+ EXPECT_EQ(0u, outputShapesSmall.size());
+ EXPECT_TRUE(badTiming(timingSmall));
+}
+
+static bool isSanitized(const FmqResultDatum& datum) {
+ using Discriminator = FmqResultDatum::hidl_discriminator;
+
+ // check to ensure the padding values in the returned
+ // FmqResultDatum::OperandInformation are initialized to 0
+ if (datum.getDiscriminator() == Discriminator::operandInformation) {
+ static_assert(
+ offsetof(FmqResultDatum::OperandInformation, isSufficient) == 0,
+ "unexpected value for offset of FmqResultDatum::OperandInformation::isSufficient");
+ static_assert(
+ sizeof(FmqResultDatum::OperandInformation::isSufficient) == 1,
+ "unexpected value for size of FmqResultDatum::OperandInformation::isSufficient");
+ static_assert(offsetof(FmqResultDatum::OperandInformation, numberOfDimensions) == 4,
+ "unexpected value for offset of "
+ "FmqResultDatum::OperandInformation::numberOfDimensions");
+ static_assert(sizeof(FmqResultDatum::OperandInformation::numberOfDimensions) == 4,
+ "unexpected value for size of "
+ "FmqResultDatum::OperandInformation::numberOfDimensions");
+ static_assert(sizeof(FmqResultDatum::OperandInformation) == 8,
+ "unexpected value for size of "
+ "FmqResultDatum::OperandInformation");
+
+ constexpr size_t paddingOffset =
+ offsetof(FmqResultDatum::OperandInformation, isSufficient) +
+ sizeof(FmqResultDatum::OperandInformation::isSufficient);
+ constexpr size_t paddingSize =
+ offsetof(FmqResultDatum::OperandInformation, numberOfDimensions) - paddingOffset;
+
+ FmqResultDatum::OperandInformation initialized{};
+ std::memset(&initialized, 0, sizeof(initialized));
+
+ const char* initializedPaddingStart =
+ reinterpret_cast<const char*>(&initialized) + paddingOffset;
+ const char* datumPaddingStart =
+ reinterpret_cast<const char*>(&datum.operandInformation()) + paddingOffset;
+
+ return std::memcmp(datumPaddingStart, initializedPaddingStart, paddingSize) == 0;
+ }
+
+ // there are no other padding initialization checks required, so return true
+ // for any sum-type that isn't FmqResultDatum::OperandInformation
+ return true;
+}
+
+static void validateBurstSanitized(const sp<IPreparedModel>& preparedModel,
+ const Request& request) {
+ // create burst
+ std::unique_ptr<RequestChannelSender> sender;
+ std::unique_ptr<ResultChannelReceiver> receiver;
+ sp<ExecutionBurstCallback> callback = new ExecutionBurstCallback();
+ sp<IBurstContext> context;
+ ASSERT_NO_FATAL_FAILURE(createBurst(preparedModel, callback, &sender, &receiver, &context));
+ ASSERT_NE(nullptr, sender.get());
+ ASSERT_NE(nullptr, receiver.get());
+ ASSERT_NE(nullptr, context.get());
+
+ // load memory into callback slots
+ std::vector<intptr_t> keys;
+ keys.reserve(request.pools.size());
+ std::transform(request.pools.begin(), request.pools.end(), std::back_inserter(keys),
+ [](const auto& pool) { return reinterpret_cast<intptr_t>(&pool); });
+ const std::vector<int32_t> slots = callback->getSlots(request.pools, keys);
+
+ // send valid request
+ ASSERT_TRUE(sender->send(request, MeasureTiming::YES, slots));
+
+ // receive valid result
+ auto serialized = receiver->getPacketBlocking();
+ ASSERT_TRUE(serialized.has_value());
+
+ // sanitize result
+ ASSERT_TRUE(std::all_of(serialized->begin(), serialized->end(), isSanitized))
+ << "The result serialized data is not properly sanitized";
+}
+
+///////////////////////////// ENTRY POINT //////////////////////////////////
+
+void validateBurst(const sp<IPreparedModel>& preparedModel, const Request& request) {
+ ASSERT_NO_FATAL_FAILURE(validateBurstSerialization(preparedModel, request));
+ ASSERT_NO_FATAL_FAILURE(validateBurstFmqLength(preparedModel, request));
+ ASSERT_NO_FATAL_FAILURE(validateBurstSanitized(preparedModel, request));
+}
+
+} // namespace android::hardware::neuralnetworks::V1_3::vts::functional
diff --git a/neuralnetworks/1.3/vts/functional/ValidateModel.cpp b/neuralnetworks/1.3/vts/functional/ValidateModel.cpp
new file mode 100644
index 0000000..44b32a9
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/ValidateModel.cpp
@@ -0,0 +1,718 @@
+/*
+ * Copyright (C) 2018 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include "1.0/Utils.h"
+#include "1.2/Callbacks.h"
+#include "GeneratedTestHarness.h"
+#include "VtsHalNeuralnetworks.h"
+
+namespace android::hardware::neuralnetworks::V1_3::vts::functional {
+
+using V1_0::ErrorStatus;
+using V1_0::OperandLifeTime;
+using V1_1::ExecutionPreference;
+using V1_2::IPreparedModel;
+using V1_2::OperationType;
+using V1_2::OperationTypeRange;
+using V1_2::SymmPerChannelQuantParams;
+using V1_2::implementation::PreparedModelCallback;
+using HidlToken =
+ hidl_array<uint8_t, static_cast<uint32_t>(V1_2::Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;
+
+///////////////////////// UTILITY FUNCTIONS /////////////////////////
+
+static void validateGetSupportedOperations(const sp<IDevice>& device, const std::string& message,
+ const Model& model) {
+ SCOPED_TRACE(message + " [getSupportedOperations_1_3]");
+
+ Return<void> ret = device->getSupportedOperations_1_3(
+ model, [&](ErrorStatus status, const hidl_vec<bool>&) {
+ EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, status);
+ });
+ EXPECT_TRUE(ret.isOk());
+}
+
+static void validatePrepareModel(const sp<IDevice>& device, const std::string& message,
+ const Model& model, ExecutionPreference preference) {
+ SCOPED_TRACE(message + " [prepareModel_1_3]");
+
+ sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+ Return<ErrorStatus> prepareLaunchStatus =
+ device->prepareModel_1_3(model, preference, hidl_vec<hidl_handle>(),
+ hidl_vec<hidl_handle>(), HidlToken(), preparedModelCallback);
+ ASSERT_TRUE(prepareLaunchStatus.isOk());
+ ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(prepareLaunchStatus));
+
+ preparedModelCallback->wait();
+ ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
+ ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, prepareReturnStatus);
+ sp<IPreparedModel> preparedModel = getPreparedModel_1_2(preparedModelCallback);
+ ASSERT_EQ(nullptr, preparedModel.get());
+}
+
+static bool validExecutionPreference(ExecutionPreference preference) {
+ return preference == ExecutionPreference::LOW_POWER ||
+ preference == ExecutionPreference::FAST_SINGLE_ANSWER ||
+ preference == ExecutionPreference::SUSTAINED_SPEED;
+}
+
+// Primary validation function. This function will take a valid model, apply a
+// mutation to it to invalidate the model, then pass it to interface calls that
+// use the model. Note that the model here is passed by value, and any mutation
+// to the model does not leave this function.
+static void validate(const sp<IDevice>& device, const std::string& message, Model model,
+ const std::function<void(Model*)>& mutation,
+ ExecutionPreference preference = ExecutionPreference::FAST_SINGLE_ANSWER) {
+ mutation(&model);
+ if (validExecutionPreference(preference)) {
+ validateGetSupportedOperations(device, message, model);
+ }
+ validatePrepareModel(device, message, model, preference);
+}
+
+static uint32_t addOperand(Model* model) {
+ return hidl_vec_push_back(&model->operands,
+ {
+ .type = OperandType::INT32,
+ .dimensions = {},
+ .numberOfConsumers = 0,
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::MODEL_INPUT,
+ .location = {.poolIndex = 0, .offset = 0, .length = 0},
+ });
+}
+
+static uint32_t addOperand(Model* model, OperandLifeTime lifetime) {
+ uint32_t index = addOperand(model);
+ model->operands[index].numberOfConsumers = 1;
+ model->operands[index].lifetime = lifetime;
+ return index;
+}
+
+///////////////////////// VALIDATE MODEL OPERAND TYPE /////////////////////////
+
+static const uint32_t invalidOperandTypes[] = {
+ static_cast<uint32_t>(OperandTypeRange::FUNDAMENTAL_MIN) - 1,
+ static_cast<uint32_t>(OperandTypeRange::FUNDAMENTAL_MAX) + 1,
+ static_cast<uint32_t>(OperandTypeRange::OEM_MIN) - 1,
+ static_cast<uint32_t>(OperandTypeRange::OEM_MAX) + 1,
+};
+
+static void mutateOperandTypeTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+ for (uint32_t invalidOperandType : invalidOperandTypes) {
+ const std::string message = "mutateOperandTypeTest: operand " +
+ std::to_string(operand) + " set to value " +
+ std::to_string(invalidOperandType);
+ validate(device, message, model, [operand, invalidOperandType](Model* model) {
+ model->operands[operand].type = static_cast<OperandType>(invalidOperandType);
+ });
+ }
+ }
+}
+
+///////////////////////// VALIDATE OPERAND RANK /////////////////////////
+
+static uint32_t getInvalidRank(OperandType type) {
+ switch (type) {
+ case OperandType::FLOAT16:
+ case OperandType::FLOAT32:
+ case OperandType::INT32:
+ case OperandType::UINT32:
+ case OperandType::BOOL:
+ return 1;
+ case OperandType::TENSOR_BOOL8:
+ case OperandType::TENSOR_FLOAT16:
+ case OperandType::TENSOR_FLOAT32:
+ case OperandType::TENSOR_INT32:
+ case OperandType::TENSOR_QUANT8_ASYMM:
+ case OperandType::TENSOR_QUANT8_SYMM:
+ case OperandType::TENSOR_QUANT16_ASYMM:
+ case OperandType::TENSOR_QUANT16_SYMM:
+ case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
+ return 0;
+ default:
+ return 0;
+ }
+}
+
+static void mutateOperandRankTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+ const uint32_t invalidRank = getInvalidRank(model.operands[operand].type);
+ if (invalidRank == 0) {
+ continue;
+ }
+ const std::string message = "mutateOperandRankTest: operand " + std::to_string(operand) +
+ " has rank of " + std::to_string(invalidRank);
+ validate(device, message, model, [operand, invalidRank](Model* model) {
+ model->operands[operand].dimensions = std::vector<uint32_t>(invalidRank, 0);
+ });
+ }
+}
+
+///////////////////////// VALIDATE OPERAND SCALE /////////////////////////
+
+static float getInvalidScale(OperandType type) {
+ switch (type) {
+ case OperandType::FLOAT16:
+ case OperandType::FLOAT32:
+ case OperandType::INT32:
+ case OperandType::UINT32:
+ case OperandType::BOOL:
+ case OperandType::TENSOR_BOOL8:
+ case OperandType::TENSOR_FLOAT16:
+ case OperandType::TENSOR_FLOAT32:
+ case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
+ return 1.0f;
+ case OperandType::TENSOR_INT32:
+ return -1.0f;
+ case OperandType::TENSOR_QUANT8_SYMM:
+ case OperandType::TENSOR_QUANT8_ASYMM:
+ case OperandType::TENSOR_QUANT16_ASYMM:
+ case OperandType::TENSOR_QUANT16_SYMM:
+ return 0.0f;
+ default:
+ return 0.0f;
+ }
+}
+
+static void mutateOperandScaleTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+ const float invalidScale = getInvalidScale(model.operands[operand].type);
+ const std::string message = "mutateOperandScaleTest: operand " + std::to_string(operand) +
+ " has scale of " + std::to_string(invalidScale);
+ validate(device, message, model, [operand, invalidScale](Model* model) {
+ model->operands[operand].scale = invalidScale;
+ });
+ }
+}
+
+///////////////////////// VALIDATE OPERAND ZERO POINT /////////////////////////
+
+static std::vector<int32_t> getInvalidZeroPoints(OperandType type) {
+ switch (type) {
+ case OperandType::FLOAT16:
+ case OperandType::FLOAT32:
+ case OperandType::INT32:
+ case OperandType::UINT32:
+ case OperandType::BOOL:
+ case OperandType::TENSOR_BOOL8:
+ case OperandType::TENSOR_FLOAT16:
+ case OperandType::TENSOR_FLOAT32:
+ case OperandType::TENSOR_INT32:
+ case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
+ return {1};
+ case OperandType::TENSOR_QUANT8_ASYMM:
+ return {-1, 256};
+ case OperandType::TENSOR_QUANT8_SYMM:
+ return {-129, -1, 1, 128};
+ case OperandType::TENSOR_QUANT16_ASYMM:
+ return {-1, 65536};
+ case OperandType::TENSOR_QUANT16_SYMM:
+ return {-32769, -1, 1, 32768};
+ default:
+ return {};
+ }
+}
+
+static void mutateOperandZeroPointTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+ const std::vector<int32_t> invalidZeroPoints =
+ getInvalidZeroPoints(model.operands[operand].type);
+ for (int32_t invalidZeroPoint : invalidZeroPoints) {
+ const std::string message = "mutateOperandZeroPointTest: operand " +
+ std::to_string(operand) + " has zero point of " +
+ std::to_string(invalidZeroPoint);
+ validate(device, message, model, [operand, invalidZeroPoint](Model* model) {
+ model->operands[operand].zeroPoint = invalidZeroPoint;
+ });
+ }
+ }
+}
+
+///////////////////////// VALIDATE EXTRA ??? /////////////////////////
+
+// TODO: Operand::lifetime
+// TODO: Operand::location
+
+///////////////////////// VALIDATE OPERATION OPERAND TYPE /////////////////////////
+
+static void mutateOperand(Operand* operand, OperandType type) {
+ Operand newOperand = *operand;
+ newOperand.type = type;
+ switch (type) {
+ case OperandType::FLOAT16:
+ case OperandType::FLOAT32:
+ case OperandType::INT32:
+ case OperandType::UINT32:
+ case OperandType::BOOL:
+ newOperand.dimensions = hidl_vec<uint32_t>();
+ newOperand.scale = 0.0f;
+ newOperand.zeroPoint = 0;
+ break;
+ case OperandType::TENSOR_BOOL8:
+ case OperandType::TENSOR_FLOAT16:
+ case OperandType::TENSOR_FLOAT32:
+ newOperand.dimensions =
+ operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
+ newOperand.scale = 0.0f;
+ newOperand.zeroPoint = 0;
+ break;
+ case OperandType::TENSOR_INT32:
+ newOperand.dimensions =
+ operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
+ newOperand.zeroPoint = 0;
+ break;
+ case OperandType::TENSOR_QUANT8_ASYMM:
+ case OperandType::TENSOR_QUANT8_SYMM:
+ case OperandType::TENSOR_QUANT16_ASYMM:
+ case OperandType::TENSOR_QUANT16_SYMM:
+ newOperand.dimensions =
+ operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
+ newOperand.scale = operand->scale != 0.0f ? operand->scale : 1.0f;
+ break;
+ case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL: {
+ newOperand.dimensions =
+ operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
+ newOperand.scale = 0.0f;
+ newOperand.zeroPoint = 0;
+
+ SymmPerChannelQuantParams channelQuant;
+ channelQuant.channelDim = 0;
+ channelQuant.scales = hidl_vec<float>(
+ operand->dimensions.size() > 0 ? static_cast<size_t>(operand->dimensions[0])
+ : 0);
+ for (size_t i = 0; i < channelQuant.scales.size(); ++i) {
+ channelQuant.scales[i] = 1.0f;
+ }
+ newOperand.extraParams.channelQuant(std::move(channelQuant));
+ } break;
+ case OperandType::OEM:
+ case OperandType::TENSOR_OEM_BYTE:
+ default:
+ break;
+ }
+ *operand = newOperand;
+}
+
+static bool mutateOperationOperandTypeSkip(size_t operand, OperandType type, const Model& model) {
+ // Do not test OEM types
+ if (type == model.operands[operand].type || type == OperandType::OEM ||
+ type == OperandType::TENSOR_OEM_BYTE) {
+ return true;
+ }
+ for (const Operation& operation : model.operations) {
+ // Skip mutateOperationOperandTypeTest for the following operations.
+ // - LSH_PROJECTION's second argument is allowed to have any type.
+ // - ARGMIN and ARGMAX's first argument can be any of
+ // TENSOR_(FLOAT16|FLOAT32|INT32|QUANT8_ASYMM).
+ // - CAST's argument can be any of TENSOR_(FLOAT16|FLOAT32|INT32|QUANT8_ASYMM).
+ // - RANDOM_MULTINOMIAL's argument can be either TENSOR_FLOAT16 or TENSOR_FLOAT32.
+ // - DEQUANTIZE input can be any of
+ // TENSOR_(QUANT8_ASYMM|QUANT8_SYMM|QUANT8_SYMM_PER_CHANNEL), output can
+ // be of either TENSOR_FLOAT16 or TENSOR_FLOAT32.
+ // - QUANTIZE input can be either TENSOR_FLOAT16 or TENSOR_FLOAT32
+ // - CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
+ // - DEPTHWISE_CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
+ // - GROUPED_CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
+ // - TRANSPOSE_CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
+ switch (operation.type) {
+ case OperationType::LSH_PROJECTION: {
+ if (operand == operation.inputs[1]) {
+ return true;
+ }
+ } break;
+ case OperationType::CAST:
+ case OperationType::ARGMAX:
+ case OperationType::ARGMIN: {
+ if (type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32 ||
+ type == OperandType::TENSOR_INT32 || type == OperandType::TENSOR_QUANT8_ASYMM) {
+ return true;
+ }
+ } break;
+ case OperationType::QUANTIZE:
+ case OperationType::RANDOM_MULTINOMIAL: {
+ if (operand == operation.inputs[0] &&
+ (type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32)) {
+ return true;
+ }
+ } break;
+ case OperationType::DEQUANTIZE: {
+ if (operand == operation.inputs[0] &&
+ (type == OperandType::TENSOR_QUANT8_ASYMM ||
+ type == OperandType::TENSOR_QUANT8_SYMM ||
+ type == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL)) {
+ return true;
+ }
+ if (operand == operation.outputs[0] &&
+ (type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32)) {
+ return true;
+ }
+ } break;
+ case OperationType::TRANSPOSE_CONV_2D:
+ case OperationType::GROUPED_CONV_2D:
+ case OperationType::DEPTHWISE_CONV_2D:
+ case OperationType::CONV_2D: {
+ if (operand == operation.inputs[1] &&
+ (type == OperandType::TENSOR_QUANT8_ASYMM ||
+ type == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL)) {
+ return true;
+ }
+ } break;
+ default:
+ break;
+ }
+ }
+ return false;
+}
+
+static void mutateOperationOperandTypeTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+ for (OperandType invalidOperandType : hidl_enum_range<OperandType>{}) {
+ if (mutateOperationOperandTypeSkip(operand, invalidOperandType, model)) {
+ continue;
+ }
+ const std::string message = "mutateOperationOperandTypeTest: operand " +
+ std::to_string(operand) + " set to type " +
+ toString(invalidOperandType);
+ validate(device, message, model, [operand, invalidOperandType](Model* model) {
+ mutateOperand(&model->operands[operand], invalidOperandType);
+ });
+ }
+ }
+}
+
+///////////////////////// VALIDATE MODEL OPERATION TYPE /////////////////////////
+
+static const uint32_t invalidOperationTypes[] = {
+ static_cast<uint32_t>(OperationTypeRange::FUNDAMENTAL_MAX) + 1,
+ static_cast<uint32_t>(OperationTypeRange::OEM_MIN) - 1,
+ static_cast<uint32_t>(OperationTypeRange::OEM_MAX) + 1,
+};
+
+static void mutateOperationTypeTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+ for (uint32_t invalidOperationType : invalidOperationTypes) {
+ const std::string message = "mutateOperationTypeTest: operation " +
+ std::to_string(operation) + " set to value " +
+ std::to_string(invalidOperationType);
+ validate(device, message, model, [operation, invalidOperationType](Model* model) {
+ model->operations[operation].type =
+ static_cast<OperationType>(invalidOperationType);
+ });
+ }
+ }
+}
+
+///////////////////////// VALIDATE MODEL OPERATION INPUT OPERAND INDEX /////////////////////////
+
+static void mutateOperationInputOperandIndexTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+ const uint32_t invalidOperand = model.operands.size();
+ for (size_t input = 0; input < model.operations[operation].inputs.size(); ++input) {
+ const std::string message = "mutateOperationInputOperandIndexTest: operation " +
+ std::to_string(operation) + " input " +
+ std::to_string(input);
+ validate(device, message, model, [operation, input, invalidOperand](Model* model) {
+ model->operations[operation].inputs[input] = invalidOperand;
+ });
+ }
+ }
+}
+
+///////////////////////// VALIDATE MODEL OPERATION OUTPUT OPERAND INDEX /////////////////////////
+
+static void mutateOperationOutputOperandIndexTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+ const uint32_t invalidOperand = model.operands.size();
+ for (size_t output = 0; output < model.operations[operation].outputs.size(); ++output) {
+ const std::string message = "mutateOperationOutputOperandIndexTest: operation " +
+ std::to_string(operation) + " output " +
+ std::to_string(output);
+ validate(device, message, model, [operation, output, invalidOperand](Model* model) {
+ model->operations[operation].outputs[output] = invalidOperand;
+ });
+ }
+ }
+}
+
+///////////////////////// REMOVE OPERAND FROM EVERYTHING /////////////////////////
+
+static void removeValueAndDecrementGreaterValues(hidl_vec<uint32_t>* vec, uint32_t value) {
+ if (vec) {
+ // remove elements matching "value"
+ auto last = std::remove(vec->begin(), vec->end(), value);
+ vec->resize(std::distance(vec->begin(), last));
+
+ // decrement elements exceeding "value"
+ std::transform(vec->begin(), vec->end(), vec->begin(),
+ [value](uint32_t v) { return v > value ? v-- : v; });
+ }
+}
+
+static void removeOperand(Model* model, uint32_t index) {
+ hidl_vec_removeAt(&model->operands, index);
+ for (Operation& operation : model->operations) {
+ removeValueAndDecrementGreaterValues(&operation.inputs, index);
+ removeValueAndDecrementGreaterValues(&operation.outputs, index);
+ }
+ removeValueAndDecrementGreaterValues(&model->inputIndexes, index);
+ removeValueAndDecrementGreaterValues(&model->outputIndexes, index);
+}
+
+static bool removeOperandSkip(size_t operand, const Model& model) {
+ for (const Operation& operation : model.operations) {
+ // Skip removeOperandTest for the following operations.
+ // - SPLIT's outputs are not checked during prepareModel.
+ if (operation.type == OperationType::SPLIT) {
+ for (const size_t outOprand : operation.outputs) {
+ if (operand == outOprand) {
+ return true;
+ }
+ }
+ }
+ // BIDIRECTIONAL_SEQUENCE_LSTM and BIDIRECTIONAL_SEQUENCE_RNN can have either one or two
+ // outputs depending on their mergeOutputs parameter.
+ if (operation.type == OperationType::BIDIRECTIONAL_SEQUENCE_LSTM ||
+ operation.type == OperationType::BIDIRECTIONAL_SEQUENCE_RNN) {
+ for (const size_t outOprand : operation.outputs) {
+ if (operand == outOprand) {
+ return true;
+ }
+ }
+ }
+ }
+ return false;
+}
+
+static void removeOperandTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+ if (removeOperandSkip(operand, model)) {
+ continue;
+ }
+ const std::string message = "removeOperandTest: operand " + std::to_string(operand);
+ validate(device, message, model,
+ [operand](Model* model) { removeOperand(model, operand); });
+ }
+}
+
+///////////////////////// REMOVE OPERATION /////////////////////////
+
+static void removeOperation(Model* model, uint32_t index) {
+ for (uint32_t operand : model->operations[index].inputs) {
+ model->operands[operand].numberOfConsumers--;
+ }
+ hidl_vec_removeAt(&model->operations, index);
+}
+
+static void removeOperationTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+ const std::string message = "removeOperationTest: operation " + std::to_string(operation);
+ validate(device, message, model,
+ [operation](Model* model) { removeOperation(model, operation); });
+ }
+}
+
+///////////////////////// REMOVE OPERATION INPUT /////////////////////////
+
+static bool removeOperationInputSkip(const Operation& op, size_t input) {
+ // Skip removeOperationInputTest for the following operations.
+ // - CONCATENATION has at least 2 inputs, with the last element being INT32.
+ // - CONV_2D, DEPTHWISE_CONV_2D, MAX_POOL_2D, AVERAGE_POOL_2D, L2_POOL_2D, RESIZE_BILINEAR,
+ // SPACE_TO_DEPTH, SPACE_TO_DEPTH, SPACE_TO_BATCH_ND, BATCH_TO_SPACE_ND can have an optional
+ // layout parameter.
+ // - L2_NORMALIZATION, LOCAL_RESPONSE_NORMALIZATION, SOFTMAX can have an optional axis
+ // parameter.
+ switch (op.type) {
+ case OperationType::CONCATENATION: {
+ if (op.inputs.size() > 2 && input != op.inputs.size() - 1) {
+ return true;
+ }
+ } break;
+ case OperationType::DEPTHWISE_CONV_2D: {
+ if ((op.inputs.size() == 12 && input == 11) || (op.inputs.size() == 9 && input == 8)) {
+ return true;
+ }
+ } break;
+ case OperationType::CONV_2D:
+ case OperationType::AVERAGE_POOL_2D:
+ case OperationType::MAX_POOL_2D:
+ case OperationType::L2_POOL_2D: {
+ if ((op.inputs.size() == 11 && input == 10) || (op.inputs.size() == 8 && input == 7)) {
+ return true;
+ }
+ } break;
+ case OperationType::RESIZE_BILINEAR: {
+ if (op.inputs.size() == 4 && input == 3) {
+ return true;
+ }
+ } break;
+ case OperationType::SPACE_TO_DEPTH:
+ case OperationType::DEPTH_TO_SPACE:
+ case OperationType::BATCH_TO_SPACE_ND: {
+ if (op.inputs.size() == 3 && input == 2) {
+ return true;
+ }
+ } break;
+ case OperationType::SPACE_TO_BATCH_ND: {
+ if (op.inputs.size() == 4 && input == 3) {
+ return true;
+ }
+ } break;
+ case OperationType::L2_NORMALIZATION: {
+ if (op.inputs.size() == 2 && input == 1) {
+ return true;
+ }
+ } break;
+ case OperationType::LOCAL_RESPONSE_NORMALIZATION: {
+ if (op.inputs.size() == 6 && input == 5) {
+ return true;
+ }
+ } break;
+ case OperationType::SOFTMAX: {
+ if (op.inputs.size() == 3 && input == 2) {
+ return true;
+ }
+ } break;
+ default:
+ break;
+ }
+ return false;
+}
+
+static void removeOperationInputTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+ for (size_t input = 0; input < model.operations[operation].inputs.size(); ++input) {
+ const Operation& op = model.operations[operation];
+ if (removeOperationInputSkip(op, input)) {
+ continue;
+ }
+ const std::string message = "removeOperationInputTest: operation " +
+ std::to_string(operation) + ", input " +
+ std::to_string(input);
+ validate(device, message, model, [operation, input](Model* model) {
+ uint32_t operand = model->operations[operation].inputs[input];
+ model->operands[operand].numberOfConsumers--;
+ hidl_vec_removeAt(&model->operations[operation].inputs, input);
+ });
+ }
+ }
+}
+
+///////////////////////// REMOVE OPERATION OUTPUT /////////////////////////
+
+static void removeOperationOutputTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+ for (size_t output = 0; output < model.operations[operation].outputs.size(); ++output) {
+ const std::string message = "removeOperationOutputTest: operation " +
+ std::to_string(operation) + ", output " +
+ std::to_string(output);
+ validate(device, message, model, [operation, output](Model* model) {
+ hidl_vec_removeAt(&model->operations[operation].outputs, output);
+ });
+ }
+ }
+}
+
+///////////////////////// MODEL VALIDATION /////////////////////////
+
+// TODO: remove model input
+// TODO: remove model output
+// TODO: add unused operation
+
+///////////////////////// ADD OPERATION INPUT /////////////////////////
+
+static bool addOperationInputSkip(const Operation& op) {
+ // Skip addOperationInputTest for the following operations.
+ // - L2_NORMALIZATION, LOCAL_RESPONSE_NORMALIZATION, SOFTMAX can have an optional INT32 axis
+ // parameter.
+ if ((op.type == OperationType::L2_NORMALIZATION && op.inputs.size() == 1) ||
+ (op.type == OperationType::LOCAL_RESPONSE_NORMALIZATION && op.inputs.size() == 5) ||
+ (op.type == OperationType::SOFTMAX && op.inputs.size() == 2)) {
+ return true;
+ }
+ return false;
+}
+
+static void addOperationInputTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+ if (addOperationInputSkip(model.operations[operation])) {
+ continue;
+ }
+ const std::string message = "addOperationInputTest: operation " + std::to_string(operation);
+ validate(device, message, model, [operation](Model* model) {
+ uint32_t index = addOperand(model, OperandLifeTime::MODEL_INPUT);
+ hidl_vec_push_back(&model->operations[operation].inputs, index);
+ hidl_vec_push_back(&model->inputIndexes, index);
+ });
+ }
+}
+
+///////////////////////// ADD OPERATION OUTPUT /////////////////////////
+
+static void addOperationOutputTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+ const std::string message =
+ "addOperationOutputTest: operation " + std::to_string(operation);
+ validate(device, message, model, [operation](Model* model) {
+ uint32_t index = addOperand(model, OperandLifeTime::MODEL_OUTPUT);
+ hidl_vec_push_back(&model->operations[operation].outputs, index);
+ hidl_vec_push_back(&model->outputIndexes, index);
+ });
+ }
+}
+
+///////////////////////// VALIDATE EXECUTION PREFERENCE /////////////////////////
+
+static const int32_t invalidExecutionPreferences[] = {
+ static_cast<int32_t>(ExecutionPreference::LOW_POWER) - 1, // lower bound
+ static_cast<int32_t>(ExecutionPreference::SUSTAINED_SPEED) + 1, // upper bound
+};
+
+static void mutateExecutionPreferenceTest(const sp<IDevice>& device, const Model& model) {
+ for (int32_t preference : invalidExecutionPreferences) {
+ const std::string message =
+ "mutateExecutionPreferenceTest: preference " + std::to_string(preference);
+ validate(
+ device, message, model, [](Model*) {},
+ static_cast<ExecutionPreference>(preference));
+ }
+}
+
+////////////////////////// ENTRY POINT //////////////////////////////
+
+void validateModel(const sp<IDevice>& device, const Model& model) {
+ mutateOperandTypeTest(device, model);
+ mutateOperandRankTest(device, model);
+ mutateOperandScaleTest(device, model);
+ mutateOperandZeroPointTest(device, model);
+ mutateOperationOperandTypeTest(device, model);
+ mutateOperationTypeTest(device, model);
+ mutateOperationInputOperandIndexTest(device, model);
+ mutateOperationOutputOperandIndexTest(device, model);
+ removeOperandTest(device, model);
+ removeOperationTest(device, model);
+ removeOperationInputTest(device, model);
+ removeOperationOutputTest(device, model);
+ addOperationInputTest(device, model);
+ addOperationOutputTest(device, model);
+ mutateExecutionPreferenceTest(device, model);
+}
+
+} // namespace android::hardware::neuralnetworks::V1_3::vts::functional
diff --git a/neuralnetworks/1.3/vts/functional/ValidateRequest.cpp b/neuralnetworks/1.3/vts/functional/ValidateRequest.cpp
new file mode 100644
index 0000000..6122123
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/ValidateRequest.cpp
@@ -0,0 +1,172 @@
+/*
+ * Copyright (C) 2018 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include "1.0/Utils.h"
+#include "1.2/Callbacks.h"
+#include "ExecutionBurstController.h"
+#include "GeneratedTestHarness.h"
+#include "TestHarness.h"
+#include "Utils.h"
+#include "VtsHalNeuralnetworks.h"
+
+namespace android::hardware::neuralnetworks::V1_3::vts::functional {
+
+using V1_0::ErrorStatus;
+using V1_0::Request;
+using V1_2::IPreparedModel;
+using V1_2::MeasureTiming;
+using V1_2::OutputShape;
+using V1_2::Timing;
+using V1_2::implementation::ExecutionCallback;
+
+///////////////////////// UTILITY FUNCTIONS /////////////////////////
+
+static bool badTiming(Timing timing) {
+ return timing.timeOnDevice == UINT64_MAX && timing.timeInDriver == UINT64_MAX;
+}
+
+// Primary validation function. This function will take a valid request, apply a
+// mutation to it to invalidate the request, then pass it to interface calls
+// that use the request. Note that the request here is passed by value, and any
+// mutation to the request does not leave this function.
+static void validate(const sp<IPreparedModel>& preparedModel, const std::string& message,
+ Request request, const std::function<void(Request*)>& mutation) {
+ mutation(&request);
+
+ // We'd like to test both with timing requested and without timing
+ // requested. Rather than running each test both ways, we'll decide whether
+ // to request timing by hashing the message. We do not use std::hash because
+ // it is not guaranteed stable across executions.
+ char hash = 0;
+ for (auto c : message) {
+ hash ^= c;
+ };
+ MeasureTiming measure = (hash & 1) ? MeasureTiming::YES : MeasureTiming::NO;
+
+ // asynchronous
+ {
+ SCOPED_TRACE(message + " [execute_1_2]");
+
+ sp<ExecutionCallback> executionCallback = new ExecutionCallback();
+ Return<ErrorStatus> executeLaunchStatus =
+ preparedModel->execute_1_2(request, measure, executionCallback);
+ ASSERT_TRUE(executeLaunchStatus.isOk());
+ ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeLaunchStatus));
+
+ executionCallback->wait();
+ ErrorStatus executionReturnStatus = executionCallback->getStatus();
+ const auto& outputShapes = executionCallback->getOutputShapes();
+ Timing timing = executionCallback->getTiming();
+ ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, executionReturnStatus);
+ ASSERT_EQ(outputShapes.size(), 0);
+ ASSERT_TRUE(badTiming(timing));
+ }
+
+ // synchronous
+ {
+ SCOPED_TRACE(message + " [executeSynchronously]");
+
+ Return<void> executeStatus = preparedModel->executeSynchronously(
+ request, measure,
+ [](ErrorStatus error, const hidl_vec<OutputShape>& outputShapes,
+ const Timing& timing) {
+ ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, error);
+ EXPECT_EQ(outputShapes.size(), 0);
+ EXPECT_TRUE(badTiming(timing));
+ });
+ ASSERT_TRUE(executeStatus.isOk());
+ }
+
+ // burst
+ {
+ SCOPED_TRACE(message + " [burst]");
+
+ // create burst
+ std::shared_ptr<::android::nn::ExecutionBurstController> burst =
+ android::nn::ExecutionBurstController::create(preparedModel, /*blocking=*/true);
+ ASSERT_NE(nullptr, burst.get());
+
+ // create memory keys
+ std::vector<intptr_t> keys(request.pools.size());
+ for (size_t i = 0; i < keys.size(); ++i) {
+ keys[i] = reinterpret_cast<intptr_t>(&request.pools[i]);
+ }
+
+ // execute and verify
+ ErrorStatus error;
+ std::vector<OutputShape> outputShapes;
+ Timing timing;
+ std::tie(error, outputShapes, timing) = burst->compute(request, measure, keys);
+ EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, error);
+ EXPECT_EQ(outputShapes.size(), 0);
+ EXPECT_TRUE(badTiming(timing));
+
+ // additional burst testing
+ if (request.pools.size() > 0) {
+ // valid free
+ burst->freeMemory(keys.front());
+
+ // negative test: invalid free of unknown (blank) memory
+ burst->freeMemory(intptr_t{});
+
+ // negative test: double free of memory
+ burst->freeMemory(keys.front());
+ }
+ }
+}
+
+///////////////////////// REMOVE INPUT ////////////////////////////////////
+
+static void removeInputTest(const sp<IPreparedModel>& preparedModel, const Request& request) {
+ for (size_t input = 0; input < request.inputs.size(); ++input) {
+ const std::string message = "removeInput: removed input " + std::to_string(input);
+ validate(preparedModel, message, request,
+ [input](Request* request) { hidl_vec_removeAt(&request->inputs, input); });
+ }
+}
+
+///////////////////////// REMOVE OUTPUT ////////////////////////////////////
+
+static void removeOutputTest(const sp<IPreparedModel>& preparedModel, const Request& request) {
+ for (size_t output = 0; output < request.outputs.size(); ++output) {
+ const std::string message = "removeOutput: removed Output " + std::to_string(output);
+ validate(preparedModel, message, request,
+ [output](Request* request) { hidl_vec_removeAt(&request->outputs, output); });
+ }
+}
+
+///////////////////////////// ENTRY POINT //////////////////////////////////
+
+void validateRequest(const sp<IPreparedModel>& preparedModel, const Request& request) {
+ removeInputTest(preparedModel, request);
+ removeOutputTest(preparedModel, request);
+}
+
+void validateRequestFailure(const sp<IPreparedModel>& preparedModel, const Request& request) {
+ SCOPED_TRACE("Expecting request to fail [executeSynchronously]");
+ Return<void> executeStatus = preparedModel->executeSynchronously(
+ request, MeasureTiming::NO,
+ [](ErrorStatus error, const hidl_vec<OutputShape>& outputShapes, const Timing& timing) {
+ ASSERT_NE(ErrorStatus::NONE, error);
+ EXPECT_EQ(outputShapes.size(), 0);
+ EXPECT_TRUE(badTiming(timing));
+ });
+ ASSERT_TRUE(executeStatus.isOk());
+}
+
+} // namespace android::hardware::neuralnetworks::V1_3::vts::functional
diff --git a/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.cpp b/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.cpp
new file mode 100644
index 0000000..4f0e150
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.cpp
@@ -0,0 +1,173 @@
+/*
+ * Copyright (C) 2018 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include "VtsHalNeuralnetworks.h"
+#include <android-base/logging.h>
+#include <hidl/ServiceManagement.h>
+#include <string>
+#include <utility>
+#include "1.0/Callbacks.h"
+#include "1.0/Utils.h"
+#include "GeneratedTestHarness.h"
+#include "TestHarness.h"
+
+namespace android::hardware::neuralnetworks::V1_3::vts::functional {
+
+using HidlToken =
+ hidl_array<uint8_t, static_cast<uint32_t>(V1_2::Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;
+using V1_0::ErrorStatus;
+using V1_0::Request;
+using V1_1::ExecutionPreference;
+using V1_2::IPreparedModel;
+using V1_2::implementation::PreparedModelCallback;
+
+// internal helper function
+void createPreparedModel(const sp<IDevice>& device, const Model& model,
+ sp<IPreparedModel>* preparedModel) {
+ ASSERT_NE(nullptr, preparedModel);
+ *preparedModel = nullptr;
+
+ // see if service can handle model
+ bool fullySupportsModel = false;
+ const Return<void> supportedCall = device->getSupportedOperations_1_3(
+ model, [&fullySupportsModel](ErrorStatus status, const hidl_vec<bool>& supported) {
+ ASSERT_EQ(ErrorStatus::NONE, status);
+ ASSERT_NE(0ul, supported.size());
+ fullySupportsModel = std::all_of(supported.begin(), supported.end(),
+ [](bool valid) { return valid; });
+ });
+ ASSERT_TRUE(supportedCall.isOk());
+
+ // launch prepare model
+ const sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+ const Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_3(
+ model, ExecutionPreference::FAST_SINGLE_ANSWER, hidl_vec<hidl_handle>(),
+ hidl_vec<hidl_handle>(), HidlToken(), preparedModelCallback);
+ ASSERT_TRUE(prepareLaunchStatus.isOk());
+ ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
+
+ // retrieve prepared model
+ preparedModelCallback->wait();
+ const ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
+ *preparedModel = getPreparedModel_1_2(preparedModelCallback);
+
+ // The getSupportedOperations_1_3 call returns a list of operations that are
+ // guaranteed not to fail if prepareModel_1_3 is called, and
+ // 'fullySupportsModel' is true i.f.f. the entire model is guaranteed.
+ // If a driver has any doubt that it can prepare an operation, it must
+ // return false. So here, if a driver isn't sure if it can support an
+ // operation, but reports that it successfully prepared the model, the test
+ // can continue.
+ if (!fullySupportsModel && prepareReturnStatus != ErrorStatus::NONE) {
+ ASSERT_EQ(nullptr, preparedModel->get());
+ LOG(INFO) << "NN VTS: Early termination of test because vendor service cannot prepare "
+ "model that it does not support.";
+ std::cout << "[ ] Early termination of test because vendor service cannot "
+ "prepare model that it does not support."
+ << std::endl;
+ GTEST_SKIP();
+ }
+ ASSERT_EQ(ErrorStatus::NONE, prepareReturnStatus);
+ ASSERT_NE(nullptr, preparedModel->get());
+}
+
+void NeuralnetworksHidlTest::SetUp() {
+ testing::TestWithParam<NeuralnetworksHidlTestParam>::SetUp();
+ ASSERT_NE(kDevice, nullptr);
+}
+
+static NamedDevice makeNamedDevice(const std::string& name) {
+ return {name, IDevice::getService(name)};
+}
+
+static std::vector<NamedDevice> getNamedDevicesImpl() {
+ // Retrieves the name of all service instances that implement IDevice,
+ // including any Lazy HAL instances.
+ const std::vector<std::string> names = hardware::getAllHalInstanceNames(IDevice::descriptor);
+
+ // Get a handle to each device and pair it with its name.
+ std::vector<NamedDevice> namedDevices;
+ namedDevices.reserve(names.size());
+ std::transform(names.begin(), names.end(), std::back_inserter(namedDevices), makeNamedDevice);
+ return namedDevices;
+}
+
+const std::vector<NamedDevice>& getNamedDevices() {
+ const static std::vector<NamedDevice> devices = getNamedDevicesImpl();
+ return devices;
+}
+
+std::string printNeuralnetworksHidlTest(
+ const testing::TestParamInfo<NeuralnetworksHidlTestParam>& info) {
+ return gtestCompliantName(getName(info.param));
+}
+
+INSTANTIATE_DEVICE_TEST(NeuralnetworksHidlTest);
+
+// Forward declaration from ValidateModel.cpp
+void validateModel(const sp<IDevice>& device, const Model& model);
+// Forward declaration from ValidateRequest.cpp
+void validateRequest(const sp<IPreparedModel>& preparedModel, const V1_0::Request& request);
+// Forward declaration from ValidateRequest.cpp
+void validateRequestFailure(const sp<IPreparedModel>& preparedModel, const V1_0::Request& request);
+// Forward declaration from ValidateBurst.cpp
+void validateBurst(const sp<IPreparedModel>& preparedModel, const V1_0::Request& request);
+
+void validateEverything(const sp<IDevice>& device, const Model& model, const Request& request) {
+ validateModel(device, model);
+
+ // Create IPreparedModel.
+ sp<IPreparedModel> preparedModel;
+ createPreparedModel(device, model, &preparedModel);
+ if (preparedModel == nullptr) return;
+
+ validateRequest(preparedModel, request);
+ validateBurst(preparedModel, request);
+}
+
+void validateFailure(const sp<IDevice>& device, const Model& model, const Request& request) {
+ // TODO: Should this always succeed?
+ // What if the invalid input is part of the model (i.e., a parameter).
+ validateModel(device, model);
+
+ // Create IPreparedModel.
+ sp<IPreparedModel> preparedModel;
+ createPreparedModel(device, model, &preparedModel);
+ if (preparedModel == nullptr) return;
+
+ validateRequestFailure(preparedModel, request);
+}
+
+TEST_P(ValidationTest, Test) {
+ const Model model = createModel(kTestModel);
+ const Request request = createRequest(kTestModel);
+ if (kTestModel.expectFailure) {
+ validateFailure(kDevice, model, request);
+ } else {
+ validateEverything(kDevice, model, request);
+ }
+}
+
+INSTANTIATE_GENERATED_TEST(ValidationTest, [](const test_helper::TestModel&) { return true; });
+
+sp<IPreparedModel> getPreparedModel_1_2(const sp<PreparedModelCallback>& callback) {
+ sp<V1_0::IPreparedModel> preparedModelV1_0 = callback->getPreparedModel();
+ return IPreparedModel::castFrom(preparedModelV1_0).withDefault(nullptr);
+}
+
+} // namespace android::hardware::neuralnetworks::V1_3::vts::functional
diff --git a/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.h b/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.h
new file mode 100644
index 0000000..fc654ce
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.h
@@ -0,0 +1,58 @@
+/*
+ * Copyright (C) 2018 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef ANDROID_HARDWARE_NEURALNETWORKS_V1_3_VTS_HAL_NEURALNETWORKS_H
+#define ANDROID_HARDWARE_NEURALNETWORKS_V1_3_VTS_HAL_NEURALNETWORKS_H
+
+#include <android/hardware/neuralnetworks/1.2/IPreparedModel.h>
+#include <android/hardware/neuralnetworks/1.3/IDevice.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <gtest/gtest.h>
+#include "1.0/Utils.h"
+#include "1.2/Callbacks.h"
+
+namespace android::hardware::neuralnetworks::V1_3::vts::functional {
+
+using NamedDevice = Named<sp<IDevice>>;
+using NeuralnetworksHidlTestParam = NamedDevice;
+
+class NeuralnetworksHidlTest : public testing::TestWithParam<NeuralnetworksHidlTestParam> {
+ protected:
+ void SetUp() override;
+ const sp<IDevice> kDevice = getData(GetParam());
+};
+
+const std::vector<NamedDevice>& getNamedDevices();
+
+std::string printNeuralnetworksHidlTest(
+ const testing::TestParamInfo<NeuralnetworksHidlTestParam>& info);
+
+#define INSTANTIATE_DEVICE_TEST(TestSuite) \
+ INSTANTIATE_TEST_SUITE_P(PerInstance, TestSuite, testing::ValuesIn(getNamedDevices()), \
+ printNeuralnetworksHidlTest)
+
+// Create an IPreparedModel object. If the model cannot be prepared,
+// "preparedModel" will be nullptr instead.
+void createPreparedModel(const sp<IDevice>& device, const Model& model,
+ sp<V1_2::IPreparedModel>* preparedModel);
+
+// Utility function to get PreparedModel from callback and downcast to V1_2.
+sp<V1_2::IPreparedModel> getPreparedModel_1_2(
+ const sp<V1_2::implementation::PreparedModelCallback>& callback);
+
+} // namespace android::hardware::neuralnetworks::V1_3::vts::functional
+
+#endif // ANDROID_HARDWARE_NEURALNETWORKS_V1_3_VTS_HAL_NEURALNETWORKS_H
diff --git a/power/stats/1.0/default/PowerStats.cpp b/power/stats/1.0/default/PowerStats.cpp
index 78766f2..68275ce 100644
--- a/power/stats/1.0/default/PowerStats.cpp
+++ b/power/stats/1.0/default/PowerStats.cpp
@@ -87,7 +87,7 @@
std::string railFileName;
std::string spsFileName;
uint32_t index = 0;
- uint32_t samplingRate;
+ unsigned long samplingRate;
for (const auto& path : mPm.devicePaths) {
railFileName = path + "/enabled_rails";
spsFileName = path + "/sampling_rate";
@@ -109,10 +109,11 @@
while (std::getline(railNames, line)) {
std::vector<std::string> words = android::base::Split(line, ":");
if (words.size() == 2) {
- mPm.railsInfo.emplace(words[0], RailData{.devicePath = path,
- .index = index,
- .subsysName = words[1],
- .samplingRate = samplingRate});
+ mPm.railsInfo.emplace(
+ words[0], RailData{.devicePath = path,
+ .index = index,
+ .subsysName = words[1],
+ .samplingRate = static_cast<uint32_t>(samplingRate)});
index++;
} else {
ALOGW("Unexpected format in file: %s", railFileName.c_str());
diff --git a/radio/1.2/types.hal b/radio/1.2/types.hal
index dffebd3..f10d753 100644
--- a/radio/1.2/types.hal
+++ b/radio/1.2/types.hal
@@ -161,7 +161,8 @@
ScanType type;
/**
- * Time interval in seconds between periodic scans, only valid when type = PERIODIC
+ * Time interval in seconds between the completion of one scan and the start of a subsequent scan.
+ * This field is only valid when 'type' is 'PERIODIC'.
* Range: ScanIntervalRange:MIN to ScanIntervalRange:MAX
*/
int32_t interval;
diff --git a/sensors/1.0/default/android.hardware.sensors@1.0-service.rc b/sensors/1.0/default/android.hardware.sensors@1.0-service.rc
index 4faa562..b41730b 100644
--- a/sensors/1.0/default/android.hardware.sensors@1.0-service.rc
+++ b/sensors/1.0/default/android.hardware.sensors@1.0-service.rc
@@ -2,6 +2,6 @@
interface android.hardware.sensors@1.0::ISensors default
class hal
user system
- group system wakelock
+ group system wakelock uhid
capabilities BLOCK_SUSPEND
rlimit rtprio 10 10
diff --git a/sensors/2.0/multihal/Android.bp b/sensors/2.0/multihal/Android.bp
index 216cc20..710835f 100644
--- a/sensors/2.0/multihal/Android.bp
+++ b/sensors/2.0/multihal/Android.bp
@@ -24,7 +24,6 @@
"libcutils",
"libfmq",
"libhidlbase",
- "libhidltransport",
"liblog",
"libpower",
"libutils",
diff --git a/sensors/2.0/multihal/HalProxy.cpp b/sensors/2.0/multihal/HalProxy.cpp
index f051a11..1fbc787 100644
--- a/sensors/2.0/multihal/HalProxy.cpp
+++ b/sensors/2.0/multihal/HalProxy.cpp
@@ -49,8 +49,15 @@
}
HalProxy::~HalProxy() {
- // TODO: Join any running threads and clean up FMQs and any other allocated
- // state.
+ {
+ std::lock_guard<std::mutex> lockGuard(mEventQueueWriteMutex);
+ mPendingWritesRun = false;
+ mEventQueueWriteCV.notify_one();
+ }
+ if (mPendingWritesThread.joinable()) {
+ mPendingWritesThread.join();
+ }
+ // TODO: Cleanup wakeup thread once it is implemented
}
Return<void> HalProxy::getSensorsList(getSensorsList_cb _hidl_cb) {
@@ -120,7 +127,8 @@
result = Result::BAD_VALUE;
}
- // TODO: start threads to read wake locks and process events from sub HALs.
+ mPendingWritesThread = std::thread(startPendingWritesThread, this);
+ // TODO: start threads to read wake locks.
for (size_t i = 0; i < mSubHalList.size(); i++) {
auto subHal = mSubHalList[i];
@@ -162,25 +170,34 @@
return result;
}
-Return<void> HalProxy::registerDirectChannel(const SharedMemInfo& /* mem */,
+Return<void> HalProxy::registerDirectChannel(const SharedMemInfo& mem,
registerDirectChannel_cb _hidl_cb) {
- // TODO: During init, discover the first sub-HAL in the config that has sensors with direct
- // channel support, if any, and proxy the API call there.
- _hidl_cb(Result::INVALID_OPERATION, -1 /* channelHandle */);
+ if (mDirectChannelSubHal == nullptr) {
+ _hidl_cb(Result::INVALID_OPERATION, -1 /* channelHandle */);
+ } else {
+ mDirectChannelSubHal->registerDirectChannel(mem, _hidl_cb);
+ }
return Return<void>();
}
-Return<Result> HalProxy::unregisterDirectChannel(int32_t /* channelHandle */) {
- // TODO: During init, discover the first sub-HAL in the config that has sensors with direct
- // channel support, if any, and proxy the API call there.
- return Result::INVALID_OPERATION;
+Return<Result> HalProxy::unregisterDirectChannel(int32_t channelHandle) {
+ Result result;
+ if (mDirectChannelSubHal == nullptr) {
+ result = Result::INVALID_OPERATION;
+ } else {
+ result = mDirectChannelSubHal->unregisterDirectChannel(channelHandle);
+ }
+ return result;
}
-Return<void> HalProxy::configDirectReport(int32_t /* sensorHandle */, int32_t /* channelHandle */,
- RateLevel /* rate */, configDirectReport_cb _hidl_cb) {
- // TODO: During init, discover the first sub-HAL in the config that has sensors with direct
- // channel support, if any, and proxy the API call there.
- _hidl_cb(Result::INVALID_OPERATION, 0 /* reportToken */);
+Return<void> HalProxy::configDirectReport(int32_t sensorHandle, int32_t channelHandle,
+ RateLevel rate, configDirectReport_cb _hidl_cb) {
+ if (mDirectChannelSubHal == nullptr) {
+ _hidl_cb(Result::INVALID_OPERATION, -1 /* reportToken */);
+ } else {
+ mDirectChannelSubHal->configDirectReport(clearSubHalIndex(sensorHandle), channelHandle,
+ rate, _hidl_cb);
+ }
return Return<void>();
}
@@ -268,21 +285,66 @@
initializeSensorList();
}
+void HalProxy::startPendingWritesThread(HalProxy* halProxy) {
+ halProxy->handlePendingWrites();
+}
+
+void HalProxy::handlePendingWrites() {
+ // TODO: Find a way to optimize locking strategy maybe using two mutexes instead of one.
+ std::unique_lock<std::mutex> lock(mEventQueueWriteMutex);
+ while (mPendingWritesRun) {
+ mEventQueueWriteCV.wait(
+ lock, [&] { return !mPendingWriteEventsQueue.empty() || !mPendingWritesRun; });
+ if (!mPendingWriteEventsQueue.empty() && mPendingWritesRun) {
+ std::vector<Event>& pendingWriteEvents = mPendingWriteEventsQueue.front();
+ size_t eventQueueSize = mEventQueue->getQuantumCount();
+ size_t numToWrite = std::min(pendingWriteEvents.size(), eventQueueSize);
+ lock.unlock();
+ // TODO: Find a way to interrup writeBlocking if the thread should exit
+ // so we don't have to wait for timeout on framework restarts.
+ if (!mEventQueue->writeBlocking(
+ pendingWriteEvents.data(), numToWrite,
+ static_cast<uint32_t>(EventQueueFlagBits::EVENTS_READ),
+ static_cast<uint32_t>(EventQueueFlagBits::READ_AND_PROCESS),
+ kWakelockTimeoutNs, mEventQueueFlag)) {
+ ALOGE("Dropping %zu events after blockingWrite failed.", numToWrite);
+ } else {
+ mEventQueueFlag->wake(static_cast<uint32_t>(EventQueueFlagBits::READ_AND_PROCESS));
+ }
+ lock.lock();
+ if (pendingWriteEvents.size() > eventQueueSize) {
+ // TODO: Check if this erase operation is too inefficient. It will copy all the
+ // events ahead of it down to fill gap off array at front after the erase.
+ pendingWriteEvents.erase(pendingWriteEvents.begin(),
+ pendingWriteEvents.begin() + eventQueueSize);
+ } else {
+ mPendingWriteEventsQueue.pop();
+ }
+ }
+ }
+}
+
void HalProxy::postEventsToMessageQueue(const std::vector<Event>& events) {
- std::lock_guard<std::mutex> lock(mEventQueueMutex);
- size_t numToWrite = std::min(events.size(), mEventQueue->availableToWrite());
- if (numToWrite > 0) {
- if (mEventQueue->write(events.data(), numToWrite)) {
- // TODO: While loop if mEventQueue->avaiableToWrite > 0 to possibly fit in more writes
- // immediately
- mEventQueueFlag->wake(static_cast<uint32_t>(EventQueueFlagBits::READ_AND_PROCESS));
- } else {
- numToWrite = 0;
+ size_t numToWrite = 0;
+ std::lock_guard<std::mutex> lock(mEventQueueWriteMutex);
+ if (mPendingWriteEventsQueue.empty()) {
+ numToWrite = std::min(events.size(), mEventQueue->availableToWrite());
+ if (numToWrite > 0) {
+ if (mEventQueue->write(events.data(), numToWrite)) {
+ // TODO: While loop if mEventQueue->avaiableToWrite > 0 to possibly fit in more
+ // writes immediately
+ mEventQueueFlag->wake(static_cast<uint32_t>(EventQueueFlagBits::READ_AND_PROCESS));
+ } else {
+ numToWrite = 0;
+ }
}
}
if (numToWrite < events.size()) {
- // TODO: Post from events[numToWrite -> end] to background events queue
- // Signal background thread
+ // TODO: Bound the mPendingWriteEventsQueue so that we do not trigger OOMs if framework
+ // stalls
+ mPendingWriteEventsQueue.push(
+ std::vector<Event>(events.begin() + numToWrite, events.end()));
+ mEventQueueWriteCV.notify_one();
}
}
diff --git a/sensors/2.0/multihal/include/HalProxy.h b/sensors/2.0/multihal/include/HalProxy.h
index bdcc1ff..ae4b2c5 100644
--- a/sensors/2.0/multihal/include/HalProxy.h
+++ b/sensors/2.0/multihal/include/HalProxy.h
@@ -24,7 +24,12 @@
#include <hidl/MQDescriptor.h>
#include <hidl/Status.h>
+#include <atomic>
+#include <condition_variable>
#include <map>
+#include <mutex>
+#include <queue>
+#include <thread>
namespace android {
namespace hardware {
@@ -159,6 +164,7 @@
*/
std::vector<ISensorsSubHal*> mSubHalList;
+ //! The list of subhal callbacks for each subhal where the indices correlate with mSubHalList
std::vector<const sp<IHalProxyCallback>> mSubHalCallbacks;
/**
@@ -179,6 +185,9 @@
//! The mutex for the event queue.
std::mutex mEventQueueMutex;
+ //! The timeout for each pending write on background thread for events.
+ static const int64_t kWakelockTimeoutNs = 5 * INT64_C(1000000000) /* 5 seconds */;
+
//! The scoped wakelock ref count.
size_t mWakelockRefCount = 0;
@@ -188,6 +197,21 @@
//! The bit mask used to get the subhal index from a sensor handle.
static constexpr uint32_t kSensorHandleSubHalIndexMask = 0xFF000000;
+ //! The events that were not able to be written to fmq right away
+ std::queue<std::vector<Event>> mPendingWriteEventsQueue;
+
+ //! The mutex protecting writing to the fmq and the pending events queue
+ std::mutex mEventQueueWriteMutex;
+
+ //! The condition variable waiting on pending write events to stack up
+ std::condition_variable mEventQueueWriteCV;
+
+ //! The thread object ptr that handles pending writes
+ std::thread mPendingWritesThread;
+
+ //! The bool indicating whether to end the pending writes background thread or not
+ bool mPendingWritesRun = true;
+
/**
* Initialize the list of SubHal objects in mSubHalList by reading from dynamic libraries
* listed in a config file.
@@ -211,6 +235,16 @@
void initializeSubHalCallbacksAndSensorList();
/**
+ * Starts the thread that handles pending writes to event fmq.
+ *
+ * @param halProxy The HalProxy object pointer.
+ */
+ static void startPendingWritesThread(HalProxy* halProxy);
+
+ //! Handles the pending writes on events to eventqueue.
+ void handlePendingWrites();
+
+ /**
* Clear direct channel flags if the HalProxy has already chosen a subhal as its direct channel
* subhal. Set the directChannelSubHal pointer to the subHal passed in if this is the first
* direct channel enabled sensor seen.
diff --git a/sensors/2.0/multihal/tests/Android.bp b/sensors/2.0/multihal/tests/Android.bp
index ab260a4..aa44687 100644
--- a/sensors/2.0/multihal/tests/Android.bp
+++ b/sensors/2.0/multihal/tests/Android.bp
@@ -28,7 +28,6 @@
"libcutils",
"libfmq",
"libhidlbase",
- "libhidltransport",
"liblog",
"libpower",
"libutils",
@@ -83,7 +82,6 @@
"libcutils",
"libfmq",
"libhidlbase",
- "libhidltransport",
"liblog",
"libpower",
"libutils",
diff --git a/sensors/2.0/multihal/tests/HalProxy_test.cpp b/sensors/2.0/multihal/tests/HalProxy_test.cpp
index 4b1a15e..61fb14c 100644
--- a/sensors/2.0/multihal/tests/HalProxy_test.cpp
+++ b/sensors/2.0/multihal/tests/HalProxy_test.cpp
@@ -22,11 +22,10 @@
#include "ScopedWakelock.h"
#include "SensorsSubHal.h"
+#include <chrono>
+#include <thread>
#include <vector>
-#undef LOG_TAG
-#define LOG_TAG "HalProxy_test"
-
namespace {
using ::android::hardware::hidl_vec;
@@ -98,7 +97,7 @@
* Construct and return a HIDL Event type thats sensorHandle refers to a proximity sensor
* which is a wakeup type sensor.
*
- * @ return A proximity event.
+ * @return A proximity event.
*/
Event makeProximityEvent();
@@ -106,10 +105,30 @@
* Construct and return a HIDL Event type thats sensorHandle refers to a proximity sensor
* which is a wakeup type sensor.
*
- * @ return A proximity event.
+ * @return A proximity event.
*/
Event makeAccelerometerEvent();
+/**
+ * Make a certain number of proximity type events with the sensorHandle field set to
+ * the proper number for AllSensorsSubHal subhal type.
+ *
+ * @param numEvents The number of events to make.
+ *
+ * @return The created list of events.
+ */
+std::vector<Event> makeMultipleProximityEvents(size_t numEvents);
+
+/**
+ * Make a certain number of accelerometer type events with the sensorHandle field set to
+ * the proper number for AllSensorsSubHal subhal type.
+ *
+ * @param numEvents The number of events to make.
+ *
+ * @return The created list of events.
+ */
+std::vector<Event> makeMultipleAccelerometerEvents(size_t numEvents);
+
// Tests follow
TEST(HalProxyTest, GetSensorsListOneSubHalTest) {
AllSensorsSubHal subHal;
@@ -232,10 +251,7 @@
::android::sp<ISensorsCallback> callback = new SensorsCallback();
proxy.initialize(*eventQueue->getDesc(), *wakeLockQueue->getDesc(), callback);
- std::vector<Event> events;
- for (size_t i = 0; i < kNumEvents; i++) {
- events.push_back(makeAccelerometerEvent());
- }
+ std::vector<Event> events = makeMultipleAccelerometerEvents(kNumEvents);
subHal.postEvents(events, false /* wakeup */);
EXPECT_EQ(eventQueue->availableToRead(), kNumEvents);
@@ -272,15 +288,114 @@
::android::sp<ISensorsCallback> callback = new SensorsCallback();
proxy.initialize(*eventQueue->getDesc(), *wakeLockQueue->getDesc(), callback);
- std::vector<Event> events;
- for (size_t i = 0; i < kNumEvents; i++) {
- events.push_back(makeProximityEvent());
- }
+ std::vector<Event> events = makeMultipleProximityEvents(kNumEvents);
subHal.postEvents(events, true /* wakeup */);
EXPECT_EQ(eventQueue->availableToRead(), kNumEvents);
}
+TEST(HalProxyTest, PostEventsMultipleSubhals) {
+ constexpr size_t kQueueSize = 5;
+ constexpr size_t kNumEvents = 2;
+ AllSensorsSubHal subHal1, subHal2;
+ std::vector<ISensorsSubHal*> subHals{&subHal1, &subHal2};
+ HalProxy proxy(subHals);
+ std::unique_ptr<EventMessageQueue> eventQueue =
+ std::make_unique<EventMessageQueue>(kQueueSize, true);
+ std::unique_ptr<WakeupMessageQueue> wakeLockQueue =
+ std::make_unique<WakeupMessageQueue>(kQueueSize, true);
+ ::android::sp<ISensorsCallback> callback = new SensorsCallback();
+ proxy.initialize(*eventQueue->getDesc(), *wakeLockQueue->getDesc(), callback);
+
+ std::vector<Event> events = makeMultipleAccelerometerEvents(kNumEvents);
+ subHal1.postEvents(events, false /* wakeup */);
+
+ EXPECT_EQ(eventQueue->availableToRead(), kNumEvents);
+
+ subHal2.postEvents(events, false /* wakeup */);
+
+ EXPECT_EQ(eventQueue->availableToRead(), kNumEvents * 2);
+}
+
+TEST(HalProxyTest, PostEventsDelayedWrite) {
+ constexpr size_t kQueueSize = 5;
+ constexpr size_t kNumEvents = 6;
+ AllSensorsSubHal subHal1, subHal2;
+ std::vector<ISensorsSubHal*> subHals{&subHal1, &subHal2};
+ HalProxy proxy(subHals);
+ std::unique_ptr<EventMessageQueue> eventQueue =
+ std::make_unique<EventMessageQueue>(kQueueSize, true);
+ std::unique_ptr<WakeupMessageQueue> wakeLockQueue =
+ std::make_unique<WakeupMessageQueue>(kQueueSize, true);
+ ::android::sp<ISensorsCallback> callback = new SensorsCallback();
+ proxy.initialize(*eventQueue->getDesc(), *wakeLockQueue->getDesc(), callback);
+
+ std::vector<Event> events = makeMultipleAccelerometerEvents(kNumEvents);
+ subHal1.postEvents(events, false /* wakeup */);
+
+ EXPECT_EQ(eventQueue->availableToRead(), kQueueSize);
+
+ Event eventOut;
+ // writeblock 1 event out of queue, timeout for half a second
+ EXPECT_TRUE(eventQueue->readBlocking(&eventOut, 1, 500000000));
+
+ // Sleep for a half second so that blocking write has time complete in background thread
+ std::this_thread::sleep_for(std::chrono::milliseconds(500));
+
+ // proxy background thread should have wrote last event when it saw space
+ EXPECT_EQ(eventQueue->availableToRead(), kQueueSize);
+}
+
+TEST(HalProxyTest, PostEventsMultipleSubhalsThreaded) {
+ constexpr size_t kQueueSize = 5;
+ constexpr size_t kNumEvents = 2;
+ AllSensorsSubHal subHal1, subHal2;
+ std::vector<ISensorsSubHal*> subHals{&subHal1, &subHal2};
+ HalProxy proxy(subHals);
+ std::unique_ptr<EventMessageQueue> eventQueue =
+ std::make_unique<EventMessageQueue>(kQueueSize, true);
+ std::unique_ptr<WakeupMessageQueue> wakeLockQueue =
+ std::make_unique<WakeupMessageQueue>(kQueueSize, true);
+ ::android::sp<ISensorsCallback> callback = new SensorsCallback();
+ proxy.initialize(*eventQueue->getDesc(), *wakeLockQueue->getDesc(), callback);
+
+ std::vector<Event> events = makeMultipleAccelerometerEvents(kNumEvents);
+
+ std::thread t1(&AllSensorsSubHal::postEvents, &subHal1, events, false);
+ std::thread t2(&AllSensorsSubHal::postEvents, &subHal2, events, false);
+
+ t1.join();
+ t2.join();
+
+ EXPECT_EQ(eventQueue->availableToRead(), kNumEvents * 2);
+}
+
+TEST(HalProxyTest, DestructingWithEventsPendingOnBackgroundThreadTest) {
+ constexpr size_t kQueueSize = 5;
+ constexpr size_t kNumEvents = 6;
+ AllSensorsSubHal subHal;
+ std::vector<ISensorsSubHal*> subHals{&subHal};
+
+ std::unique_ptr<EventMessageQueue> eventQueue =
+ std::make_unique<EventMessageQueue>(kQueueSize, true);
+ std::unique_ptr<WakeupMessageQueue> wakeLockQueue =
+ std::make_unique<WakeupMessageQueue>(kQueueSize, true);
+ ::android::sp<ISensorsCallback> callback = new SensorsCallback();
+ HalProxy proxy(subHals);
+ proxy.initialize(*eventQueue->getDesc(), *wakeLockQueue->getDesc(), callback);
+
+ std::vector<Event> events = makeMultipleAccelerometerEvents(kNumEvents);
+ subHal.postEvents(events, false /* wakeup */);
+
+ // Sleep for a half second so that background thread has time to attempt it's blocking write
+ std::this_thread::sleep_for(std::chrono::milliseconds(500));
+
+ // Should see a 5 second wait for blocking write timeout here
+
+ // Should be one events left on pending writes queue here and proxy will destruct
+ // If this TEST completes then it was a success, if it hangs we will see a crash
+}
+
// Helper implementations follow
void testSensorsListFromProxyAndSubHal(const std::vector<SensorInfo>& proxySensorsList,
const std::vector<SensorInfo>& subHalSensorsList) {
@@ -332,4 +447,20 @@
return event;
}
+std::vector<Event> makeMultipleProximityEvents(size_t numEvents) {
+ std::vector<Event> events;
+ for (size_t i = 0; i < numEvents; i++) {
+ events.push_back(makeProximityEvent());
+ }
+ return events;
+}
+
+std::vector<Event> makeMultipleAccelerometerEvents(size_t numEvents) {
+ std::vector<Event> events;
+ for (size_t i = 0; i < numEvents; i++) {
+ events.push_back(makeAccelerometerEvent());
+ }
+ return events;
+}
+
} // namespace
diff --git a/tv/tuner/1.0/IDemux.hal b/tv/tuner/1.0/IDemux.hal
index e03095b..7fd7e26 100644
--- a/tv/tuner/1.0/IDemux.hal
+++ b/tv/tuner/1.0/IDemux.hal
@@ -1,3 +1,19 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
package android.hardware.tv.tuner@1.0;
import IDemuxCallback;
@@ -46,9 +62,9 @@
*
* It is used by the client to get the descriptor of the filter's Fast
* Message Queue. The data in FMQ is filtered out from MPEG transport
- * stream. The data is origanized to data blocks which may have
+ * stream. The data is organized to data blocks which may have
* different length. The length's information of one or multiple data blocks
- * is sent to client throught DemuxFilterEvent.
+ * is sent to client through DemuxFilterEvent.
*
* @param filterId the ID of the filter.
* @return result Result status of the operation.
@@ -81,7 +97,7 @@
/**
* Start the filter.
*
- * It is used by the client to ask the filter to start filterring data.
+ * It is used by the client to ask the filter to start filtering data.
*
* @param filterId the ID of the filter.
* @return result Result status of the operation.
@@ -202,7 +218,7 @@
*
* It is used by the client to get the descriptor of the output's Fast
* Message Queue. The data in FMQ is muxed packets output from selected
- * filters. The packet's format is specifed by DemuxDataFormat in
+ * filters. The packet's format is specified by DemuxDataFormat in
* DemuxOutputSettings.
*
* @return result Result status of the operation.
@@ -236,7 +252,7 @@
* INVALID_STATE if failed for wrong state.
* UNKNOWN_ERROR if failed for other reasons.
*/
- attachOutputTsFilter(DemuxFilterId filterId) generates (Result result);
+ attachOutputFilter(DemuxFilterId filterId) generates (Result result);
/**
* Detach one filter from the demux's output.
@@ -250,7 +266,7 @@
* INVALID_STATE if failed for wrong state.
* UNKNOWN_ERROR if failed for other reasons.
*/
- detachOutputTsFilter(DemuxFilterId filterId) generates (Result result);
+ detachOutputFilter(DemuxFilterId filterId) generates (Result result);
/**
* Start to take data to the demux's output.
diff --git a/tv/tuner/1.0/IDemuxCallback.hal b/tv/tuner/1.0/IDemuxCallback.hal
index 55e8420..7bce9ef 100644
--- a/tv/tuner/1.0/IDemuxCallback.hal
+++ b/tv/tuner/1.0/IDemuxCallback.hal
@@ -1,3 +1,19 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
package android.hardware.tv.tuner@1.0;
interface IDemuxCallback {
diff --git a/tv/tuner/1.0/IDescrambler.hal b/tv/tuner/1.0/IDescrambler.hal
index d078657..61ff1df 100644
--- a/tv/tuner/1.0/IDescrambler.hal
+++ b/tv/tuner/1.0/IDescrambler.hal
@@ -1,3 +1,19 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
package android.hardware.tv.tuner@1.0;
/**
* Descrambler is used to descramble input data.
diff --git a/tv/tuner/1.0/IFrontend.hal b/tv/tuner/1.0/IFrontend.hal
index 8788643..83e390d 100644
--- a/tv/tuner/1.0/IFrontend.hal
+++ b/tv/tuner/1.0/IFrontend.hal
@@ -145,10 +145,10 @@
* cable frontend.
* UNKNOWN_ERROR if failed for other reasons.
*/
- setLnb(ILnb lnb) generates (Result result);
+ setLnb(LnbId lnbId) generates (Result result);
/**
- * Enble or Disable Low Noise Amplifier (LNA).
+ * Enable or Disable Low Noise Amplifier (LNA).
*
* @param bEnable true if activate LNA module; false if deactivate LNA
*
@@ -158,22 +158,4 @@
* UNKNOWN_ERROR if failed for other reasons.
*/
setLna(bool bEnable) generates (Result result);
-
- /**
- * Sends DiSEqC (Digital Satellite Equipment Control) message.
- *
- * Client sends DiSeqc message to DiSEqc compatible device through the
- * frontend. The response message from the device comes back to the client
- * through frontend's callback onDiseqcMessage.
- *
- * @param diseqcMessage a byte array of data for DiSEqC message which is
- * specified by EUTELSAT Bus Functional Specification Version 4.2.
- *
- * @return result Result status of the operation.
- * SUCCESS if successful,
- * INVALID_STATE if the frontend can't send DiSEqc Message, such as
- * cable frontend.
- * UNKNOWN_ERROR if failed for other reasons.
- */
- sendDiseqcMessage(vec<uint8_t> diseqcMessage) generates (Result result);
};
diff --git a/tv/tuner/1.0/ILnb.hal b/tv/tuner/1.0/ILnb.hal
index 49fc3b4..6b7119e 100644
--- a/tv/tuner/1.0/ILnb.hal
+++ b/tv/tuner/1.0/ILnb.hal
@@ -55,6 +55,24 @@
setSatellitePosition(FrontendLnbPosition position) generates (Result result);
/**
+ * Sends DiSEqC (Digital Satellite Equipment Control) message.
+ *
+ * Client sends DiSeqc message to DiSEqc to LNB. The response message from
+ * the device comes back to the client through frontend's callback
+ * onDiseqcMessage.
+ *
+ * @param diseqcMessage a byte array of data for DiSEqC message which is
+ * specified by EUTELSAT Bus Functional Specification Version 4.2.
+ *
+ * @return result Result status of the operation.
+ * SUCCESS if successful,
+ * INVALID_STATE if the frontend can't send DiSEqc Message, such as
+ * cable frontend.
+ * UNKNOWN_ERROR if failed for other reasons.
+ */
+ sendDiseqcMessage(vec<uint8_t> diseqcMessage) generates (Result result);
+
+ /**
* Releases the LNB instance
*
* Associated resources are released. close may be called more than once.
diff --git a/tv/tuner/1.0/ITuner.hal b/tv/tuner/1.0/ITuner.hal
index f1a8617..1cf0e38 100644
--- a/tv/tuner/1.0/ITuner.hal
+++ b/tv/tuner/1.0/ITuner.hal
@@ -23,7 +23,7 @@
/**
* Top level interface to manage Frontend, Demux and Decrambler hardware
- * resouces which are needed for Android TV.
+ * resources which are needed for Android TV.
*/
interface ITuner {
/**
@@ -68,6 +68,16 @@
generates (Result result, DemuxId demuxId, IDemux demux);
/**
+ * Retrieve the Demux's Capabilities.
+ *
+ * @return result Result status of the operation.
+ * SUCCESS if successful,
+ * UNKNOWN_ERROR if the inquiry failed for other reasons.
+ * @return caps the Demux's Capabilities.
+ */
+ getDemuxCaps() generates (Result result, DemuxCapabilities caps);
+
+ /**
* Create a new instance of Descrambler.
*
* It is used by the client to create a Descrambler instance.
@@ -81,14 +91,13 @@
generates (Result result, IDescrambler descrambler);
/**
- * Create a new instance of Descrambler.
+ * Retrieve the frontend's information.
*
- * It is used by the client to create a Descrambler instance.
- *
+ * @param frontendId the id of the frontend to be inquiried.
* @return result Result status of the operation.
* SUCCESS if successful,
- * UNKNOWN_ERROR if creation failed for other reasons.
- * @return descrambler the newly created descrambler interface.
+ * UNKNOWN_ERROR if the inquiry failed for other reasons.
+ * @return info the frontend's information.
*/
getFrontendInfo(FrontendId frontendId)
generates (Result result, FrontendInfo info);
@@ -119,6 +128,5 @@
*/
openLnbById(LnbId lnbId)
generates (Result result, ILnb lnb);
-
};
diff --git a/tv/tuner/1.0/default/Demux.cpp b/tv/tuner/1.0/default/Demux.cpp
index 889e42e..d65df59 100644
--- a/tv/tuner/1.0/default/Demux.cpp
+++ b/tv/tuner/1.0/default/Demux.cpp
@@ -67,8 +67,9 @@
0x73, 0x63, 0x65, 0x6e, 0x65,
};
-Demux::Demux(uint32_t demuxId) {
+Demux::Demux(uint32_t demuxId, sp<Tuner> tuner) {
mDemuxId = demuxId;
+ mTunerService = tuner;
}
Demux::~Demux() {}
@@ -76,9 +77,20 @@
Return<Result> Demux::setFrontendDataSource(uint32_t frontendId) {
ALOGV("%s", __FUNCTION__);
- mSourceFrontendId = frontendId;
+ if (mTunerService == nullptr) {
+ return Result::NOT_INITIALIZED;
+ }
- return Result::SUCCESS;
+ mFrontend = mTunerService->getFrontendById(frontendId);
+
+ if (mFrontend == nullptr) {
+ return Result::INVALID_STATE;
+ }
+
+ mFrontendSourceFile = mFrontend->getSourceFile();
+
+ mTunerService->setFrontendAsDemuxSource(frontendId, mDemuxId);
+ return startBroadcastInputLoop();
}
Return<void> Demux::addFilter(DemuxFilterType type, uint32_t bufferSize,
@@ -100,6 +112,8 @@
mFilterEventFlags.resize(filterId + 1);
mFilterThreadRunning.resize(filterId + 1);
mFilterThreads.resize(filterId + 1);
+ mFilterPids.resize(filterId + 1);
+ mFilterOutputs.resize(filterId + 1);
}
mUsedFilterIds.insert(filterId);
@@ -142,10 +156,34 @@
return Void();
}
-Return<Result> Demux::configureFilter(uint32_t /* filterId */,
- const DemuxFilterSettings& /* settings */) {
+Return<Result> Demux::configureFilter(uint32_t filterId, const DemuxFilterSettings& settings) {
ALOGV("%s", __FUNCTION__);
+ switch (mFilterEvents[filterId].filterType) {
+ case DemuxFilterType::SECTION:
+ mFilterPids[filterId] = settings.section().tpid;
+ break;
+ case DemuxFilterType::PES:
+ mFilterPids[filterId] = settings.pesData().tpid;
+ break;
+ case DemuxFilterType::TS:
+ mFilterPids[filterId] = settings.ts().tpid;
+ break;
+ case DemuxFilterType::AUDIO:
+ mFilterPids[filterId] = settings.audio().tpid;
+ break;
+ case DemuxFilterType::VIDEO:
+ mFilterPids[filterId] = settings.video().tpid;
+ break;
+ case DemuxFilterType::RECORD:
+ mFilterPids[filterId] = settings.record().tpid;
+ break;
+ case DemuxFilterType::PCR:
+ mFilterPids[filterId] = settings.pcr().tpid;
+ break;
+ default:
+ return Result::UNKNOWN_ERROR;
+ }
return Result::SUCCESS;
}
@@ -158,36 +196,18 @@
return Result::INVALID_ARGUMENT;
}
- switch (mFilterEvents[filterId].filterType) {
- case DemuxFilterType::SECTION:
- result = startFilterLoop(filterId);
- break;
- case DemuxFilterType::PES:
- result = startPesFilterHandler(filterId);
- break;
- case DemuxFilterType::TS:
- result = startTsFilterHandler();
- return Result::SUCCESS;
- case DemuxFilterType::AUDIO:
- case DemuxFilterType::VIDEO:
- result = startMediaFilterHandler(filterId);
- break;
- case DemuxFilterType::RECORD:
- result = startRecordFilterHandler(filterId);
- break;
- case DemuxFilterType::PCR:
- result = startPcrFilterHandler();
- return Result::SUCCESS;
- default:
- return Result::UNKNOWN_ERROR;
- }
+ result = startFilterLoop(filterId);
return result;
}
-Return<Result> Demux::stopFilter(uint32_t /* filterId */) {
+Return<Result> Demux::stopFilter(uint32_t filterId) {
ALOGV("%s", __FUNCTION__);
+ mFilterThreadRunning[filterId] = false;
+
+ std::lock_guard<std::mutex> lock(mFilterThreadLock);
+
return Result::SUCCESS;
}
@@ -238,6 +258,8 @@
mFilterMQs.clear();
mFilterEvents.clear();
mFilterEventFlags.clear();
+ mFilterOutputs.clear();
+ mFilterPids.clear();
mLastUsedFilterId = -1;
return Result::SUCCESS;
@@ -277,19 +299,21 @@
return Void();
}
-Return<Result> Demux::configureOutput(const DemuxOutputSettings& /* settings */) {
+Return<Result> Demux::configureOutput(const DemuxOutputSettings& settings) {
+ ALOGV("%s", __FUNCTION__);
+
+ mOutputConfigured = true;
+ mOutputSettings = settings;
+ return Result::SUCCESS;
+}
+
+Return<Result> Demux::attachOutputFilter(uint32_t /*filterId*/) {
ALOGV("%s", __FUNCTION__);
return Result::SUCCESS;
}
-Return<Result> Demux::attachOutputTsFilter(uint32_t /*filterId*/) {
- ALOGV("%s", __FUNCTION__);
-
- return Result::SUCCESS;
-}
-
-Return<Result> Demux::detachOutputTsFilter(uint32_t /* filterId */) {
+Return<Result> Demux::detachOutputFilter(uint32_t /* filterId */) {
ALOGV("%s", __FUNCTION__);
return Result::SUCCESS;
@@ -353,15 +377,26 @@
return Void();
}
-Return<Result> Demux::configureInput(const DemuxInputSettings& /* settings */) {
+Return<Result> Demux::configureInput(const DemuxInputSettings& settings) {
ALOGV("%s", __FUNCTION__);
+ mInputConfigured = true;
+ mInputSettings = settings;
+
return Result::SUCCESS;
}
Return<Result> Demux::startInput() {
ALOGV("%s", __FUNCTION__);
+ if (!mInputCallback) {
+ return Result::NOT_INITIALIZED;
+ }
+
+ if (!mInputConfigured) {
+ return Result::INVALID_STATE;
+ }
+
pthread_create(&mInputThread, NULL, __threadLoopInput, this);
pthread_setname_np(mInputThread, "demux_input_waiting_loop");
@@ -373,6 +408,10 @@
Return<Result> Demux::stopInput() {
ALOGV("%s", __FUNCTION__);
+ mInputThreadRunning = false;
+
+ std::lock_guard<std::mutex> lock(mInputThreadLock);
+
return Result::SUCCESS;
}
@@ -403,36 +442,52 @@
return Result::SUCCESS;
}
-Result Demux::startSectionFilterHandler(uint32_t filterId, vector<uint8_t> data) {
- if (!writeSectionsAndCreateEvent(filterId, data)) {
+Result Demux::startSectionFilterHandler(uint32_t filterId) {
+ if (mFilterOutputs[filterId].empty()) {
+ return Result::SUCCESS;
+ }
+ if (!writeSectionsAndCreateEvent(filterId, mFilterOutputs[filterId])) {
ALOGD("[Demux] filter %d fails to write into FMQ. Ending thread", filterId);
return Result::UNKNOWN_ERROR;
}
+ mFilterOutputs[filterId].clear();
+
return Result::SUCCESS;
}
Result Demux::startPesFilterHandler(uint32_t filterId) {
- // TODO generate multiple events in one event callback
+ std::lock_guard<std::mutex> lock(mFilterEventLock);
DemuxFilterPesEvent pesEvent;
- pesEvent = {
- // temp dump meta data
- .streamId = 0,
- .dataLength = 530,
- };
- mFilterEvents[filterId].events.resize(1);
- mFilterEvents[filterId].events[0].pes(pesEvent);
- /*pthread_create(&mThreadId, NULL, __threadLoop, this);
- pthread_setname_np(mThreadId, "demux_section_filter_waiting_loop");*/
- if (!writeDataToFilterMQ(fakeDataInputBuffer, filterId)) {
- return Result::INVALID_STATE;
+ if (mFilterOutputs[filterId].empty()) {
+ return Result::SUCCESS;
}
- if (mDemuxCallbacks[filterId] == nullptr) {
- return Result::NOT_INITIALIZED;
+ for (int i = 0; i < mFilterOutputs[filterId].size(); i += 188) {
+ uint8_t pusi = mFilterOutputs[filterId][i + 1] & 0x40;
+ uint8_t adaptFieldControl = (mFilterOutputs[filterId][i + 3] & 0x30) >> 4;
+ ALOGD("[Demux] pusi %d, adaptFieldControl %d", pusi, adaptFieldControl);
+ if (pusi && (adaptFieldControl == 0x01)) {
+ vector<uint8_t>::const_iterator first = mFilterOutputs[filterId].begin() + i + 4;
+ vector<uint8_t>::const_iterator last = mFilterOutputs[filterId].begin() + i + 187;
+ vector<uint8_t> filterOutData(first, last);
+ if (!writeDataToFilterMQ(filterOutData, filterId)) {
+ mFilterOutputs[filterId].clear();
+ return Result::INVALID_STATE;
+ }
+ pesEvent = {
+ // temp dump meta data
+ .streamId = filterOutData[3],
+ .dataLength = static_cast<uint16_t>(filterOutData.size()),
+ };
+ int size = mFilterEvents[filterId].events.size();
+ mFilterEvents[filterId].events.resize(size + 1);
+ mFilterEvents[filterId].events[size].pes(pesEvent);
+ }
}
- mDemuxCallbacks[filterId]->onFilterEvent(mFilterEvents[filterId]);
+ mFilterOutputs[filterId].clear();
+
return Result::SUCCESS;
}
@@ -451,6 +506,8 @@
};
mFilterEvents[filterId].events.resize(1);
mFilterEvents[filterId].events[0].media() = mediaEvent;
+
+ mFilterOutputs[filterId].clear();
// TODO handle write FQM for media stream
return Result::SUCCESS;
}
@@ -465,6 +522,8 @@
recordEvent.indexMask.tsIndexMask() = 0x01;
mFilterEvents[filterId].events.resize(1);
mFilterEvents[filterId].events[0].ts() = recordEvent;
+
+ mFilterOutputs[filterId].clear();
return Result::SUCCESS;
}
@@ -499,18 +558,18 @@
bool Demux::writeSectionsAndCreateEvent(uint32_t filterId, vector<uint8_t> data) {
// TODO check how many sections has been read
std::lock_guard<std::mutex> lock(mFilterEventLock);
- int size = mFilterEvents[filterId].events.size();
- mFilterEvents[filterId].events.resize(size + 1);
if (!writeDataToFilterMQ(data, filterId)) {
return false;
}
+ int size = mFilterEvents[filterId].events.size();
+ mFilterEvents[filterId].events.resize(size + 1);
DemuxFilterSectionEvent secEvent;
secEvent = {
// temp dump meta data
.tableId = 0,
.version = 1,
.sectionNum = 1,
- .dataLength = 530,
+ .dataLength = static_cast<uint16_t>(data.size()),
};
mFilterEvents[filterId].events[size].section(secEvent);
return true;
@@ -524,21 +583,44 @@
return false;
}
-bool Demux::filterAndOutputData() {
- ALOGD("[Demux] start to dispatch data to filters");
+bool Demux::readInputFMQ() {
// Read input data from the input FMQ
int size = mInputMQ->availableToRead();
+ int inputPacketSize = mInputSettings.packetSize;
vector<uint8_t> dataOutputBuffer;
- dataOutputBuffer.resize(size);
- mInputMQ->read(dataOutputBuffer.data(), size);
+ dataOutputBuffer.resize(inputPacketSize);
- Result result;
- // Filter the data and feed the output to each filter
+ // Dispatch the packet to the PID matching filter output buffer
+ for (int i = 0; i < size / inputPacketSize; i++) {
+ if (!mInputMQ->read(dataOutputBuffer.data(), inputPacketSize)) {
+ return false;
+ }
+ startTsFilter(dataOutputBuffer);
+ }
+
+ return true;
+}
+
+void Demux::startTsFilter(vector<uint8_t> data) {
set<uint32_t>::iterator it;
for (it = mUsedFilterIds.begin(); it != mUsedFilterIds.end(); it++) {
+ uint16_t pid = ((data[1] & 0x1f) << 8) | ((data[2] & 0xff));
+ ALOGW("start ts filter pid: %d", pid);
+ if (pid == mFilterPids[*it]) {
+ mFilterOutputs[*it].insert(mFilterOutputs[*it].end(), data.begin(), data.end());
+ }
+ }
+}
+
+bool Demux::startFilterDispatcher() {
+ Result result;
+ set<uint32_t>::iterator it;
+
+ // Handle the output data per filter type
+ for (it = mUsedFilterIds.begin(); it != mUsedFilterIds.end(); it++) {
switch (mFilterEvents[*it].filterType) {
case DemuxFilterType::SECTION:
- result = startSectionFilterHandler(*it, dataOutputBuffer);
+ result = startSectionFilterHandler(*it);
break;
case DemuxFilterType::PES:
result = startPesFilterHandler(*it);
@@ -578,6 +660,7 @@
void Demux::filterThreadLoop(uint32_t filterId) {
ALOGD("[Demux] filter %d threadLoop start.", filterId);
+ std::lock_guard<std::mutex> lock(mFilterThreadLock);
mFilterThreadRunning[filterId] = true;
// For the first time of filter output, implementation needs to send the filter
@@ -640,6 +723,7 @@
void Demux::inputThreadLoop() {
ALOGD("[Demux] input threadLoop start.");
+ std::lock_guard<std::mutex> lock(mInputThreadLock);
mInputThreadRunning = true;
while (mInputThreadRunning) {
@@ -651,18 +735,112 @@
ALOGD("[Demux] wait for data ready on the input FMQ");
continue;
}
- // Our current implementation filter the data and write it into the filter FMQ immedaitely
+ // Our current implementation filter the data and write it into the filter FMQ immediately
// after the DATA_READY from the VTS/framework
- if (!filterAndOutputData()) {
+ if (!readInputFMQ() || !startFilterDispatcher()) {
ALOGD("[Demux] input data failed to be filtered. Ending thread");
break;
}
+
+ maySendInputStatusCallback();
}
mInputThreadRunning = false;
ALOGD("[Demux] input thread ended.");
}
+void Demux::maySendInputStatusCallback() {
+ std::lock_guard<std::mutex> lock(mInputStatusLock);
+ int availableToRead = mInputMQ->availableToRead();
+ int availableToWrite = mInputMQ->availableToWrite();
+
+ DemuxInputStatus newStatus =
+ checkStatusChange(availableToWrite, availableToRead, mInputSettings.highThreshold,
+ mInputSettings.lowThreshold);
+ if (mIntputStatus != newStatus) {
+ mInputCallback->onInputStatus(newStatus);
+ mIntputStatus = newStatus;
+ }
+}
+
+DemuxInputStatus Demux::checkStatusChange(uint32_t availableToWrite, uint32_t availableToRead,
+ uint32_t highThreshold, uint32_t lowThreshold) {
+ if (availableToWrite == 0) {
+ return DemuxInputStatus::SPACE_FULL;
+ } else if (availableToRead > highThreshold) {
+ return DemuxInputStatus::SPACE_ALMOST_FULL;
+ } else if (availableToRead < lowThreshold) {
+ return DemuxInputStatus::SPACE_ALMOST_EMPTY;
+ } else if (availableToRead == 0) {
+ return DemuxInputStatus::SPACE_EMPTY;
+ }
+ return mIntputStatus;
+}
+
+Result Demux::startBroadcastInputLoop() {
+ pthread_create(&mBroadcastInputThread, NULL, __threadLoopBroadcast, this);
+ pthread_setname_np(mBroadcastInputThread, "broadcast_input_thread");
+
+ return Result::SUCCESS;
+}
+
+void* Demux::__threadLoopBroadcast(void* user) {
+ Demux* const self = static_cast<Demux*>(user);
+ self->broadcastInputThreadLoop();
+ return 0;
+}
+
+void Demux::broadcastInputThreadLoop() {
+ std::lock_guard<std::mutex> lock(mBroadcastInputThreadLock);
+ mBroadcastInputThreadRunning = true;
+ mKeepFetchingDataFromFrontend = true;
+
+ // open the stream and get its length
+ std::ifstream inputData(mFrontendSourceFile, std::ifstream::binary);
+ // TODO take the packet size from the frontend setting
+ int packetSize = 188;
+ int writePacketAmount = 6;
+ char* buffer = new char[packetSize];
+ ALOGW("[Demux] broadcast input thread loop start %s", mFrontendSourceFile.c_str());
+ if (!inputData.is_open()) {
+ mBroadcastInputThreadRunning = false;
+ ALOGW("[Demux] Error %s", strerror(errno));
+ }
+
+ while (mBroadcastInputThreadRunning) {
+ // move the stream pointer for packet size * 6 every read until the end
+ while (mKeepFetchingDataFromFrontend) {
+ for (int i = 0; i < writePacketAmount; i++) {
+ inputData.read(buffer, packetSize);
+ if (!inputData) {
+ mBroadcastInputThreadRunning = false;
+ break;
+ }
+ // filter and dispatch filter output
+ vector<uint8_t> byteBuffer;
+ byteBuffer.resize(sizeof(buffer));
+ for (int index = 0; index < byteBuffer.size(); index++) {
+ byteBuffer[index] = static_cast<uint8_t>(buffer[index]);
+ }
+ startTsFilter(byteBuffer);
+ inputData.seekg(packetSize, inputData.cur);
+ }
+ startFilterDispatcher();
+ sleep(1);
+ }
+ }
+
+ ALOGW("[Demux] Broadcast Input thread end.");
+ delete[] buffer;
+ inputData.close();
+}
+
+void Demux::stopBroadcastInput() {
+ mKeepFetchingDataFromFrontend = false;
+ mBroadcastInputThreadRunning = false;
+ std::lock_guard<std::mutex> lock(mBroadcastInputThreadLock);
+}
+
} // namespace implementation
} // namespace V1_0
} // namespace tuner
diff --git a/tv/tuner/1.0/default/Demux.h b/tv/tuner/1.0/default/Demux.h
index 2fdde8d..e4a4e2b 100644
--- a/tv/tuner/1.0/default/Demux.h
+++ b/tv/tuner/1.0/default/Demux.h
@@ -20,6 +20,8 @@
#include <android/hardware/tv/tuner/1.0/IDemux.h>
#include <fmq/MessageQueue.h>
#include <set>
+#include "Frontend.h"
+#include "Tuner.h"
using namespace std;
@@ -40,9 +42,12 @@
using FilterMQ = MessageQueue<uint8_t, kSynchronizedReadWrite>;
+class Tuner;
+class Frontend;
+
class Demux : public IDemux {
public:
- Demux(uint32_t demuxId);
+ Demux(uint32_t demuxId, sp<Tuner> tuner);
~Demux();
@@ -91,9 +96,9 @@
virtual Return<Result> configureOutput(const DemuxOutputSettings& settings) override;
- virtual Return<Result> attachOutputTsFilter(uint32_t filterId) override;
+ virtual Return<Result> attachOutputFilter(uint32_t filterId) override;
- virtual Return<Result> detachOutputTsFilter(uint32_t filterId) override;
+ virtual Return<Result> detachOutputFilter(uint32_t filterId) override;
virtual Return<Result> startOutput() override;
@@ -103,7 +108,17 @@
virtual Return<Result> removeOutput() override;
+ // Functions interacts with Tuner Service
+ void stopBroadcastInput();
+
private:
+ // Tuner service
+ sp<Tuner> mTunerService;
+
+ // Frontend source
+ sp<Frontend> mFrontend;
+ string mFrontendSourceFile;
+
// A struct that passes the arguments to a newly created filter thread
struct ThreadArgs {
Demux* user;
@@ -115,13 +130,14 @@
* They are also responsible to write the filtered output into the filter FMQ
* and update the filterEvent bound with the same filterId.
*/
- Result startSectionFilterHandler(uint32_t filterId, vector<uint8_t> data);
+ Result startSectionFilterHandler(uint32_t filterId);
Result startPesFilterHandler(uint32_t filterId);
Result startTsFilterHandler();
Result startMediaFilterHandler(uint32_t filterId);
Result startRecordFilterHandler(uint32_t filterId);
Result startPcrFilterHandler();
Result startFilterLoop(uint32_t filterId);
+ Result startBroadcastInputLoop();
/**
* To create a FilterMQ with the the next available Filter ID.
@@ -136,18 +152,24 @@
bool writeDataToFilterMQ(const std::vector<uint8_t>& data, uint32_t filterId);
bool readDataFromMQ();
bool writeSectionsAndCreateEvent(uint32_t filterId, vector<uint8_t> data);
+ void maySendInputStatusCallback();
+ DemuxInputStatus checkStatusChange(uint32_t availableToWrite, uint32_t availableToRead,
+ uint32_t highThreshold, uint32_t lowThreshold);
/**
* A dispatcher to read and dispatch input data to all the started filters.
* Each filter handler handles the data filtering/output writing/filterEvent updating.
*/
- bool filterAndOutputData();
+ bool readInputFMQ();
+ void startTsFilter(vector<uint8_t> data);
+ bool startFilterDispatcher();
static void* __threadLoopFilter(void* data);
static void* __threadLoopInput(void* user);
+ static void* __threadLoopBroadcast(void* user);
void filterThreadLoop(uint32_t filterId);
void inputThreadLoop();
+ void broadcastInputThreadLoop();
uint32_t mDemuxId;
- uint32_t mSourceFrontendId;
/**
* Record the last used filter id. Initial value is -1.
* Filter Id starts with 0.
@@ -169,6 +191,8 @@
* A list of created FilterMQ ptrs.
* The array number is the filter ID.
*/
+ vector<uint16_t> mFilterPids;
+ vector<vector<uint8_t>> mFilterOutputs;
vector<unique_ptr<FilterMQ>> mFilterMQs;
vector<EventFlag*> mFilterEventFlags;
vector<DemuxFilterEvent> mFilterEvents;
@@ -182,15 +206,26 @@
vector<sp<IDemuxCallback>> mDemuxCallbacks;
sp<IDemuxCallback> mInputCallback;
sp<IDemuxCallback> mOutputCallback;
+ bool mInputConfigured = false;
+ bool mOutputConfigured = false;
+ DemuxInputSettings mInputSettings;
+ DemuxOutputSettings mOutputSettings;
+
// Thread handlers
pthread_t mInputThread;
pthread_t mOutputThread;
+ pthread_t mBroadcastInputThread;
vector<pthread_t> mFilterThreads;
+
+ // FMQ status local records
+ DemuxInputStatus mIntputStatus;
/**
* If a specific filter's writing loop is still running
*/
vector<bool> mFilterThreadRunning;
bool mInputThreadRunning;
+ bool mBroadcastInputThreadRunning;
+ bool mKeepFetchingDataFromFrontend;
/**
* Lock to protect writes to the FMQs
*/
@@ -198,8 +233,16 @@
/**
* Lock to protect writes to the filter event
*/
+ // TODO make each filter separate event lock
std::mutex mFilterEventLock;
/**
+ * Lock to protect writes to the input status
+ */
+ std::mutex mInputStatusLock;
+ std::mutex mBroadcastInputThreadLock;
+ std::mutex mFilterThreadLock;
+ std::mutex mInputThreadLock;
+ /**
* How many times a filter should write
* TODO make this dynamic/random/can take as a parameter
*/
diff --git a/tv/tuner/1.0/default/Frontend.cpp b/tv/tuner/1.0/default/Frontend.cpp
index 0609d05..1e07edd 100644
--- a/tv/tuner/1.0/default/Frontend.cpp
+++ b/tv/tuner/1.0/default/Frontend.cpp
@@ -27,14 +27,10 @@
namespace V1_0 {
namespace implementation {
-Frontend::Frontend() {
- // Init callback to nullptr
- mCallback = nullptr;
-}
-
-Frontend::Frontend(FrontendType type, FrontendId id) {
+Frontend::Frontend(FrontendType type, FrontendId id, sp<Tuner> tuner) {
mType = type;
mId = id;
+ mTunerService = tuner;
// Init callback to nullptr
mCallback = nullptr;
}
@@ -67,13 +63,18 @@
return Result::INVALID_STATE;
}
- mCallback->onEvent(FrontendEventType::NO_SIGNAL);
+ // TODO dynamically allocate file to the source file
+ mSourceStreamFile = FRONTEND_STREAM_FILE;
+
+ mCallback->onEvent(FrontendEventType::LOCKED);
return Result::SUCCESS;
}
Return<Result> Frontend::stopTune() {
ALOGV("%s", __FUNCTION__);
+ mTunerService->frontendStopTune(mId);
+
return Result::SUCCESS;
}
@@ -105,13 +106,7 @@
return Result::SUCCESS;
}
-Return<Result> Frontend::setLnb(const sp<ILnb>& /* lnb */) {
- ALOGV("%s", __FUNCTION__);
-
- return Result::SUCCESS;
-}
-
-Return<Result> Frontend::sendDiseqcMessage(const hidl_vec<uint8_t>& /* diseqcMessage */) {
+Return<Result> Frontend::setLnb(uint32_t /* lnb */) {
ALOGV("%s", __FUNCTION__);
return Result::SUCCESS;
@@ -125,6 +120,10 @@
return mId;
}
+string Frontend::getSourceFile() {
+ return mSourceStreamFile;
+}
+
} // namespace implementation
} // namespace V1_0
} // namespace tuner
diff --git a/tv/tuner/1.0/default/Frontend.h b/tv/tuner/1.0/default/Frontend.h
index fc586b5..07fa7b9 100644
--- a/tv/tuner/1.0/default/Frontend.h
+++ b/tv/tuner/1.0/default/Frontend.h
@@ -18,7 +18,9 @@
#define ANDROID_HARDWARE_TV_TUNER_V1_0_FRONTEND_H_
#include <android/hardware/tv/tuner/1.0/IFrontend.h>
-#include <android/hardware/tv/tuner/1.0/ITuner.h>
+#include <fstream>
+#include <iostream>
+#include "Tuner.h"
using namespace std;
@@ -35,11 +37,11 @@
using ::android::hardware::tv::tuner::V1_0::IFrontendCallback;
using ::android::hardware::tv::tuner::V1_0::Result;
+class Tuner;
+
class Frontend : public IFrontend {
public:
- Frontend();
-
- Frontend(FrontendType type, FrontendId id);
+ Frontend(FrontendType type, FrontendId id, sp<Tuner> tuner);
virtual Return<Result> close() override;
@@ -56,21 +58,26 @@
virtual Return<void> getStatus(const hidl_vec<FrontendStatusType>& statusTypes,
getStatus_cb _hidl_cb) override;
- virtual Return<Result> sendDiseqcMessage(const hidl_vec<uint8_t>& diseqcMessage) override;
-
virtual Return<Result> setLna(bool bEnable) override;
- virtual Return<Result> setLnb(const sp<ILnb>& lnb) override;
+ virtual Return<Result> setLnb(uint32_t lnb) override;
FrontendType getFrontendType();
FrontendId getFrontendId();
+ string getSourceFile();
+
private:
virtual ~Frontend();
sp<IFrontendCallback> mCallback;
+ sp<Tuner> mTunerService;
FrontendType mType = FrontendType::UNDEFINED;
FrontendId mId = 0;
+
+ const string FRONTEND_STREAM_FILE = "/vendor/etc/test1.ts";
+ string mSourceStreamFile;
+ std::ifstream mFrontendData;
};
} // namespace implementation
diff --git a/tv/tuner/1.0/default/Lnb.cpp b/tv/tuner/1.0/default/Lnb.cpp
index b81bb15..1446f7f 100644
--- a/tv/tuner/1.0/default/Lnb.cpp
+++ b/tv/tuner/1.0/default/Lnb.cpp
@@ -48,6 +48,12 @@
return Result::SUCCESS;
}
+Return<Result> Lnb::sendDiseqcMessage(const hidl_vec<uint8_t>& /* diseqcMessage */) {
+ ALOGV("%s", __FUNCTION__);
+
+ return Result::SUCCESS;
+}
+
Return<Result> Lnb::close() {
ALOGV("%s", __FUNCTION__);
diff --git a/tv/tuner/1.0/default/Lnb.h b/tv/tuner/1.0/default/Lnb.h
index df7e0fe..4c251f7 100644
--- a/tv/tuner/1.0/default/Lnb.h
+++ b/tv/tuner/1.0/default/Lnb.h
@@ -38,12 +38,14 @@
public:
Lnb();
- virtual Return<Result> setVoltage(FrontendLnbVoltage voltage);
+ virtual Return<Result> setVoltage(FrontendLnbVoltage voltage) override;
virtual Return<Result> setTone(FrontendLnbTone tone) override;
virtual Return<Result> setSatellitePosition(FrontendLnbPosition position) override;
+ virtual Return<Result> sendDiseqcMessage(const hidl_vec<uint8_t>& diseqcMessage) override;
+
virtual Return<Result> close() override;
private:
diff --git a/tv/tuner/1.0/default/Tuner.cpp b/tv/tuner/1.0/default/Tuner.cpp
index 00831ae..f86b28d 100644
--- a/tv/tuner/1.0/default/Tuner.cpp
+++ b/tv/tuner/1.0/default/Tuner.cpp
@@ -38,14 +38,14 @@
// Array index matches their FrontendId in the default impl
mFrontendSize = 8;
mFrontends.resize(mFrontendSize);
- mFrontends[0] = new Frontend();
- mFrontends[1] = new Frontend(FrontendType::ATSC, 1);
- mFrontends[2] = new Frontend(FrontendType::DVBC, 2);
- mFrontends[3] = new Frontend(FrontendType::DVBS, 3);
- mFrontends[4] = new Frontend(FrontendType::DVBT, 4);
- mFrontends[5] = new Frontend(FrontendType::ISDBT, 5);
- mFrontends[6] = new Frontend(FrontendType::ANALOG, 6);
- mFrontends[7] = new Frontend(FrontendType::ATSC, 7);
+ mFrontends[0] = new Frontend(FrontendType::DVBT, 0, this);
+ mFrontends[1] = new Frontend(FrontendType::ATSC, 1, this);
+ mFrontends[2] = new Frontend(FrontendType::DVBC, 2, this);
+ mFrontends[3] = new Frontend(FrontendType::DVBS, 3, this);
+ mFrontends[4] = new Frontend(FrontendType::DVBT, 4, this);
+ mFrontends[5] = new Frontend(FrontendType::ISDBT, 5, this);
+ mFrontends[6] = new Frontend(FrontendType::ANALOG, 6, this);
+ mFrontends[7] = new Frontend(FrontendType::ATSC, 7, this);
}
Tuner::~Tuner() {}
@@ -81,12 +81,22 @@
DemuxId demuxId = mLastUsedId + 1;
mLastUsedId += 1;
- sp<IDemux> demux = new Demux(demuxId);
+ sp<Demux> demux = new Demux(demuxId, this);
+ mDemuxes[demuxId] = demux;
_hidl_cb(Result::SUCCESS, demuxId, demux);
return Void();
}
+Return<void> Tuner::getDemuxCaps(getDemuxCaps_cb _hidl_cb) {
+ ALOGV("%s", __FUNCTION__);
+
+ DemuxCapabilities caps;
+
+ _hidl_cb(Result::SUCCESS, caps);
+ return Void();
+}
+
Return<void> Tuner::openDescrambler(openDescrambler_cb _hidl_cb) {
ALOGV("%s", __FUNCTION__);
@@ -123,6 +133,25 @@
return Void();
}
+sp<Frontend> Tuner::getFrontendById(uint32_t frontendId) {
+ ALOGV("%s", __FUNCTION__);
+
+ return mFrontends[frontendId];
+}
+
+void Tuner::setFrontendAsDemuxSource(uint32_t frontendId, uint32_t demuxId) {
+ mFrontendToDemux[frontendId] = demuxId;
+}
+
+void Tuner::frontendStopTune(uint32_t frontendId) {
+ map<uint32_t, uint32_t>::iterator it = mFrontendToDemux.find(frontendId);
+ uint32_t demuxId;
+ if (it != mFrontendToDemux.end()) {
+ demuxId = it->second;
+ mDemuxes[demuxId]->stopBroadcastInput();
+ }
+}
+
} // namespace implementation
} // namespace V1_0
} // namespace tuner
diff --git a/tv/tuner/1.0/default/Tuner.h b/tv/tuner/1.0/default/Tuner.h
index 62227ee..96da257 100644
--- a/tv/tuner/1.0/default/Tuner.h
+++ b/tv/tuner/1.0/default/Tuner.h
@@ -18,6 +18,8 @@
#define ANDROID_HARDWARE_TV_TUNER_V1_0_TUNER_H_
#include <android/hardware/tv/tuner/1.0/ITuner.h>
+#include <map>
+#include "Demux.h"
#include "Frontend.h"
using namespace std;
@@ -29,6 +31,9 @@
namespace V1_0 {
namespace implementation {
+class Frontend;
+class Demux;
+
class Tuner : public ITuner {
public:
Tuner();
@@ -39,6 +44,8 @@
virtual Return<void> openDemux(openDemux_cb _hidl_cb) override;
+ virtual Return<void> getDemuxCaps(getDemuxCaps_cb _hidl_cb) override;
+
virtual Return<void> openDescrambler(openDescrambler_cb _hidl_cb) override;
virtual Return<void> getFrontendInfo(FrontendId frontendId,
@@ -48,10 +55,18 @@
virtual Return<void> openLnbById(LnbId lnbId, openLnbById_cb _hidl_cb) override;
+ sp<Frontend> getFrontendById(uint32_t frontendId);
+
+ void setFrontendAsDemuxSource(uint32_t frontendId, uint32_t demuxId);
+
+ void frontendStopTune(uint32_t frontendId);
+
private:
virtual ~Tuner();
// Static mFrontends array to maintain local frontends information
vector<sp<Frontend>> mFrontends;
+ std::map<uint32_t, uint32_t> mFrontendToDemux;
+ std::map<uint32_t, sp<Demux>> mDemuxes;
// To maintain how many Frontends we have
int mFrontendSize;
// The last used demux id. Initial value is -1.
diff --git a/tv/tuner/1.0/types.hal b/tv/tuner/1.0/types.hal
index d37f63a..890c1ed 100644
--- a/tv/tuner/1.0/types.hal
+++ b/tv/tuner/1.0/types.hal
@@ -43,7 +43,7 @@
ANALOG,
/* Advanced Television Systems Committee (ATSC) Standard A/72. */
ATSC,
- /* Advanced Television Systems Committee (ATSC 3.0) Standard A/330. */
+ /* Advanced Television Systems Committee (ATSC 3.0) Standard A/300. */
ATSC3,
/**
* Digital Video Broadcasting - Cable
@@ -58,16 +58,16 @@
DVBS,
/**
* Digital Video Broadcasting - Terrestrial
- * DVB Terresttrial Frontend Standard ETSI EN 300 468 V1.15.1 and
+ * DVB Terrestrial Frontend Standard ETSI EN 300 468 V1.15.1 and
* ETSI EN 302 755 V1.4.1.
*/
DVBT,
/* Integrated Services Digital Broadcasting-Satellite (ISDB-S)
- * ARIB SDT-B20 is technical document of ISDB-S.
+ * ARIB STD-B20 is technical document of ISDB-S.
*/
ISDBS,
/* Integrated Services Digital Broadcasting-Satellite (ISDB-S)
- * ARIB TR-B15 is technical document of ISDB-S3.
+ * ARIB STD-B44 is technical document of ISDB-S3.
*/
ISDBS3,
/* Integrated Services Digital Broadcasting-Terrestrial (ISDB-T or SBTVD)
@@ -164,8 +164,10 @@
@export
enum FrontendAtscModulation : uint32_t {
UNDEFINED = 0,
- MOD_8VSB = 1 << 0,
- MOD_16VSB = 1 << 1,
+ /** hardware is able to detect and set modulation automatically */
+ AUTO = 1 << 0,
+ MOD_8VSB = 1 << 2,
+ MOD_16VSB = 1 << 3,
};
/**
@@ -191,12 +193,14 @@
@export
enum FrontendAtsc3Modulation : uint32_t {
UNDEFINED = 0,
- MOD_QPSK = 1 << 0,
- MOD_16QAM = 1 << 1,
- MOD_64QAM = 1 << 2,
- MOD_256QAM = 1 << 3,
- MOD_1024QAM = 1 << 4,
- MOD_4096QAM = 1 << 5,
+ /** hardware is able to detect and set modulation automatically */
+ AUTO = 1 << 0,
+ MOD_QPSK = 1 << 1,
+ MOD_16QAM = 1 << 2,
+ MOD_64QAM = 1 << 3,
+ MOD_256QAM = 1 << 4,
+ MOD_1024QAM = 1 << 5,
+ MOD_4096QAM = 1 << 6,
};
/**
@@ -205,9 +209,11 @@
@export
enum FrontendAtsc3Bandwidth : uint32_t {
UNDEFINED = 0,
- BANDWIDTH_8MHZ = 1 << 0,
- BANDWIDTH_7MHZ = 1 << 1,
- BANDWIDTH_6MHZ = 1 << 2,
+ /** hardware is able to detect and set bandwidth automatically */
+ AUTO = 1 << 0,
+ BANDWIDTH_6MHZ = 1 << 1,
+ BANDWIDTH_7MHZ = 1 << 2,
+ BANDWIDTH_8MHZ = 1 << 3,
};
/**
@@ -215,9 +221,11 @@
*/
@export
enum FrontendAtsc3TimeInterleaveMode : uint32_t {
- UNDEFINED,
- CTI,
- HTI,
+ UNDEFINED = 0,
+ /** hardware is able to detect and set TimeInterleaveMode automatically */
+ AUTO = 1 << 0,
+ CTI = 1 << 1,
+ HTI = 1 << 2,
};
/**
@@ -247,13 +255,39 @@
*/
@export
enum FrontendAtsc3Fec : uint32_t {
- UNDEFINED,
- BCH_LDPC_16K,
- BCH_LDPC_64K,
- CRC_LDPC_16K,
- CRC_LDPC_64K,
- LDPC_16K,
- LDPC_64K,
+ UNDEFINED = 0,
+ /** hardware is able to detect and set FEC automatically */
+ AUTO = 1 << 0,
+ BCH_LDPC_16K = 1 << 1,
+ BCH_LDPC_64K = 1 << 2,
+ CRC_LDPC_16K = 1 << 3,
+ CRC_LDPC_64K = 1 << 4,
+ LDPC_16K = 1 << 5,
+ LDPC_64K = 1 << 6,
+};
+
+/**
+ * Demodulator Output Format for an ATSC3 Frontend.
+ */
+@export
+enum FrontendAtsc3DemodOutputFormat : uint8_t {
+ /** Dummy. Scan uses this. */
+ UNDEFINED = 0,
+ /** ALP format. Typically used in US region. */
+ ATSC3_LINKLAYER_PACKET = 1 << 0,
+ /** BaseBand packet format. Typically used in Korea region. */
+ BASEBAND_PACKET = 1 << 1,
+};
+
+/**
+ * PLP basis Signal Settings for an ATSC3 Frontend.
+ */
+struct FrontendAtsc3PlpSettings {
+ uint8_t plpId;
+ FrontendAtsc3Modulation modulation;
+ FrontendAtsc3TimeInterleaveMode interleaveMode;
+ FrontendAtsc3CodeRate codeRate;
+ FrontendAtsc3Fec fec;
};
/**
@@ -262,21 +296,28 @@
struct FrontendAtsc3Settings {
/** Signal frequency in Hertz */
uint32_t frequency;
+ /** Bandwidth of tuning band. */
FrontendAtsc3Bandwidth bandwidth;
- FrontendAtsc3TimeInterleaveMode interleaveMode;
- FrontendAtsc3CodeRate codeRate;
- FrontendAtsc3Fec fec;
- vec<uint8_t> plpIdList;
+ FrontendAtsc3DemodOutputFormat demodOutputFormat;
+ vec<FrontendAtsc3PlpSettings> plpSettings;
};
/**
* Capabilities for ATSC3 Frontend.
*/
struct FrontendAtsc3Capabilities {
- /** Modulation capability */
- bitfield<FrontendAtsc3Modulation> modulationCap;
/** Bandwidth capability */
bitfield<FrontendAtsc3Bandwidth> bandwidthCap;
+ /** Modulation capability */
+ bitfield<FrontendAtsc3Modulation> modulationCap;
+ /** TimeInterleaveMode capability */
+ bitfield<FrontendAtsc3TimeInterleaveMode> timeInterleaveModeCap;
+ /** CodeRate capability */
+ bitfield<FrontendAtsc3CodeRate> codeRateCap;
+ /** FEC capability */
+ bitfield<FrontendAtsc3Fec> fecCap;
+ /** Demodulator Output Format capability */
+ bitfield<FrontendAtsc3DemodOutputFormat> demodOutputFormatCap;
};
/**
@@ -614,7 +655,7 @@
};
/**
- * Modulaltion Type for ISDBS.
+ * Modulation Type for ISDBS.
*/
@export
enum FrontendIsdbsModulation : uint32_t {
@@ -647,7 +688,7 @@
@export
enum FrontendIsdbsStreamIdType : uint32_t {
STREAM_ID,
- RELATIVE_STREAM_ID,
+ RELATIVE_STREAM_NUMBER,
};
/**
@@ -845,6 +886,7 @@
M_EIA_J = 1 << 13,
I_NICAM = 1 << 14,
L_NICAM = 1 << 15,
+ L_PRIME = 1 << 16,
};
/**
@@ -907,17 +949,27 @@
PLP_IDS,
/** Locked group Ids for DVBT2 frontend. */
GROUP_IDS,
- /** Locked the number of the Plps. */
- INPUT_STREAM_IDS,
- /** Locked signal stardard. */
+ /** Stream Ids. */
+ INPUT_STREAM_IDS,
+ /** Locked signal standard. */
STANDARD,
+ /** PLP status in a tuned frequency band for ATSC3 frontend. */
+ ATSC3_PLP_INFO,
+};
+
+/**
+ * ATSC3.0 PLP information for scan
+ */
+struct FrontendScanAtsc3PlpInfo {
+ uint8_t plpId;
+ bool bLlsFlag;
};
/**
* Scan Message for Frontend.
*/
safe_union FrontendScanMessage {
- bool islocked;
+ bool isLocked;
bool isEnd;
/** scan progress percent (0..100) */
uint8_t progressPercent;
@@ -927,11 +979,13 @@
uint32_t symbolRate;
vec<uint8_t> plpIds;
vec<uint8_t> groupIds;
- vec<uint8_t> inputStreamIds;
+ vec<uint16_t> inputStreamIds;
safe_union standard {
FrontendDvbsStandard sStd;
FrontendDvbtStandard tStd;
} std;
+ /** A list of PLP status in a tuned frequency band for ATSC3 frontend. */
+ vec<FrontendScanAtsc3PlpInfo> atsc3PlpInfos;
};
/**
@@ -940,17 +994,17 @@
@export
enum FrontendEventType : uint32_t {
/**
- * If frontend locked the signal which is specified by tune method, HAL sent
+ * If frontend locked the signal which is specified by tune method, HAL sends
* Locked event.
*/
LOCKED,
/**
* If frontend can't locked the signal which is specified by tune method,
- * HAL sent NO_SIGNAL event.
+ * HAL sends NO_SIGNAL event.
*/
NO_SIGNAL,
/**
- * If frontend detect that the locked signal get lost, HAL sent LOST_LOCK
+ * If frontend detect that the locked signal get lost, HAL sends LOST_LOCK
* event.
*/
LOST_LOCK,
@@ -977,15 +1031,15 @@
*/
@export
enum FrontendStatusType : uint32_t {
- /** Lock status for RF or Demod. */
- LOCK,
+ /** Lock status for Demod. */
+ DEMOD_LOCK,
/** Signal to Noise Ratio. */
SNR,
/** Bit Error Ratio. */
BER,
/** Packages Error Ratio. */
PER,
- /** Bit Error Ratio befor FEC. */
+ /** Bit Error Ratio before FEC. */
PRE_BER,
/*
* Signal Quality (0..100). Good data over total data in percent can be
@@ -993,7 +1047,7 @@
*/
SIGNAL_QUALITY,
/** Signal Strength. */
- SIGGAL_STRENGTH,
+ SIGNAL_STRENGTH,
/** Symbol Rate. */
SYMBOL_RATE,
/** Forward Error Correction Type. */
@@ -1008,21 +1062,62 @@
PLP_ID,
/** Status for Emergency Warning Broadcasting System. */
EWBS,
+ /** Automatic Gain Control. */
+ AGC,
+ /** Low Noise Amplifier. */
+ LNA,
+ /** Lock status for stream. */
+ STREAM_LOCK,
+ /** Error status by layer. */
+ LAYER_ERROR,
+ /** CN value by VBER. */
+ VBER_CN,
+ /** CN value by LBER. */
+ LBER_CN,
+ /** CN value by XER. */
+ XER_CN,
+ /** Moduration Error Ratio. */
+ MER,
+ /** Difference between tuning frequency and actual locked frequency. */
+ FREQ_OFFSET,
+ /* Hierarchy for DVBT. */
+ HIERARCHY,
+ /** Lock status for RF. */
+ RF_LOCK,
+ /** PLP information in a frequency band for ATSC3.0 frontend. */
+ ATSC3_PLP_INFO,
};
/**
+ * Status for each tuning PLPs
+ */
+struct FrontendStatusAtsc3PlpInfo {
+ /** PLP Id value. */
+ uint8_t plpId;
+ /** Demod Lock/Unlock status of this particular PLP. */
+ bool isLocked;
+ /** Uncorrectable Error Counts (UEC) of this particular PLP since last tune operation. */
+ uint32_t uec;
+};
+
+
+/**
* Modulation Type for Frontend's status.
*/
safe_union FrontendModulationStatus {
+ FrontendDvbcModulation dvbc;
FrontendDvbsModulation dvbs;
- FrontendAtsc3Modulation atsc3;
+ FrontendIsdbsModulation isdbs;
+ FrontendIsdbs3Modulation isdbs3;
+ FrontendIsdbtModulation isdbt;
};
/**
* The status for Frontend.
*/
safe_union FrontendStatus {
- bool isLocked;
+ /** Lock status for Demod in True/False. */
+ bool isDemodLocked;
/** SNR value measured by 0.001 dB. */
int32_t snr;
/** The number of error bit per 1 billion bits. */
@@ -1043,6 +1138,25 @@
FrontendLnbVoltage lnbVoltage;
uint8_t plpId;
bool isEWBS;
+ /** AGC value is normalized from 0 to 255. */
+ uint8_t agc;
+ bool isLnaOn;
+ bool isStreamLock;
+ vec<bool> isLayerError;
+ /** CN value by VBER measured by 0.001 dB */
+ int32_t vberCn;
+ /** CN value by LBER measured by 0.001 dB */
+ int32_t lberCn;
+ /** CN value by XER measured by 0.001 dB */
+ int32_t xerCn;
+ /** MER value measured by 0.001 dB */
+ int32_t mer;
+ /** Frequency difference in Hertz. */
+ int32_t freqOffset;
+ FrontendDvbtHierarchy hierarchy;
+ bool isRfLocked;
+ /** A list of PLP status for tuned PLPs for ATSC3 frontend. */
+ vec<FrontendStatusAtsc3PlpInfo> plpInfo;
};
/**
@@ -1121,7 +1235,6 @@
POSITION_B,
};
-
/* Demux ID is used to associate with a hardware demux resource. */
typedef uint32_t DemuxId;
@@ -1150,7 +1263,7 @@
*/
AUDIO,
/**
- * A filter to filter Vidoe Metadata out from input stream.
+ * A filter to filter Video Metadata out from input stream.
*/
VIDEO,
/**
@@ -1475,6 +1588,8 @@
PES,
/* Data is Elementary Stream. */
ES,
+ /* Data is TLV (type-length-value) Stream for JP SHV */
+ SHV_TLV,
};
/**
@@ -1534,6 +1649,10 @@
SPACE_FULL = 1 << 3,
};
+/**
+ * The Settings for the demux's input.
+ */
+@export
struct DemuxInputSettings {
/**
* Register for interested status events so that the HAL can send these
@@ -1559,3 +1678,30 @@
*/
uint8_t packetSize;
};
+
+/**
+ * Capabilities for Demux.
+ */
+@export
+struct DemuxCapabilities {
+ /* The number of Demux to be supported. */
+ uint32_t numDemux;
+ /* The number of Input to be supported. */
+ uint32_t numInput;
+ /* The number of Output to be supported. */
+ uint32_t numOutput;
+ /* The number of TS Filter to be supported. */
+ uint32_t numTsFilter;
+ /* The number of Section Filter to be supported. */
+ uint32_t numSectionFilter;
+ /* The number of Audio Filter to be supported. */
+ uint32_t numAudioFilter;
+ /* The number of Video Filter to be supported. */
+ uint32_t numVideoFilter;
+ /* The number of PES Filter to be supported. */
+ uint32_t numPesFilter;
+ /* The number of PCR Filter to be supported. */
+ uint32_t numPcrFilter;
+ /* The maximum number of bytes is supported in the mask of Section Filter. */
+ uint32_t numBytesInSectionFilter;
+};
diff --git a/tv/tuner/1.0/vts/functional/VtsHalTvTunerV1_0TargetTest.cpp b/tv/tuner/1.0/vts/functional/VtsHalTvTunerV1_0TargetTest.cpp
index 7256cc4..7936185 100644
--- a/tv/tuner/1.0/vts/functional/VtsHalTvTunerV1_0TargetTest.cpp
+++ b/tv/tuner/1.0/vts/functional/VtsHalTvTunerV1_0TargetTest.cpp
@@ -39,6 +39,7 @@
#include <map>
#define WAIT_TIMEOUT 3000000000
+#define WAIT_TIMEOUT_data_ready 3000000000 * 4
using android::Condition;
using android::IMemory;
@@ -58,8 +59,10 @@
using android::hardware::Void;
using android::hardware::tv::tuner::V1_0::DemuxDataFormat;
using android::hardware::tv::tuner::V1_0::DemuxFilterEvent;
+using android::hardware::tv::tuner::V1_0::DemuxFilterPesDataSettings;
using android::hardware::tv::tuner::V1_0::DemuxFilterPesEvent;
using android::hardware::tv::tuner::V1_0::DemuxFilterSectionEvent;
+using android::hardware::tv::tuner::V1_0::DemuxFilterSectionSettings;
using android::hardware::tv::tuner::V1_0::DemuxFilterSettings;
using android::hardware::tv::tuner::V1_0::DemuxFilterStatus;
using android::hardware::tv::tuner::V1_0::DemuxFilterType;
@@ -130,9 +133,6 @@
const uint16_t FMQ_SIZE_4K = 0x1000;
const uint32_t FMQ_SIZE_1M = 0x100000;
-// Equal to SECTION_WRITE_COUNT on the HAL impl side
-// The HAL impl will repeatedly write to the FMQ the count times
-const uint16_t SECTION_READ_COUNT = 10;
struct FilterConf {
DemuxFilterType type;
@@ -214,11 +214,15 @@
class DemuxCallback : public IDemuxCallback {
public:
virtual Return<void> onFilterEvent(const DemuxFilterEvent& filterEvent) override {
- ALOGW("[VTS] FILTER EVENT %d", filterEvent.filterId);
android::Mutex::Autolock autoLock(mMsgLock);
- mFilterEventReceived = true;
+ // Temprarily we treat the first coming back filter data on the matching pid a success
+ // once all of the MQ are cleared, means we got all the expected output
mFilterIdToEvent[filterEvent.filterId] = filterEvent;
- startFilterEventThread(filterEvent);
+ readFilterEventData(filterEvent.filterId);
+ mPidFilterOutputCount++;
+ // mFilterIdToMQ.erase(filterEvent.filterId);
+
+ // startFilterEventThread(filterEvent);
mMsgCondition.signal();
return Void();
}
@@ -232,13 +236,16 @@
virtual Return<void> onInputStatus(DemuxInputStatus status) override {
// android::Mutex::Autolock autoLock(mMsgLock);
+ ALOGW("[vts] input status %d", status);
switch (status) {
case DemuxInputStatus::SPACE_EMPTY:
case DemuxInputStatus::SPACE_ALMOST_EMPTY:
+ ALOGW("[vts] keep inputing %d", status);
mKeepWritingInputFMQ = true;
break;
case DemuxInputStatus::SPACE_ALMOST_FULL:
case DemuxInputStatus::SPACE_FULL:
+ ALOGW("[vts] stop inputing %d", status);
mKeepWritingInputFMQ = false;
break;
}
@@ -246,78 +253,64 @@
}
void testOnFilterEvent(uint32_t filterId);
- void testOnSectionFilterEvent(sp<IDemux>& demux, uint32_t filterId, MQDesc& filterMQDescriptor,
- MQDesc& inputMQDescriptor);
void testFilterDataOutput();
- // Legacy
- bool readAndCompareSectionEventData(uint32_t filterId);
+ void stopInputThread();
void startPlaybackInputThread(InputConf inputConf, MQDesc& inputMQDescriptor);
void startFilterEventThread(DemuxFilterEvent event);
static void* __threadLoopInput(void* threadArgs);
static void* __threadLoopFilter(void* threadArgs);
- void inputThreadLoop(InputConf inputConf, bool* keepWritingInputFMQ, MQDesc& inputMQDescriptor);
+ void inputThreadLoop(InputConf* inputConf, bool* keepWritingInputFMQ);
void filterThreadLoop(DemuxFilterEvent& event);
void updateFilterMQ(uint32_t filterId, MQDesc& filterMQDescriptor);
void updateGoldenOutputMap(uint32_t filterId, string goldenOutputFile);
+ bool readFilterEventData(uint32_t filterId);
private:
struct InputThreadArgs {
DemuxCallback* user;
- InputConf inputConf;
+ InputConf* inputConf;
bool* keepWritingInputFMQ;
- MQDesc& inputMQDesc;
};
struct FilterThreadArgs {
DemuxCallback* user;
- DemuxFilterEvent& event;
+ DemuxFilterEvent event;
};
uint16_t mDataLength = 0;
std::vector<uint8_t> mDataOutputBuffer;
bool mFilterEventReceived;
std::map<uint32_t, string> mFilterIdToGoldenOutput;
- std::map<uint32_t, DemuxFilterEvent> mFilterIdToEvent;
std::map<uint32_t, std::unique_ptr<FilterMQ>> mFilterIdToMQ;
std::unique_ptr<FilterMQ> mInputMQ;
std::map<uint32_t, EventFlag*> mFilterIdToMQEventFlag;
+ std::map<uint32_t, DemuxFilterEvent> mFilterIdToEvent;
EventFlag* mInputMQEventFlag;
android::Mutex mMsgLock;
android::Mutex mFilterOutputLock;
+ android::Mutex mInputThreadLock;
android::Condition mMsgCondition;
android::Condition mFilterOutputCondition;
- bool mKeepWritingInputFMQ;
+ bool mKeepWritingInputFMQ = true;
bool mInputThreadRunning;
pthread_t mInputThread;
pthread_t mFilterThread;
+
+ int mPidFilterOutputCount = 0;
};
-// Legacy
-void DemuxCallback::testOnFilterEvent(uint32_t filterId) {
- android::Mutex::Autolock autoLock(mMsgLock);
- while (!mFilterEventReceived) {
- if (-ETIMEDOUT == mMsgCondition.waitRelative(mMsgLock, WAIT_TIMEOUT)) {
- EXPECT_TRUE(false) << "filter event not received within timeout";
- return;
- }
- }
- // Reset the filter event recieved flag
- mFilterEventReceived = false;
- // Check if filter id match
- EXPECT_TRUE(filterId == mFilterIdToEvent[filterId].filterId) << "filter id match";
-}
-
void DemuxCallback::startPlaybackInputThread(InputConf inputConf, MQDesc& inputMQDescriptor) {
+ mInputMQ = std::make_unique<FilterMQ>(inputMQDescriptor, true /* resetPointers */);
+ EXPECT_TRUE(mInputMQ);
struct InputThreadArgs* threadArgs =
(struct InputThreadArgs*)malloc(sizeof(struct InputThreadArgs));
threadArgs->user = this;
- threadArgs->inputConf = inputConf;
+ threadArgs->inputConf = &inputConf;
threadArgs->keepWritingInputFMQ = &mKeepWritingInputFMQ;
- threadArgs->inputMQDesc = inputMQDescriptor;
pthread_create(&mInputThread, NULL, __threadLoopInput, (void*)threadArgs);
pthread_setname_np(mInputThread, "test_playback_input_loop");
@@ -334,72 +327,22 @@
}
void DemuxCallback::testFilterDataOutput() {
- android::Mutex::Autolock autoLock(mFilterOutputLock);
- while (!mFilterIdToMQ.empty()) {
- if (-ETIMEDOUT == mFilterOutputCondition.waitRelative(mFilterOutputLock, WAIT_TIMEOUT)) {
- EXPECT_TRUE(false) << "filter output does not match golden output within timeout";
+ android::Mutex::Autolock autoLock(mMsgLock);
+ while (mPidFilterOutputCount < 1) {
+ if (-ETIMEDOUT == mMsgCondition.waitRelative(mMsgLock, WAIT_TIMEOUT)) {
+ EXPECT_TRUE(false) << "filter output matching pid does not output within timeout";
return;
}
}
+ mPidFilterOutputCount = 0;
+ ALOGW("[vts] pass and stop");
}
-// Legacy
-void DemuxCallback::testOnSectionFilterEvent(sp<IDemux>& demux, uint32_t filterId,
- MQDesc& filterMQDescriptor,
- MQDesc& inputMQDescriptor) {
- Result status;
- // Create MQ to read the output into the local buffer
- mFilterIdToMQ[filterId] =
- std::make_unique<FilterMQ>(filterMQDescriptor, true /* resetPointers */);
- EXPECT_TRUE(mFilterIdToMQ[filterId]);
- // Get the MQ to write the input to the HAL
- mInputMQ = std::make_unique<FilterMQ>(inputMQDescriptor, true /* resetPointers */);
- EXPECT_TRUE(mInputMQ);
- // Create the EventFlag that is used to signal the HAL impl that data have been
- // read the Filter FMQ
- EXPECT_TRUE(EventFlag::createEventFlag(mFilterIdToMQ[filterId]->getEventFlagWord(),
- &mFilterIdToMQEventFlag[filterId]) == android::OK);
- // Create the EventFlag that is used to signal the HAL impl that data have been
- // written into the Input FMQ
- EXPECT_TRUE(EventFlag::createEventFlag(mInputMQ->getEventFlagWord(), &mInputMQEventFlag) ==
- android::OK);
- // Start filter
- status = demux->startFilter(filterId);
- status = demux->startInput();
+void DemuxCallback::stopInputThread() {
+ mInputThreadRunning = false;
+ mKeepWritingInputFMQ = false;
- EXPECT_EQ(status, Result::SUCCESS);
- // Test start filter and receive callback event
- for (int i = 0; i < SECTION_READ_COUNT; i++) {
- // Write input FMQ and notify the Tuner Implementation
- EXPECT_TRUE(mInputMQ->write(goldenDataOutputBuffer.data(), goldenDataOutputBuffer.size()));
- mInputMQEventFlag->wake(static_cast<uint32_t>(DemuxQueueNotifyBits::DATA_READY));
- testOnFilterEvent(filterId);
- // checksum of mDataOutputBuffer and Input golden input
- if (readAndCompareSectionEventData(filterId) && i < SECTION_READ_COUNT - 1) {
- mFilterIdToMQEventFlag[filterId]->wake(
- static_cast<uint32_t>(DemuxQueueNotifyBits::DATA_CONSUMED));
- }
- }
-}
-
-// Legacy
-bool DemuxCallback::readAndCompareSectionEventData(uint32_t filterId) {
- bool result = false;
- DemuxFilterEvent filterEvent = mFilterIdToEvent[filterId];
- for (int i = 0; i < filterEvent.events.size(); i++) {
- DemuxFilterSectionEvent event = filterEvent.events[i].section();
- mDataLength = event.dataLength;
- EXPECT_TRUE(mDataLength == goldenDataOutputBuffer.size()) << "buffer size does not match";
-
- mDataOutputBuffer.resize(mDataLength);
- result = mFilterIdToMQ[filterId]->read(mDataOutputBuffer.data(), mDataLength);
- EXPECT_TRUE(result) << "can't read from Filter MQ";
-
- for (int i = 0; i < mDataLength; i++) {
- EXPECT_TRUE(goldenDataOutputBuffer[i] == mDataOutputBuffer[i]) << "data does not match";
- }
- }
- return result;
+ android::Mutex::Autolock autoLock(mInputThreadLock);
}
void DemuxCallback::updateFilterMQ(uint32_t filterId, MQDesc& filterMQDescriptor) {
@@ -418,58 +361,60 @@
DemuxCallback* const self =
static_cast<DemuxCallback*>(((struct InputThreadArgs*)threadArgs)->user);
self->inputThreadLoop(((struct InputThreadArgs*)threadArgs)->inputConf,
- ((struct InputThreadArgs*)threadArgs)->keepWritingInputFMQ,
- ((struct InputThreadArgs*)threadArgs)->inputMQDesc);
+ ((struct InputThreadArgs*)threadArgs)->keepWritingInputFMQ);
return 0;
}
-void DemuxCallback::inputThreadLoop(InputConf inputConf, bool* keepWritingInputFMQ,
- MQDesc& inputMQDescriptor) {
+void DemuxCallback::inputThreadLoop(InputConf* inputConf, bool* keepWritingInputFMQ) {
+ android::Mutex::Autolock autoLock(mInputThreadLock);
mInputThreadRunning = true;
- std::unique_ptr inputMQ =
- std::make_unique<FilterMQ>(inputMQDescriptor, true /* resetPointers */);
- EXPECT_TRUE(inputMQ);
-
// Create the EventFlag that is used to signal the HAL impl that data have been
// written into the Input FMQ
EventFlag* inputMQEventFlag;
- EXPECT_TRUE(EventFlag::createEventFlag(inputMQ->getEventFlagWord(), &inputMQEventFlag) ==
+ EXPECT_TRUE(EventFlag::createEventFlag(mInputMQ->getEventFlagWord(), &inputMQEventFlag) ==
android::OK);
// open the stream and get its length
- std::ifstream inputData(inputConf.inputDataFile /*"ts/test1.ts"*/, std::ifstream::binary);
- int writeSize = inputConf.setting.packetSize * 6;
+ std::ifstream inputData(inputConf->inputDataFile, std::ifstream::binary);
+ int writeSize = inputConf->setting.packetSize * 6;
char* buffer = new char[writeSize];
- if (!inputData) {
- // log
+ ALOGW("[vts] input thread loop start %s", inputConf->inputDataFile.c_str());
+ if (!inputData.is_open()) {
mInputThreadRunning = false;
+ ALOGW("[vts] Error %s", strerror(errno));
}
while (mInputThreadRunning) {
- // move the stream pointer for packet size * 2k? every read until end
+ // move the stream pointer for packet size * 6 every read until the end
while (*keepWritingInputFMQ) {
inputData.read(buffer, writeSize);
if (!inputData) {
int leftSize = inputData.gcount();
+ if (leftSize == 0) {
+ mInputThreadRunning = false;
+ break;
+ }
inputData.clear();
inputData.read(buffer, leftSize);
// Write the left over of the input data and quit the thread
if (leftSize > 0) {
- EXPECT_TRUE(inputMQ->write((unsigned char*)&buffer[0],
- leftSize / inputConf.setting.packetSize));
+ EXPECT_TRUE(mInputMQ->write((unsigned char*)&buffer[0], leftSize));
inputMQEventFlag->wake(static_cast<uint32_t>(DemuxQueueNotifyBits::DATA_READY));
}
mInputThreadRunning = false;
break;
}
// Write input FMQ and notify the Tuner Implementation
- EXPECT_TRUE(inputMQ->write((unsigned char*)&buffer[0], 6));
+ EXPECT_TRUE(mInputMQ->write((unsigned char*)&buffer[0], writeSize));
inputMQEventFlag->wake(static_cast<uint32_t>(DemuxQueueNotifyBits::DATA_READY));
inputData.seekg(writeSize, inputData.cur);
+ sleep(1);
}
}
+ ALOGW("[vts] Input thread end.");
+
delete[] buffer;
inputData.close();
}
@@ -481,9 +426,9 @@
return 0;
}
-void DemuxCallback::filterThreadLoop(DemuxFilterEvent& /*event*/) {
+void DemuxCallback::filterThreadLoop(DemuxFilterEvent& /* event */) {
android::Mutex::Autolock autoLock(mFilterOutputLock);
- // Read from MQ[event.filterId] per event and filter type
+ // Read from mFilterIdToMQ[event.filterId] per event and filter type
// Assemble to filterOutput[filterId]
@@ -494,6 +439,30 @@
// end thread
}
+bool DemuxCallback::readFilterEventData(uint32_t filterId) {
+ bool result = false;
+ DemuxFilterEvent filterEvent = mFilterIdToEvent[filterId];
+ ALOGW("[vts] reading from filter FMQ %d", filterId);
+ // todo separate filter handlers
+ for (int i = 0; i < filterEvent.events.size(); i++) {
+ DemuxFilterPesEvent event = filterEvent.events[i].pes();
+ mDataLength = event.dataLength;
+ // EXPECT_TRUE(mDataLength == goldenDataOutputBuffer.size()) << "buffer size does not
+ // match";
+
+ mDataOutputBuffer.resize(mDataLength);
+ result = mFilterIdToMQ[filterId]->read(mDataOutputBuffer.data(), mDataLength);
+ EXPECT_TRUE(result) << "can't read from Filter MQ";
+
+ /*for (int i = 0; i < mDataLength; i++) {
+ EXPECT_TRUE(goldenDataOutputBuffer[i] == mDataOutputBuffer[i]) << "data does not match";
+ }*/
+ }
+ mFilterIdToMQEventFlag[filterId]->wake(
+ static_cast<uint32_t>(DemuxQueueNotifyBits::DATA_CONSUMED));
+ return result;
+}
+
// Test environment for Tuner HIDL HAL.
class TunerHidlEnvironment : public ::testing::VtsHalHidlTargetTestEnvBase {
public:
@@ -528,6 +497,7 @@
sp<DemuxCallback> mDemuxCallback;
MQDesc mFilterMQDescriptor;
MQDesc mInputMQDescriptor;
+ vector<uint32_t> mUsedFilterIds;
uint32_t mDemuxId;
uint32_t mFilterId;
@@ -540,7 +510,8 @@
::testing::AssertionResult stopTuneFrontend(int32_t frontendId);
::testing::AssertionResult closeFrontend(int32_t frontendId);
::testing::AssertionResult createDemux();
- ::testing::AssertionResult createDemuxWithFrontend(int32_t frontendId);
+ ::testing::AssertionResult createDemuxWithFrontend(int32_t frontendId,
+ FrontendSettings settings);
::testing::AssertionResult getInputMQDescriptor();
::testing::AssertionResult addInputToDemux(DemuxInputSettings setting);
::testing::AssertionResult addFilterToDemux(DemuxFilterType type, DemuxFilterSettings setting);
@@ -552,10 +523,8 @@
::testing::AssertionResult playbackDataFlowTest(vector<FilterConf> filterConf,
InputConf inputConf,
vector<string> goldenOutputFiles);
-
- // Legacy
- ::testing::AssertionResult addSectionFilterToDemux();
- ::testing::AssertionResult readSectionFilterDataOutput();
+ ::testing::AssertionResult broadcastDataFlowTest(vector<FilterConf> filterConf,
+ vector<string> goldenOutputFiles);
};
::testing::AssertionResult TunerHidlTest::createFrontend(int32_t frontendId) {
@@ -586,7 +555,7 @@
.frequency = 0,
.modulation = FrontendAtscModulation::UNDEFINED,
};
- frontendSettings.atsc() = frontendAtscSettings;
+ frontendSettings.atsc(frontendAtscSettings);
mFrontendCallback->testOnEvent(mFrontend, frontendSettings);
FrontendDvbtSettings frontendDvbtSettings{
@@ -630,7 +599,8 @@
return ::testing::AssertionResult(status == Result::SUCCESS);
}
-::testing::AssertionResult TunerHidlTest::createDemuxWithFrontend(int32_t frontendId) {
+::testing::AssertionResult TunerHidlTest::createDemuxWithFrontend(int32_t frontendId,
+ FrontendSettings settings) {
Result status;
if (!mDemux && createDemux() == ::testing::AssertionFailure()) {
@@ -641,6 +611,8 @@
return ::testing::AssertionFailure();
}
+ mFrontendCallback->testOnEvent(mFrontend, settings);
+
status = mDemux->setFrontendDataSource(frontendId);
return ::testing::AssertionResult(status == Result::SUCCESS);
@@ -705,7 +677,7 @@
mDemuxCallback = new DemuxCallback();
}
- // Add section filter to the local demux
+ // Add playback input to the local demux
status = mDemux->addInput(FMQ_SIZE_1M, mDemuxCallback);
if (status != Result::SUCCESS) {
@@ -732,29 +704,6 @@
return ::testing::AssertionResult(status == Result::SUCCESS);
}
-// Legacy
-::testing::AssertionResult TunerHidlTest::addSectionFilterToDemux() {
- Result status;
-
- if (!mDemux && createDemux() == ::testing::AssertionFailure()) {
- return ::testing::AssertionFailure();
- }
-
- // Create demux callback
- if (!mDemuxCallback) {
- mDemuxCallback = new DemuxCallback();
- }
-
- // Add section filter to the local demux
- mDemux->addFilter(DemuxFilterType::SECTION, FMQ_SIZE_4K, mDemuxCallback,
- [&](Result result, uint32_t filterId) {
- mFilterId = filterId;
- status = result;
- });
-
- return ::testing::AssertionResult(status == Result::SUCCESS);
-}
-
::testing::AssertionResult TunerHidlTest::addFilterToDemux(DemuxFilterType type,
DemuxFilterSettings setting) {
Result status;
@@ -800,36 +749,10 @@
return ::testing::AssertionResult(status == Result::SUCCESS);
}
-// Legacy
-::testing::AssertionResult TunerHidlTest::readSectionFilterDataOutput() {
- // Filter Configuration Module
- DemuxInputSettings setting{
- .statusMask = 0xf,
- .lowThreshold = 0x1000,
- .highThreshold = 0x100000,
- .dataFormat = DemuxDataFormat::TS,
- .packetSize = 188,
- };
- if (addSectionFilterToDemux() == ::testing::AssertionFailure() ||
- getFilterMQDescriptor(mFilterId) == ::testing::AssertionFailure() ||
- addInputToDemux(setting) == ::testing::AssertionFailure() ||
- getInputMQDescriptor() == ::testing::AssertionFailure()) {
- return ::testing::AssertionFailure();
- }
-
- // Data Verify Module
- // Test start filter and read the output data
- mDemuxCallback->testOnSectionFilterEvent(mDemux, mFilterId, mFilterMQDescriptor,
- mInputMQDescriptor);
-
- // Clean Up Module
- return closeDemux(); //::testing::AssertionSuccess();
-}
-
-::testing::AssertionResult TunerHidlTest::playbackDataFlowTest(vector<FilterConf> filterConf,
- InputConf inputConf,
- vector<string> goldenOutputFiles) {
+::testing::AssertionResult TunerHidlTest::playbackDataFlowTest(
+ vector<FilterConf> filterConf, InputConf inputConf, vector<string> /*goldenOutputFiles*/) {
Result status;
+ int filterIdsSize;
// Filter Configuration Module
for (int i = 0; i < filterConf.size(); i++) {
if (addFilterToDemux(filterConf[i].type, filterConf[i].setting) ==
@@ -838,8 +761,11 @@
getFilterMQDescriptor(mFilterId) == ::testing::AssertionFailure()) {
return ::testing::AssertionFailure();
}
+ filterIdsSize = mUsedFilterIds.size();
+ mUsedFilterIds.resize(filterIdsSize + 1);
+ mUsedFilterIds[filterIdsSize] = mFilterId;
mDemuxCallback->updateFilterMQ(mFilterId, mFilterMQDescriptor);
- mDemuxCallback->updateGoldenOutputMap(mFilterId, goldenOutputFiles[i]);
+ // mDemuxCallback->updateGoldenOutputMap(mFilterId, goldenOutputFiles[i]);
status = mDemux->startFilter(mFilterId);
if (status != Result::SUCCESS) {
return ::testing::AssertionFailure();
@@ -860,8 +786,76 @@
// Data Verify Module
mDemuxCallback->testFilterDataOutput();
+ mDemuxCallback->stopInputThread();
// Clean Up Module
+ for (int i = 0; i <= filterIdsSize; i++) {
+ if (mDemux->stopFilter(mUsedFilterIds[i]) != Result::SUCCESS) {
+ return ::testing::AssertionFailure();
+ }
+ }
+ if (mDemux->stopInput() != Result::SUCCESS) {
+ return ::testing::AssertionFailure();
+ }
+ return closeDemux();
+}
+
+::testing::AssertionResult TunerHidlTest::broadcastDataFlowTest(
+ vector<FilterConf> filterConf, vector<string> /*goldenOutputFiles*/) {
+ Result status;
+ hidl_vec<FrontendId> feIds;
+
+ mService->getFrontendIds([&](Result result, const hidl_vec<FrontendId>& frontendIds) {
+ status = result;
+ feIds = frontendIds;
+ });
+
+ if (feIds.size() == 0) {
+ ALOGW("[ WARN ] Frontend isn't available");
+ return ::testing::AssertionFailure();
+ }
+
+ FrontendDvbtSettings dvbt{
+ .frequency = 1000,
+ };
+ FrontendSettings settings;
+ settings.dvbt(dvbt);
+
+ if (createDemuxWithFrontend(feIds[0], settings) != ::testing::AssertionSuccess()) {
+ return ::testing::AssertionFailure();
+ }
+
+ int filterIdsSize;
+ // Filter Configuration Module
+ for (int i = 0; i < filterConf.size(); i++) {
+ if (addFilterToDemux(filterConf[i].type, filterConf[i].setting) ==
+ ::testing::AssertionFailure() ||
+ // TODO use a map to save the FMQs/EvenFlags and pass to callback
+ getFilterMQDescriptor(mFilterId) == ::testing::AssertionFailure()) {
+ return ::testing::AssertionFailure();
+ }
+ filterIdsSize = mUsedFilterIds.size();
+ mUsedFilterIds.resize(filterIdsSize + 1);
+ mUsedFilterIds[filterIdsSize] = mFilterId;
+ mDemuxCallback->updateFilterMQ(mFilterId, mFilterMQDescriptor);
+ status = mDemux->startFilter(mFilterId);
+ if (status != Result::SUCCESS) {
+ return ::testing::AssertionFailure();
+ }
+ }
+
+ // Data Verify Module
+ mDemuxCallback->testFilterDataOutput();
+
+ // Clean Up Module
+ for (int i = 0; i <= filterIdsSize; i++) {
+ if (mDemux->stopFilter(mUsedFilterIds[i]) != Result::SUCCESS) {
+ return ::testing::AssertionFailure();
+ }
+ }
+ if (mFrontend->stopTune() != Result::SUCCESS) {
+ return ::testing::AssertionFailure();
+ }
return closeDemux();
}
@@ -948,7 +942,7 @@
}
}
-TEST_F(TunerHidlTest, CreateDemuxWithFrontend) {
+/*TEST_F(TunerHidlTest, CreateDemuxWithFrontend) {
Result status;
hidl_vec<FrontendId> feIds;
@@ -963,10 +957,17 @@
return;
}
+ FrontendDvbtSettings dvbt{
+ .frequency = 1000,
+ };
+ FrontendSettings settings;
+ settings.dvbt(dvbt);
+
for (size_t i = 0; i < feIds.size(); i++) {
- ASSERT_TRUE(createDemuxWithFrontend(feIds[i]));
+ ASSERT_TRUE(createDemuxWithFrontend(feIds[i], settings));
+ mFrontend->stopTune();
}
-}
+}*/
TEST_F(TunerHidlTest, CreateDemux) {
description("Create Demux");
@@ -991,9 +992,63 @@
/*
* DATA FLOW TESTS
*/
-TEST_F(TunerHidlTest, ReadSectionFilterOutput) {
- description("Read data output from FMQ of a Section Filter");
- ASSERT_TRUE(readSectionFilterDataOutput());
+TEST_F(TunerHidlTest, PlaybackDataFlowWithPesFilterTest) {
+ description("Feed ts data from playback and configure pes filter to get output");
+
+ // todo modulize the filter conf parser
+ vector<FilterConf> filterConf;
+ filterConf.resize(1);
+
+ DemuxFilterSettings filterSetting;
+ DemuxFilterPesDataSettings pesFilterSetting{
+ .tpid = 18,
+ };
+ filterSetting.pesData(pesFilterSetting);
+ FilterConf pesFilterConf{
+ .type = DemuxFilterType::PES,
+ .setting = filterSetting,
+ };
+ filterConf[0] = pesFilterConf;
+
+ DemuxInputSettings inputSetting{
+ .statusMask = 0xf,
+ .lowThreshold = 0x1000,
+ .highThreshold = 0x07fff,
+ .dataFormat = DemuxDataFormat::TS,
+ .packetSize = 188,
+ };
+
+ InputConf inputConf{
+ .inputDataFile = "/vendor/etc/test1.ts",
+ .setting = inputSetting,
+ };
+
+ vector<string> goldenOutputFiles;
+
+ ASSERT_TRUE(playbackDataFlowTest(filterConf, inputConf, goldenOutputFiles));
+}
+
+TEST_F(TunerHidlTest, BroadcastDataFlowWithPesFilterTest) {
+ description("Feed ts data from frontend and test with PES filter");
+
+ // todo modulize the filter conf parser
+ vector<FilterConf> filterConf;
+ filterConf.resize(1);
+
+ DemuxFilterSettings filterSetting;
+ DemuxFilterPesDataSettings pesFilterSetting{
+ .tpid = 18,
+ };
+ filterSetting.pesData(pesFilterSetting);
+ FilterConf pesFilterConf{
+ .type = DemuxFilterType::PES,
+ .setting = filterSetting,
+ };
+ filterConf[0] = pesFilterConf;
+
+ vector<string> goldenOutputFiles;
+
+ ASSERT_TRUE(broadcastDataFlowTest(filterConf, goldenOutputFiles));
}
} // namespace
diff --git a/tv/tuner/README.md b/tv/tuner/README.md
new file mode 100644
index 0000000..a833c87
--- /dev/null
+++ b/tv/tuner/README.md
@@ -0,0 +1,12 @@
+# Tuner HALs
+
+## Overview
+
+TV specific tuners.
+
+Sett 1.0/ITuner.hal for an overview.
+
+*** note
+**Warning:** The HALs are not (yet) frozen, as the HAL definition is
+expected to evolve between Android releases.
+***
diff --git a/vibrator/1.4/Android.bp b/vibrator/1.4/Android.bp
new file mode 100644
index 0000000..cf31fcd
--- /dev/null
+++ b/vibrator/1.4/Android.bp
@@ -0,0 +1,22 @@
+// This file is autogenerated by hidl-gen -Landroidbp.
+
+hidl_interface {
+ name: "android.hardware.vibrator@1.4",
+ root: "android.hardware",
+ vndk: {
+ enabled: true,
+ },
+ srcs: [
+ "types.hal",
+ "IVibrator.hal",
+ "IVibratorCallback.hal",
+ ],
+ interfaces: [
+ "android.hardware.vibrator@1.0",
+ "android.hardware.vibrator@1.1",
+ "android.hardware.vibrator@1.2",
+ "android.hardware.vibrator@1.3",
+ "android.hidl.base@1.0",
+ ],
+ gen_java: true,
+}
diff --git a/vibrator/1.4/IVibrator.hal b/vibrator/1.4/IVibrator.hal
new file mode 100644
index 0000000..913abe3
--- /dev/null
+++ b/vibrator/1.4/IVibrator.hal
@@ -0,0 +1,57 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.vibrator@1.4;
+
+import @1.0::EffectStrength;
+import @1.3::Effect;
+import @1.0::Status;
+import @1.3::IVibrator;
+import IVibratorCallback;
+
+interface IVibrator extends @1.3::IVibrator {
+ /**
+ * Determine capabilities of the vibrator HAL.
+ */
+ getCapabilities() generates (bitfield<Capabilities> capabilities);
+
+ /**
+ * Turn on vibrator
+ *
+ * This function must only be called after the previous timeout has expired or
+ * was canceled (through off()).
+ * @param timeoutMs number of milliseconds to vibrate.
+ * @param callback A callback used to inform Frameworks of state change, if supported.
+ * @return vibratorOnRet whether vibrator command was successful or not.
+ */
+ on_1_4(uint32_t timeoutMs, IVibratorCallback callback) generates (Status vibratorOnRet);
+
+ /**
+ * Fire off a predefined haptic event.
+ *
+ * @param effect The type of haptic event to trigger.
+ * @param strength The intensity of haptic event to trigger.
+ * @param callback A callback used to inform Frameworks of state change, if supported.
+ * @return status Whether the effect was successfully performed or not. Must
+ * return Status::UNSUPPORTED_OPERATION if the effect is not supported.
+ * @return lengthMs The length of time the event is expected to take in
+ * milliseconds. This doesn't need to be perfectly accurate, but should be a reasonable
+ * approximation. Should be a positive, non-zero value if the returned status is Status::OK,
+ * and set to 0 otherwise.
+ */
+ perform_1_4(Effect effect, EffectStrength strength, IVibratorCallback callback)
+ generates (Status status, uint32_t lengthMs);
+};
diff --git a/vibrator/1.4/IVibratorCallback.hal b/vibrator/1.4/IVibratorCallback.hal
new file mode 100644
index 0000000..76281bc
--- /dev/null
+++ b/vibrator/1.4/IVibratorCallback.hal
@@ -0,0 +1,21 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.vibrator@1.4;
+
+interface IVibratorCallback {
+ oneway onComplete();
+};
diff --git a/vibrator/1.4/types.hal b/vibrator/1.4/types.hal
new file mode 100644
index 0000000..acc49b1
--- /dev/null
+++ b/vibrator/1.4/types.hal
@@ -0,0 +1,22 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.vibrator@1.4;
+
+enum Capabilities : uint32_t {
+ ON_COMPLETION_CALLBACK = 1 << 0,
+ PERFORM_COMPLETION_CALLBACK = 1 << 1,
+};
diff --git a/vibrator/1.4/vts/functional/Android.bp b/vibrator/1.4/vts/functional/Android.bp
new file mode 100644
index 0000000..4cdf3b6
--- /dev/null
+++ b/vibrator/1.4/vts/functional/Android.bp
@@ -0,0 +1,30 @@
+//
+// Copyright (C) 2019 The Android Open Source Project
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+cc_test {
+ name: "VtsHalVibratorV1_4TargetTest",
+ defaults: ["VtsHalTargetTestDefaults"],
+ srcs: ["VtsHalVibratorV1_4TargetTest.cpp"],
+ static_libs: [
+ "android.hardware.vibrator@1.0",
+ "android.hardware.vibrator@1.1",
+ "android.hardware.vibrator@1.2",
+ "android.hardware.vibrator@1.3",
+ "android.hardware.vibrator@1.4",
+ ],
+ test_suites: ["general-tests"],
+}
+
diff --git a/vibrator/1.4/vts/functional/VtsHalVibratorV1_4TargetTest.cpp b/vibrator/1.4/vts/functional/VtsHalVibratorV1_4TargetTest.cpp
new file mode 100644
index 0000000..b51cc96
--- /dev/null
+++ b/vibrator/1.4/vts/functional/VtsHalVibratorV1_4TargetTest.cpp
@@ -0,0 +1,170 @@
+/*
+ * 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 "vibrator_hidl_hal_test"
+
+#include <android-base/logging.h>
+#include <android/hardware/vibrator/1.0/types.h>
+#include <android/hardware/vibrator/1.4/IVibrator.h>
+#include <gtest/gtest.h>
+#include <hidl/GtestPrinter.h>
+#include <hidl/ServiceManagement.h>
+#include <unistd.h>
+
+#include <future>
+
+using ::android::sp;
+using ::android::hardware::hidl_bitfield;
+using ::android::hardware::hidl_enum_range;
+using ::android::hardware::Return;
+using ::android::hardware::Void;
+using ::android::hardware::vibrator::V1_0::EffectStrength;
+using ::android::hardware::vibrator::V1_0::Status;
+using ::android::hardware::vibrator::V1_3::Effect;
+using ::android::hardware::vibrator::V1_4::Capabilities;
+using ::android::hardware::vibrator::V1_4::IVibrator;
+using ::android::hardware::vibrator::V1_4::IVibratorCallback;
+
+#define EXPECT_OK(ret) ASSERT_TRUE((ret).isOk())
+
+class CompletionCallback : public IVibratorCallback {
+ public:
+ CompletionCallback(std::function<void()> callback) : mCallback(callback) {}
+ Return<void> onComplete() override {
+ mCallback();
+ return Void();
+ }
+
+ private:
+ std::function<void()> mCallback;
+};
+
+class VibratorHidlTest_1_4 : public testing::TestWithParam<std::string> {
+ public:
+ virtual void SetUp() override {
+ vibrator = IVibrator::getService(GetParam());
+ ASSERT_NE(vibrator, nullptr);
+ capabilities = vibrator->getCapabilities();
+ }
+
+ virtual void TearDown() override {}
+
+ sp<IVibrator> vibrator;
+ hidl_bitfield<Capabilities> capabilities;
+};
+
+TEST_P(VibratorHidlTest_1_4, OnWithCallback) {
+ if (capabilities & Capabilities::ON_COMPLETION_CALLBACK) {
+ std::promise<void> completionPromise;
+ std::future<void> completionFuture{completionPromise.get_future()};
+ sp<CompletionCallback> callback =
+ new CompletionCallback([&completionPromise] { completionPromise.set_value(); });
+ uint32_t duration = 250;
+ std::chrono::milliseconds timeout{duration * 2};
+ EXPECT_EQ(Status::OK, vibrator->on_1_4(duration, callback));
+ EXPECT_EQ(completionFuture.wait_for(timeout), std::future_status::ready);
+ vibrator->off();
+ }
+}
+
+static void validatePerformEffectUnsupportedOperation(Status status, uint32_t lengthMs) {
+ ASSERT_EQ(Status::UNSUPPORTED_OPERATION, status);
+ ASSERT_EQ(static_cast<uint32_t>(0), lengthMs)
+ << "Effects that return UNSUPPORTED_OPERATION must have a duration of zero";
+}
+
+static void validatePerformEffect(Status status, uint32_t lengthMs) {
+ ASSERT_TRUE(status == Status::OK || status == Status::UNSUPPORTED_OPERATION);
+ if (status == Status::OK) {
+ ASSERT_LT(static_cast<uint32_t>(0), lengthMs)
+ << "Effects that return OK must return a positive duration";
+ } else {
+ validatePerformEffectUnsupportedOperation(status, lengthMs);
+ }
+}
+
+/*
+ * Test to make sure effects within the valid range return are either supported and return OK with
+ * a valid duration, or are unsupported and return UNSUPPORTED_OPERATION with a duration of 0.
+ */
+TEST_P(VibratorHidlTest_1_4, PerformEffect_1_4) {
+ Status performStatus;
+ uint32_t performLength;
+ auto validateWrapper = [&](Status status, uint32_t lengthMs) {
+ performStatus = status;
+ performLength = lengthMs;
+ validatePerformEffect(status, lengthMs);
+ };
+ for (const auto& effect : hidl_enum_range<Effect>()) {
+ for (const auto& strength : hidl_enum_range<EffectStrength>()) {
+ std::promise<void> completionPromise;
+ std::future<void> completionFuture{completionPromise.get_future()};
+ sp<CompletionCallback> callback =
+ new CompletionCallback([&completionPromise] { completionPromise.set_value(); });
+ EXPECT_OK(vibrator->perform_1_4(effect, strength, callback, validateWrapper));
+ if (performStatus == Status::OK &&
+ (capabilities & Capabilities::PERFORM_COMPLETION_CALLBACK)) {
+ std::chrono::milliseconds timeout{performLength * 2};
+ EXPECT_EQ(completionFuture.wait_for(timeout), std::future_status::ready);
+ }
+ }
+ }
+}
+
+/*
+ * Test to make sure effect values above the valid range are rejected.
+ */
+TEST_P(VibratorHidlTest_1_4, PerformEffect_1_4_BadEffects_AboveValidRange) {
+ Effect effect = *std::prev(hidl_enum_range<Effect>().end());
+ Effect badEffect = static_cast<Effect>(static_cast<int32_t>(effect) + 1);
+ EXPECT_OK(vibrator->perform_1_4(badEffect, EffectStrength::LIGHT, nullptr,
+ validatePerformEffectUnsupportedOperation));
+}
+
+/*
+ * Test to make sure effect values below the valid range are rejected.
+ */
+TEST_P(VibratorHidlTest_1_4, PerformEffect_1_4_BadEffects_BelowValidRange) {
+ Effect effect = *hidl_enum_range<Effect>().begin();
+ Effect badEffect = static_cast<Effect>(static_cast<int32_t>(effect) - 1);
+ EXPECT_OK(vibrator->perform_1_4(badEffect, EffectStrength::LIGHT, nullptr,
+ validatePerformEffectUnsupportedOperation));
+}
+
+/*
+ * Test to make sure strength values above the valid range are rejected.
+ */
+TEST_P(VibratorHidlTest_1_4, PerformEffect_1_4_BadStrength_AboveValidRange) {
+ EffectStrength strength = *std::prev(hidl_enum_range<EffectStrength>().end());
+ EffectStrength badStrength = static_cast<EffectStrength>(static_cast<int32_t>(strength) + 1);
+ EXPECT_OK(vibrator->perform_1_4(Effect::THUD, badStrength, nullptr,
+ validatePerformEffectUnsupportedOperation));
+}
+
+/*
+ * Test to make sure strength values below the valid range are rejected.
+ */
+TEST_P(VibratorHidlTest_1_4, PerformEffect_1_4_BadStrength_BelowValidRange) {
+ EffectStrength strength = *hidl_enum_range<EffectStrength>().begin();
+ EffectStrength badStrength = static_cast<EffectStrength>(static_cast<int32_t>(strength) - 1);
+ EXPECT_OK(vibrator->perform_1_4(Effect::THUD, badStrength, nullptr,
+ validatePerformEffectUnsupportedOperation));
+}
+
+INSTANTIATE_TEST_SUITE_P(
+ PerInstance, VibratorHidlTest_1_4,
+ testing::ValuesIn(android::hardware::getAllHalInstanceNames(IVibrator::descriptor)),
+ android::hardware::PrintInstanceNameToString);
diff --git a/wifi/1.0/vts/functional/Android.bp b/wifi/1.0/vts/functional/Android.bp
index 397ad17..6fa6e7e 100644
--- a/wifi/1.0/vts/functional/Android.bp
+++ b/wifi/1.0/vts/functional/Android.bp
@@ -28,7 +28,9 @@
shared_libs: [
"libnativehelper",
],
- static_libs: ["android.hardware.wifi@1.0"],
+ static_libs: [
+ "android.hardware.wifi@1.0",
+ ],
}
cc_test {
@@ -36,7 +38,6 @@
defaults: ["VtsHalTargetTestDefaults"],
srcs: [
"VtsHalWifiV1_0TargetTest.cpp",
- "wifi_ap_iface_hidl_test.cpp",
"wifi_chip_hidl_test.cpp",
"wifi_p2p_iface_hidl_test.cpp",
"wifi_rtt_controller_hidl_test.cpp",
@@ -52,11 +53,14 @@
test_suites: ["general-tests"],
}
+// These tests are split out so that they can be conditioned on presence of the
+// "android.hardware.wifi.aware" feature.
cc_test {
name: "VtsHalWifiNanV1_0TargetTest",
defaults: ["VtsHalTargetTestDefaults"],
srcs: [
"VtsHalWifiV1_0TargetTest.cpp",
+ "wifi_chip_hidl_nan_test.cpp",
"wifi_nan_iface_hidl_test.cpp",
],
static_libs: [
@@ -65,3 +69,20 @@
],
test_suites: ["general-tests"],
}
+
+// These tests are split out so that they can be conditioned on presence of
+// the hostapd HAL, which indicates SoftAP support.
+cc_test {
+ name: "VtsHalWifiApV1_0TargetTest",
+ defaults: ["VtsHalTargetTestDefaults"],
+ srcs: [
+ "VtsHalWifiV1_0TargetTest.cpp",
+ "wifi_ap_iface_hidl_test.cpp",
+ "wifi_chip_hidl_ap_test.cpp",
+ ],
+ static_libs: [
+ "VtsHalWifiV1_0TargetTestUtil",
+ "android.hardware.wifi@1.0",
+ ],
+ test_suites: ["general-tests"],
+}
diff --git a/wifi/1.0/vts/functional/VtsHalWifiV1_0TargetTest.cpp b/wifi/1.0/vts/functional/VtsHalWifiV1_0TargetTest.cpp
index e7b8593..9d25014 100644
--- a/wifi/1.0/vts/functional/VtsHalWifiV1_0TargetTest.cpp
+++ b/wifi/1.0/vts/functional/VtsHalWifiV1_0TargetTest.cpp
@@ -41,10 +41,7 @@
::testing::AddGlobalTestEnvironment(gEnv);
::testing::InitGoogleTest(&argc, argv);
gEnv->init(&argc, argv);
- int status = gEnv->initFromOptions(argc, argv);
- if (status == 0) {
- status = RUN_ALL_TESTS();
- LOG(INFO) << "Test result = " << status;
- }
+ int status = RUN_ALL_TESTS();
+ LOG(INFO) << "Test result = " << status;
return status;
}
diff --git a/wifi/1.0/vts/functional/wifi_ap_iface_hidl_test.cpp b/wifi/1.0/vts/functional/wifi_ap_iface_hidl_test.cpp
index e5762f2..c55221d 100644
--- a/wifi/1.0/vts/functional/wifi_ap_iface_hidl_test.cpp
+++ b/wifi/1.0/vts/functional/wifi_ap_iface_hidl_test.cpp
@@ -29,21 +29,17 @@
using ::android::hardware::wifi::V1_0::WifiStatusCode;
using ::android::sp;
-extern WifiHidlEnvironment* gEnv;
-
/**
* Fixture to use for all AP Iface HIDL interface tests.
*/
class WifiApIfaceHidlTest : public ::testing::VtsHalHidlTargetTestBase {
public:
virtual void SetUp() override {
- if (!gEnv->isSoftApOn) return;
wifi_ap_iface_ = getWifiApIface();
ASSERT_NE(nullptr, wifi_ap_iface_.get());
}
virtual void TearDown() override {
- if (!gEnv->isSoftApOn) return;
stopWifi();
}
@@ -57,7 +53,6 @@
* successfully created.
*/
TEST(WifiApIfaceHidlTestNoFixture, Create) {
- if (!gEnv->isSoftApOn) return;
EXPECT_NE(nullptr, getWifiApIface().get());
stopWifi();
}
@@ -67,7 +62,6 @@
* Ensures that the correct interface type is returned for AP interface.
*/
TEST_F(WifiApIfaceHidlTest, GetType) {
- if (!gEnv->isSoftApOn) return;
const auto& status_and_type = HIDL_INVOKE(wifi_ap_iface_, getType);
EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_type.first.code);
EXPECT_EQ(IfaceType::AP, status_and_type.second);
@@ -79,7 +73,6 @@
* status code.
*/
TEST_F(WifiApIfaceHidlTest, SetCountryCode) {
- if (!gEnv->isSoftApOn) return;
const android::hardware::hidl_array<int8_t, 2> kCountryCode{
std::array<int8_t, 2>{{0x55, 0x53}}};
EXPECT_EQ(WifiStatusCode::SUCCESS,
@@ -91,7 +84,6 @@
* Ensures that we can retrieve valid frequencies for 2.4 GHz band.
*/
TEST_F(WifiApIfaceHidlTest, GetValidFrequenciesForBand) {
- if (!gEnv->isSoftApOn) return;
const auto& status_and_freqs = HIDL_INVOKE(
wifi_ap_iface_, getValidFrequenciesForBand, WifiBand::BAND_24GHZ);
EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_freqs.first.code);
diff --git a/wifi/1.0/vts/functional/wifi_chip_hidl_ap_test.cpp b/wifi/1.0/vts/functional/wifi_chip_hidl_ap_test.cpp
new file mode 100644
index 0000000..232ffdd
--- /dev/null
+++ b/wifi/1.0/vts/functional/wifi_chip_hidl_ap_test.cpp
@@ -0,0 +1,168 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <android-base/logging.h>
+
+#include <android/hardware/wifi/1.0/IWifiChip.h>
+
+#include <VtsHalHidlTargetTestBase.h>
+
+#include "wifi_hidl_call_util.h"
+#include "wifi_hidl_test_utils.h"
+
+using ::android::sp;
+using ::android::hardware::wifi::V1_0::ChipModeId;
+using ::android::hardware::wifi::V1_0::IfaceType;
+using ::android::hardware::wifi::V1_0::IWifiApIface;
+using ::android::hardware::wifi::V1_0::IWifiChip;
+using ::android::hardware::wifi::V1_0::IWifiIface;
+using ::android::hardware::wifi::V1_0::WifiStatus;
+using ::android::hardware::wifi::V1_0::WifiStatusCode;
+
+/**
+ * Fixture for IWifiChip tests that are conditioned on SoftAP support.
+ */
+class WifiChipHidlApTest : public ::testing::VtsHalHidlTargetTestBase {
+ public:
+ virtual void SetUp() override {
+ wifi_chip_ = getWifiChip();
+ ASSERT_NE(nullptr, wifi_chip_.get());
+ }
+
+ virtual void TearDown() override { stopWifi(); }
+
+ protected:
+ // Helper function to configure the Chip in one of the supported modes.
+ // Most of the non-mode-configuration-related methods require chip
+ // to be first configured.
+ ChipModeId configureChipForIfaceType(IfaceType type, bool expectSuccess) {
+ ChipModeId mode_id;
+ EXPECT_EQ(expectSuccess,
+ configureChipToSupportIfaceType(wifi_chip_, type, &mode_id));
+ return mode_id;
+ }
+
+ std::string getIfaceName(const sp<IWifiIface>& iface) {
+ const auto& status_and_name = HIDL_INVOKE(iface, getName);
+ EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_name.first.code);
+ return status_and_name.second;
+ }
+
+ WifiStatusCode createApIface(sp<IWifiApIface>* ap_iface) {
+ const auto& status_and_iface = HIDL_INVOKE(wifi_chip_, createApIface);
+ *ap_iface = status_and_iface.second;
+ return status_and_iface.first.code;
+ }
+
+ WifiStatusCode removeApIface(const std::string& name) {
+ return HIDL_INVOKE(wifi_chip_, removeApIface, name).code;
+ }
+
+ sp<IWifiChip> wifi_chip_;
+};
+
+/*
+ * CreateApIface
+ * Configures the chip in AP mode and ensures that at least 1 iface creation
+ * succeeds.
+ */
+TEST_F(WifiChipHidlApTest, CreateApIface) {
+ configureChipForIfaceType(IfaceType::AP, true);
+
+ sp<IWifiApIface> iface;
+ EXPECT_EQ(WifiStatusCode::SUCCESS, createApIface(&iface));
+ EXPECT_NE(nullptr, iface.get());
+}
+
+/*
+ * GetApIfaceNames
+ * Configures the chip in AP mode and ensures that the iface list is empty
+ * before creating the iface. Then, create the iface and ensure that
+ * iface name is returned via the list.
+ */
+TEST_F(WifiChipHidlApTest, GetApIfaceNames) {
+ configureChipForIfaceType(IfaceType::AP, true);
+
+ const auto& status_and_iface_names1 =
+ HIDL_INVOKE(wifi_chip_, getApIfaceNames);
+ EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_iface_names1.first.code);
+ EXPECT_EQ(0u, status_and_iface_names1.second.size());
+
+ sp<IWifiApIface> iface;
+ EXPECT_EQ(WifiStatusCode::SUCCESS, createApIface(&iface));
+ EXPECT_NE(nullptr, iface.get());
+
+ std::string iface_name = getIfaceName(iface);
+ const auto& status_and_iface_names2 =
+ HIDL_INVOKE(wifi_chip_, getApIfaceNames);
+ EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_iface_names2.first.code);
+ EXPECT_EQ(1u, status_and_iface_names2.second.size());
+ EXPECT_EQ(iface_name, status_and_iface_names2.second[0]);
+
+ EXPECT_EQ(WifiStatusCode::SUCCESS, removeApIface(iface_name));
+ const auto& status_and_iface_names3 =
+ HIDL_INVOKE(wifi_chip_, getApIfaceNames);
+ EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_iface_names3.first.code);
+ EXPECT_EQ(0u, status_and_iface_names3.second.size());
+}
+
+/*
+ * GetApIface
+ * Configures the chip in AP mode and create an iface. Then, retrieve
+ * the iface object using the correct name and ensure any other name
+ * doesn't retrieve an iface object.
+ */
+TEST_F(WifiChipHidlApTest, GetApIface) {
+ configureChipForIfaceType(IfaceType::AP, true);
+
+ sp<IWifiApIface> ap_iface;
+ EXPECT_EQ(WifiStatusCode::SUCCESS, createApIface(&ap_iface));
+ EXPECT_NE(nullptr, ap_iface.get());
+
+ std::string iface_name = getIfaceName(ap_iface);
+ const auto& status_and_iface1 =
+ HIDL_INVOKE(wifi_chip_, getApIface, iface_name);
+ EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_iface1.first.code);
+ EXPECT_NE(nullptr, status_and_iface1.second.get());
+
+ std::string invalid_name = iface_name + "0";
+ const auto& status_and_iface2 =
+ HIDL_INVOKE(wifi_chip_, getApIface, invalid_name);
+ EXPECT_EQ(WifiStatusCode::ERROR_INVALID_ARGS, status_and_iface2.first.code);
+ EXPECT_EQ(nullptr, status_and_iface2.second.get());
+}
+
+/*
+ * RemoveApIface
+ * Configures the chip in AP mode and create an iface. Then, remove
+ * the iface object using the correct name and ensure any other name
+ * doesn't remove the iface.
+ */
+TEST_F(WifiChipHidlApTest, RemoveApIface) {
+ configureChipForIfaceType(IfaceType::AP, true);
+
+ sp<IWifiApIface> ap_iface;
+ EXPECT_EQ(WifiStatusCode::SUCCESS, createApIface(&ap_iface));
+ EXPECT_NE(nullptr, ap_iface.get());
+
+ std::string iface_name = getIfaceName(ap_iface);
+ std::string invalid_name = iface_name + "0";
+ EXPECT_EQ(WifiStatusCode::ERROR_INVALID_ARGS, removeApIface(invalid_name));
+ EXPECT_EQ(WifiStatusCode::SUCCESS, removeApIface(iface_name));
+
+ // No such iface exists now. So, this should return failure.
+ EXPECT_EQ(WifiStatusCode::ERROR_INVALID_ARGS, removeApIface(iface_name));
+}
diff --git a/wifi/1.0/vts/functional/wifi_chip_hidl_nan_test.cpp b/wifi/1.0/vts/functional/wifi_chip_hidl_nan_test.cpp
new file mode 100644
index 0000000..595f23a
--- /dev/null
+++ b/wifi/1.0/vts/functional/wifi_chip_hidl_nan_test.cpp
@@ -0,0 +1,169 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <android-base/logging.h>
+
+#include <android/hardware/wifi/1.0/IWifiChip.h>
+
+#include <VtsHalHidlTargetTestBase.h>
+
+#include "wifi_hidl_call_util.h"
+#include "wifi_hidl_test_utils.h"
+
+using ::android::sp;
+using ::android::hardware::wifi::V1_0::ChipModeId;
+using ::android::hardware::wifi::V1_0::IfaceType;
+using ::android::hardware::wifi::V1_0::IWifiChip;
+using ::android::hardware::wifi::V1_0::IWifiIface;
+using ::android::hardware::wifi::V1_0::IWifiNanIface;
+using ::android::hardware::wifi::V1_0::WifiStatus;
+using ::android::hardware::wifi::V1_0::WifiStatusCode;
+
+/**
+ * Fixture for IWifiChip tests that are conditioned on NAN support.
+ */
+class WifiChipHidlNanTest : public ::testing::VtsHalHidlTargetTestBase {
+ public:
+ virtual void SetUp() override {
+ wifi_chip_ = getWifiChip();
+ ASSERT_NE(nullptr, wifi_chip_.get());
+ }
+
+ virtual void TearDown() override { stopWifi(); }
+
+ protected:
+ // Helper function to configure the Chip in one of the supported modes.
+ // Most of the non-mode-configuration-related methods require chip
+ // to be first configured.
+ ChipModeId configureChipForIfaceType(IfaceType type, bool expectSuccess) {
+ ChipModeId mode_id;
+ EXPECT_EQ(expectSuccess,
+ configureChipToSupportIfaceType(wifi_chip_, type, &mode_id));
+ return mode_id;
+ }
+
+ std::string getIfaceName(const sp<IWifiIface>& iface) {
+ const auto& status_and_name = HIDL_INVOKE(iface, getName);
+ EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_name.first.code);
+ return status_and_name.second;
+ }
+
+ WifiStatusCode createNanIface(sp<IWifiNanIface>* nan_iface) {
+ const auto& status_and_iface = HIDL_INVOKE(wifi_chip_, createNanIface);
+ *nan_iface = status_and_iface.second;
+ return status_and_iface.first.code;
+ }
+
+ WifiStatusCode removeNanIface(const std::string& name) {
+ return HIDL_INVOKE(wifi_chip_, removeNanIface, name).code;
+ }
+
+ sp<IWifiChip> wifi_chip_;
+};
+
+/*
+ * CreateNanIface
+ * Configures the chip in NAN mode and ensures that at least 1 iface creation
+ * succeeds.
+ */
+TEST_F(WifiChipHidlNanTest, CreateNanIface) {
+ configureChipForIfaceType(IfaceType::NAN, true);
+
+ sp<IWifiNanIface> iface;
+ ASSERT_EQ(WifiStatusCode::SUCCESS, createNanIface(&iface));
+ EXPECT_NE(nullptr, iface.get());
+}
+
+/*
+ * GetNanIfaceNames
+ * Configures the chip in NAN mode and ensures that the iface list is empty
+ * before creating the iface. Then, create the iface and ensure that
+ * iface name is returned via the list.
+ */
+TEST_F(WifiChipHidlNanTest, GetNanIfaceNames) {
+ configureChipForIfaceType(IfaceType::NAN, true);
+
+ const auto& status_and_iface_names1 =
+ HIDL_INVOKE(wifi_chip_, getNanIfaceNames);
+ ASSERT_EQ(WifiStatusCode::SUCCESS, status_and_iface_names1.first.code);
+ EXPECT_EQ(0u, status_and_iface_names1.second.size());
+
+ sp<IWifiNanIface> iface;
+ EXPECT_EQ(WifiStatusCode::SUCCESS, createNanIface(&iface));
+ EXPECT_NE(nullptr, iface.get());
+
+ std::string iface_name = getIfaceName(iface);
+ const auto& status_and_iface_names2 =
+ HIDL_INVOKE(wifi_chip_, getNanIfaceNames);
+ EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_iface_names2.first.code);
+ EXPECT_EQ(1u, status_and_iface_names2.second.size());
+ EXPECT_EQ(iface_name, status_and_iface_names2.second[0]);
+
+ EXPECT_EQ(WifiStatusCode::SUCCESS, removeNanIface(iface_name));
+ const auto& status_and_iface_names3 =
+ HIDL_INVOKE(wifi_chip_, getNanIfaceNames);
+ EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_iface_names3.first.code);
+ EXPECT_EQ(0u, status_and_iface_names3.second.size());
+}
+
+/*
+ * GetNanIface
+ * Configures the chip in NAN mode and create an iface. Then, retrieve
+ * the iface object using the correct name and ensure any other name
+ * doesn't retrieve an iface object.
+ */
+TEST_F(WifiChipHidlNanTest, GetNanIface) {
+ configureChipForIfaceType(IfaceType::NAN, true);
+
+ sp<IWifiNanIface> nan_iface;
+ EXPECT_EQ(WifiStatusCode::SUCCESS, createNanIface(&nan_iface));
+ EXPECT_NE(nullptr, nan_iface.get());
+
+ std::string iface_name = getIfaceName(nan_iface);
+ const auto& status_and_iface1 =
+ HIDL_INVOKE(wifi_chip_, getNanIface, iface_name);
+ EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_iface1.first.code);
+ EXPECT_NE(nullptr, status_and_iface1.second.get());
+
+ std::string invalid_name = iface_name + "0";
+ const auto& status_and_iface2 =
+ HIDL_INVOKE(wifi_chip_, getNanIface, invalid_name);
+ EXPECT_EQ(WifiStatusCode::ERROR_INVALID_ARGS, status_and_iface2.first.code);
+ EXPECT_EQ(nullptr, status_and_iface2.second.get());
+}
+
+/*
+ * RemoveNanIface
+ * Configures the chip in NAN mode and create an iface. Then, remove
+ * the iface object using the correct name and ensure any other name
+ * doesn't remove the iface.
+ */
+TEST_F(WifiChipHidlNanTest, RemoveNanIface) {
+ configureChipForIfaceType(IfaceType::NAN, true);
+
+ sp<IWifiNanIface> nan_iface;
+ EXPECT_EQ(WifiStatusCode::SUCCESS, createNanIface(&nan_iface));
+ EXPECT_NE(nullptr, nan_iface.get());
+
+ std::string iface_name = getIfaceName(nan_iface);
+ std::string invalid_name = iface_name + "0";
+ EXPECT_EQ(WifiStatusCode::ERROR_INVALID_ARGS, removeNanIface(invalid_name));
+
+ EXPECT_EQ(WifiStatusCode::SUCCESS, removeNanIface(iface_name));
+
+ // No such iface exists now. So, this should return failure.
+ EXPECT_EQ(WifiStatusCode::ERROR_INVALID_ARGS, removeNanIface(iface_name));
+}
diff --git a/wifi/1.0/vts/functional/wifi_chip_hidl_test.cpp b/wifi/1.0/vts/functional/wifi_chip_hidl_test.cpp
index 1b7e821..2601b78 100644
--- a/wifi/1.0/vts/functional/wifi_chip_hidl_test.cpp
+++ b/wifi/1.0/vts/functional/wifi_chip_hidl_test.cpp
@@ -36,9 +36,7 @@
using ::android::hardware::wifi::V1_0::WifiStatus;
using ::android::hardware::wifi::V1_0::WifiStatusCode;
using ::android::hardware::wifi::V1_0::IWifiChip;
-using ::android::hardware::wifi::V1_0::IWifiApIface;
using ::android::hardware::wifi::V1_0::IWifiIface;
-using ::android::hardware::wifi::V1_0::IWifiNanIface;
using ::android::hardware::wifi::V1_0::IWifiP2pIface;
using ::android::hardware::wifi::V1_0::IWifiRttController;
using ::android::hardware::wifi::V1_0::IWifiStaIface;
@@ -64,7 +62,10 @@
} // namespace
/**
- * Fixture to use for all Wifi chip HIDL interface tests.
+ * Fixture for IWifiChip tests.
+ *
+ * Tests that require SoftAP or NAN support should go into WifiChipHidlApTest or
+ * WifiChipHidlNanTest respectively.
*/
class WifiChipHidlTest : public ::testing::VtsHalHidlTargetTestBase {
public:
@@ -114,26 +115,6 @@
return status_and_name.second;
}
- WifiStatusCode createApIface(sp<IWifiApIface>* ap_iface) {
- const auto& status_and_iface = HIDL_INVOKE(wifi_chip_, createApIface);
- *ap_iface = status_and_iface.second;
- return status_and_iface.first.code;
- }
-
- WifiStatusCode removeApIface(const std::string& name) {
- return HIDL_INVOKE(wifi_chip_, removeApIface, name).code;
- }
-
- WifiStatusCode createNanIface(sp<IWifiNanIface>* nan_iface) {
- const auto& status_and_iface = HIDL_INVOKE(wifi_chip_, createNanIface);
- *nan_iface = status_and_iface.second;
- return status_and_iface.first.code;
- }
-
- WifiStatusCode removeNanIface(const std::string& name) {
- return HIDL_INVOKE(wifi_chip_, removeNanIface, name).code;
- }
-
WifiStatusCode createP2pIface(sp<IWifiP2pIface>* p2p_iface) {
const auto& status_and_iface = HIDL_INVOKE(wifi_chip_, createP2pIface);
*p2p_iface = status_and_iface.second;
@@ -360,201 +341,6 @@
}
/*
- * CreateApIface
- * Configures the chip in AP mode and ensures that at least 1 iface creation
- * succeeds.
- */
-TEST_F(WifiChipHidlTest, CreateApIface) {
- if (!gEnv->isSoftApOn) return;
- configureChipForIfaceType(IfaceType::AP, true);
-
- sp<IWifiApIface> iface;
- EXPECT_EQ(WifiStatusCode::SUCCESS, createApIface(&iface));
- EXPECT_NE(nullptr, iface.get());
-}
-
-/*
- * GetApIfaceNames
- * Configures the chip in AP mode and ensures that the iface list is empty
- * before creating the iface. Then, create the iface and ensure that
- * iface name is returned via the list.
- */
-TEST_F(WifiChipHidlTest, GetApIfaceNames) {
- if (!gEnv->isSoftApOn) return;
- configureChipForIfaceType(IfaceType::AP, true);
-
- const auto& status_and_iface_names1 =
- HIDL_INVOKE(wifi_chip_, getApIfaceNames);
- EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_iface_names1.first.code);
- EXPECT_EQ(0u, status_and_iface_names1.second.size());
-
- sp<IWifiApIface> iface;
- EXPECT_EQ(WifiStatusCode::SUCCESS, createApIface(&iface));
- EXPECT_NE(nullptr, iface.get());
-
- std::string iface_name = getIfaceName(iface);
- const auto& status_and_iface_names2 =
- HIDL_INVOKE(wifi_chip_, getApIfaceNames);
- EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_iface_names2.first.code);
- EXPECT_EQ(1u, status_and_iface_names2.second.size());
- EXPECT_EQ(iface_name, status_and_iface_names2.second[0]);
-
- EXPECT_EQ(WifiStatusCode::SUCCESS, removeApIface(iface_name));
- const auto& status_and_iface_names3 =
- HIDL_INVOKE(wifi_chip_, getApIfaceNames);
- EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_iface_names3.first.code);
- EXPECT_EQ(0u, status_and_iface_names3.second.size());
-}
-
-/*
- * GetApIface
- * Configures the chip in AP mode and create an iface. Then, retrieve
- * the iface object using the correct name and ensure any other name
- * doesn't retrieve an iface object.
- */
-TEST_F(WifiChipHidlTest, GetApIface) {
- if (!gEnv->isSoftApOn) return;
- configureChipForIfaceType(IfaceType::AP, true);
-
- sp<IWifiApIface> ap_iface;
- EXPECT_EQ(WifiStatusCode::SUCCESS, createApIface(&ap_iface));
- EXPECT_NE(nullptr, ap_iface.get());
-
- std::string iface_name = getIfaceName(ap_iface);
- const auto& status_and_iface1 =
- HIDL_INVOKE(wifi_chip_, getApIface, iface_name);
- EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_iface1.first.code);
- EXPECT_NE(nullptr, status_and_iface1.second.get());
-
- std::string invalid_name = iface_name + "0";
- const auto& status_and_iface2 =
- HIDL_INVOKE(wifi_chip_, getApIface, invalid_name);
- EXPECT_EQ(WifiStatusCode::ERROR_INVALID_ARGS, status_and_iface2.first.code);
- EXPECT_EQ(nullptr, status_and_iface2.second.get());
-}
-
-/*
- * RemoveApIface
- * Configures the chip in AP mode and create an iface. Then, remove
- * the iface object using the correct name and ensure any other name
- * doesn't remove the iface.
- */
-TEST_F(WifiChipHidlTest, RemoveApIface) {
- if (!gEnv->isSoftApOn) return;
- configureChipForIfaceType(IfaceType::AP, true);
-
- sp<IWifiApIface> ap_iface;
- EXPECT_EQ(WifiStatusCode::SUCCESS, createApIface(&ap_iface));
- EXPECT_NE(nullptr, ap_iface.get());
-
- std::string iface_name = getIfaceName(ap_iface);
- std::string invalid_name = iface_name + "0";
- EXPECT_EQ(WifiStatusCode::ERROR_INVALID_ARGS, removeApIface(invalid_name));
- EXPECT_EQ(WifiStatusCode::SUCCESS, removeApIface(iface_name));
-
- // No such iface exists now. So, this should return failure.
- EXPECT_EQ(WifiStatusCode::ERROR_INVALID_ARGS, removeApIface(iface_name));
-}
-
-/*
- * CreateNanIface
- * Configures the chip in NAN mode and ensures that at least 1 iface creation
- * succeeds.
- */
-TEST_F(WifiChipHidlTest, CreateNanIface) {
- if (!gEnv->isNanOn) return;
- configureChipForIfaceType(IfaceType::NAN, gEnv->isNanOn);
-
- sp<IWifiNanIface> iface;
- ASSERT_EQ(WifiStatusCode::SUCCESS, createNanIface(&iface));
- EXPECT_NE(nullptr, iface.get());
-}
-
-/*
- * GetNanIfaceNames
- * Configures the chip in NAN mode and ensures that the iface list is empty
- * before creating the iface. Then, create the iface and ensure that
- * iface name is returned via the list.
- */
-TEST_F(WifiChipHidlTest, GetNanIfaceNames) {
- if (!gEnv->isNanOn) return;
- configureChipForIfaceType(IfaceType::NAN, gEnv->isNanOn);
-
- const auto& status_and_iface_names1 =
- HIDL_INVOKE(wifi_chip_, getNanIfaceNames);
- ASSERT_EQ(WifiStatusCode::SUCCESS, status_and_iface_names1.first.code);
- EXPECT_EQ(0u, status_and_iface_names1.second.size());
-
- sp<IWifiNanIface> iface;
- EXPECT_EQ(WifiStatusCode::SUCCESS, createNanIface(&iface));
- EXPECT_NE(nullptr, iface.get());
-
- std::string iface_name = getIfaceName(iface);
- const auto& status_and_iface_names2 =
- HIDL_INVOKE(wifi_chip_, getNanIfaceNames);
- EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_iface_names2.first.code);
- EXPECT_EQ(1u, status_and_iface_names2.second.size());
- EXPECT_EQ(iface_name, status_and_iface_names2.second[0]);
-
- EXPECT_EQ(WifiStatusCode::SUCCESS, removeNanIface(iface_name));
- const auto& status_and_iface_names3 =
- HIDL_INVOKE(wifi_chip_, getNanIfaceNames);
- EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_iface_names3.first.code);
- EXPECT_EQ(0u, status_and_iface_names3.second.size());
-}
-
-/*
- * GetNanIface
- * Configures the chip in NAN mode and create an iface. Then, retrieve
- * the iface object using the correct name and ensure any other name
- * doesn't retrieve an iface object.
- */
-TEST_F(WifiChipHidlTest, GetNanIface) {
- if (!gEnv->isNanOn) return;
- configureChipForIfaceType(IfaceType::NAN, gEnv->isNanOn);
-
- sp<IWifiNanIface> nan_iface;
- EXPECT_EQ(WifiStatusCode::SUCCESS, createNanIface(&nan_iface));
- EXPECT_NE(nullptr, nan_iface.get());
-
- std::string iface_name = getIfaceName(nan_iface);
- const auto& status_and_iface1 =
- HIDL_INVOKE(wifi_chip_, getNanIface, iface_name);
- EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_iface1.first.code);
- EXPECT_NE(nullptr, status_and_iface1.second.get());
-
- std::string invalid_name = iface_name + "0";
- const auto& status_and_iface2 =
- HIDL_INVOKE(wifi_chip_, getNanIface, invalid_name);
- EXPECT_EQ(WifiStatusCode::ERROR_INVALID_ARGS, status_and_iface2.first.code);
- EXPECT_EQ(nullptr, status_and_iface2.second.get());
-}
-
-/*
- * RemoveNanIface
- * Configures the chip in NAN mode and create an iface. Then, remove
- * the iface object using the correct name and ensure any other name
- * doesn't remove the iface.
- */
-TEST_F(WifiChipHidlTest, RemoveNanIface) {
- if (!gEnv->isNanOn) return;
- configureChipForIfaceType(IfaceType::NAN, gEnv->isNanOn);
-
- sp<IWifiNanIface> nan_iface;
- EXPECT_EQ(WifiStatusCode::SUCCESS, createNanIface(&nan_iface));
- EXPECT_NE(nullptr, nan_iface.get());
-
- std::string iface_name = getIfaceName(nan_iface);
- std::string invalid_name = iface_name + "0";
- EXPECT_EQ(WifiStatusCode::ERROR_INVALID_ARGS, removeNanIface(invalid_name));
-
- EXPECT_EQ(WifiStatusCode::SUCCESS, removeNanIface(iface_name));
-
- // No such iface exists now. So, this should return failure.
- EXPECT_EQ(WifiStatusCode::ERROR_INVALID_ARGS, removeNanIface(iface_name));
-}
-
-/*
* CreateP2pIface
* Configures the chip in P2P mode and ensures that at least 1 iface creation
* succeeds.
diff --git a/wifi/1.0/vts/functional/wifi_hidl_test_utils.h b/wifi/1.0/vts/functional/wifi_hidl_test_utils.h
index d430ce0..7dacaf1 100644
--- a/wifi/1.0/vts/functional/wifi_hidl_test_utils.h
+++ b/wifi/1.0/vts/functional/wifi_hidl_test_utils.h
@@ -54,48 +54,4 @@
stopWifi();
sleep(5);
}
-
- public:
- // Whether NaN feature is supported on the device.
- bool isNanOn = false;
- // Whether SoftAp feature is supported on the device.
- bool isSoftApOn = false;
-
- void usage(char* me, char* arg) {
- fprintf(stderr,
- "unrecognized option: %s\n\n"
- "usage: %s <gtest options> <test options>\n\n"
- "test options are:\n\n"
- "-N, --nan_on: Whether NAN feature is supported\n"
- "-S, --softap_on: Whether SOFTAP feature is supported\n",
- arg, me);
- }
-
- int initFromOptions(int argc, char** argv) {
- static struct option options[] = {{"nan_on", no_argument, 0, 'N'},
- {"softap_on", no_argument, 0, 'S'},
- {0, 0, 0, 0}};
-
- int c;
- while ((c = getopt_long(argc, argv, "NS", options, NULL)) >= 0) {
- switch (c) {
- case 'N':
- isNanOn = true;
- break;
- case 'S':
- isSoftApOn = true;
- break;
- default:
- usage(argv[0], argv[optind]);
- return 2;
- }
- }
-
- if (optind < argc) {
- usage(argv[0], argv[optind]);
- return 2;
- }
-
- return 0;
- }
};
diff --git a/wifi/1.1/vts/functional/VtsHalWifiV1_1TargetTest.cpp b/wifi/1.1/vts/functional/VtsHalWifiV1_1TargetTest.cpp
index a0f97f8..673fed3 100644
--- a/wifi/1.1/vts/functional/VtsHalWifiV1_1TargetTest.cpp
+++ b/wifi/1.1/vts/functional/VtsHalWifiV1_1TargetTest.cpp
@@ -41,10 +41,7 @@
::testing::AddGlobalTestEnvironment(gEnv);
::testing::InitGoogleTest(&argc, argv);
gEnv->init(&argc, argv);
- int status = gEnv->initFromOptions(argc, argv);
- if (status == 0) {
- int status = RUN_ALL_TESTS();
- LOG(INFO) << "Test result = " << status;
- }
+ int status = RUN_ALL_TESTS();
+ LOG(INFO) << "Test result = " << status;
return status;
}
diff --git a/wifi/1.3/vts/functional/wifi_sta_iface_hidl_test.cpp b/wifi/1.3/vts/functional/wifi_sta_iface_hidl_test.cpp
index 71e90ac..d382f30 100644
--- a/wifi/1.3/vts/functional/wifi_sta_iface_hidl_test.cpp
+++ b/wifi/1.3/vts/functional/wifi_sta_iface_hidl_test.cpp
@@ -27,6 +27,8 @@
#include "wifi_hidl_test_utils.h"
using ::android::sp;
+using ::android::hardware::hidl_array;
+using ::android::hardware::wifi::V1_0::WifiStatus;
using ::android::hardware::wifi::V1_0::WifiStatusCode;
using ::android::hardware::wifi::V1_3::IWifiStaIface;
@@ -59,14 +61,11 @@
* and return a success status code.
*/
TEST_F(WifiStaIfaceHidlTest, GetFactoryMacAddress) {
- const auto& status_and_mac =
+ std::pair<WifiStatus, hidl_array<uint8_t, 6> > status_and_mac =
HIDL_INVOKE(wifi_sta_iface_, getFactoryMacAddress);
EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_mac.first.code);
- const int num_elements = sizeof(status_and_mac.second) / sizeof(uint8_t);
- EXPECT_EQ(6, num_elements);
- for (int i = 0; i < num_elements; i++) {
- EXPECT_NE(0, status_and_mac.second[i]);
- }
+ hidl_array<uint8_t, 6> all_zero{};
+ EXPECT_NE(all_zero, status_and_mac.second);
}
/*
diff --git a/wifi/1.4/Android.bp b/wifi/1.4/Android.bp
index a6ac020..aba8b44 100644
--- a/wifi/1.4/Android.bp
+++ b/wifi/1.4/Android.bp
@@ -8,6 +8,7 @@
},
srcs: [
"IWifi.hal",
+ "IWifiApIface.hal",
],
interfaces: [
"android.hardware.wifi@1.0",
diff --git a/wifi/1.4/IWifiApIface.hal b/wifi/1.4/IWifiApIface.hal
new file mode 100644
index 0000000..af88afb
--- /dev/null
+++ b/wifi/1.4/IWifiApIface.hal
@@ -0,0 +1,53 @@
+/*
+ * Copyright 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.wifi@1.4;
+
+import @1.0::IWifiApIface;
+import @1.0::MacAddress;
+import @1.0::WifiStatus;
+
+/**
+ * Represents a network interface in AP mode.
+ *
+ * This can be obtained through @1.0::IWifiChip.getApIface() and casting
+ * IWifiApIface up to 1.4.
+ */
+interface IWifiApIface extends @1.0::IWifiApIface {
+ /**
+ * Changes the MAC address of the interface to the given MAC address.
+ *
+ * @param mac MAC address to change to.
+ * @return status WifiStatus of the operation.
+ * Possible status codes:
+ * |WifiStatusCode.SUCCESS|,
+ * |WifiStatusCode.ERROR_WIFI_IFACE_INVALID|,
+ * |WifiStatusCode.ERROR_UNKNOWN|
+ */
+ setMacAddress(MacAddress mac) generates (WifiStatus status);
+
+ /**
+ * Gets the factory MAC address of the interface.
+ *
+ * @return status WifiStatus of the operation
+ * Possible status codes:
+ * |WifiStatusCode.SUCCESS|,
+ * |WifiStatusCode.ERROR_WIFI_IFACE_INVALID|,
+ * |WifiStatusCode.ERROR_UNKNOWN|
+ * @return mac factory MAC address of the interface
+ */
+ getFactoryMacAddress() generates (WifiStatus status, MacAddress mac);
+};
diff --git a/wifi/1.4/default/tests/wifi_chip_unit_tests.cpp b/wifi/1.4/default/tests/wifi_chip_unit_tests.cpp
index 2ad093a..b0357ba 100644
--- a/wifi/1.4/default/tests/wifi_chip_unit_tests.cpp
+++ b/wifi/1.4/default/tests/wifi_chip_unit_tests.cpp
@@ -173,8 +173,9 @@
std::string createIface(const IfaceType& type) {
std::string iface_name;
if (type == IfaceType::AP) {
- chip_->createApIface([&iface_name](const WifiStatus& status,
- const sp<IWifiApIface>& iface) {
+ chip_->createApIface([&iface_name](
+ const WifiStatus& status,
+ const sp<V1_0::IWifiApIface>& iface) {
if (WifiStatusCode::SUCCESS == status.code) {
ASSERT_NE(iface.get(), nullptr);
iface->getName([&iface_name](const WifiStatus& status,
diff --git a/wifi/1.4/default/wifi_ap_iface.cpp b/wifi/1.4/default/wifi_ap_iface.cpp
index 13ce2dd..b860910 100644
--- a/wifi/1.4/default/wifi_ap_iface.cpp
+++ b/wifi/1.4/default/wifi_ap_iface.cpp
@@ -85,6 +85,20 @@
hidl_status_cb, band);
}
+Return<void> WifiApIface::setMacAddress(const hidl_array<uint8_t, 6>& mac,
+ setMacAddress_cb hidl_status_cb) {
+ return validateAndCall(this, WifiStatusCode::ERROR_WIFI_IFACE_INVALID,
+ &WifiApIface::setMacAddressInternal, hidl_status_cb,
+ mac);
+}
+
+Return<void> WifiApIface::getFactoryMacAddress(
+ getFactoryMacAddress_cb hidl_status_cb) {
+ return validateAndCall(this, WifiStatusCode::ERROR_WIFI_IFACE_INVALID,
+ &WifiApIface::getFactoryMacAddressInternal,
+ hidl_status_cb);
+}
+
std::pair<WifiStatus, std::string> WifiApIface::getNameInternal() {
return {createWifiStatus(WifiStatusCode::SUCCESS), ifname_};
}
@@ -111,6 +125,26 @@
ifname_, hidl_struct_util::convertHidlWifiBandToLegacy(band));
return {createWifiStatusFromLegacyError(legacy_status), valid_frequencies};
}
+
+WifiStatus WifiApIface::setMacAddressInternal(
+ const std::array<uint8_t, 6>& mac) {
+ bool status = iface_util_.lock()->setMacAddress(ifname_, mac);
+ if (!status) {
+ return createWifiStatus(WifiStatusCode::ERROR_UNKNOWN);
+ }
+ return createWifiStatus(WifiStatusCode::SUCCESS);
+}
+
+std::pair<WifiStatus, std::array<uint8_t, 6>>
+WifiApIface::getFactoryMacAddressInternal() {
+ std::array<uint8_t, 6> mac =
+ iface_util_.lock()->getFactoryMacAddress(ifname_);
+ if (mac[0] == 0 && mac[1] == 0 && mac[2] == 0 && mac[3] == 0 &&
+ mac[4] == 0 && mac[5] == 0) {
+ return {createWifiStatus(WifiStatusCode::ERROR_UNKNOWN), mac};
+ }
+ return {createWifiStatus(WifiStatusCode::SUCCESS), mac};
+}
} // namespace implementation
} // namespace V1_4
} // namespace wifi
diff --git a/wifi/1.4/default/wifi_ap_iface.h b/wifi/1.4/default/wifi_ap_iface.h
index 179acac..cb3ed3d 100644
--- a/wifi/1.4/default/wifi_ap_iface.h
+++ b/wifi/1.4/default/wifi_ap_iface.h
@@ -18,7 +18,7 @@
#define WIFI_AP_IFACE_H_
#include <android-base/macros.h>
-#include <android/hardware/wifi/1.0/IWifiApIface.h>
+#include <android/hardware/wifi/1.4/IWifiApIface.h>
#include "wifi_feature_flags.h"
#include "wifi_iface_util.h"
@@ -34,7 +34,7 @@
/**
* HIDL interface object used to control a AP Iface instance.
*/
-class WifiApIface : public V1_0::IWifiApIface {
+class WifiApIface : public V1_4::IWifiApIface {
public:
WifiApIface(
const std::string& ifname,
@@ -53,6 +53,10 @@
setCountryCode_cb hidl_status_cb) override;
Return<void> getValidFrequenciesForBand(
WifiBand band, getValidFrequenciesForBand_cb hidl_status_cb) override;
+ Return<void> setMacAddress(const hidl_array<uint8_t, 6>& mac,
+ setMacAddress_cb hidl_status_cb) override;
+ Return<void> getFactoryMacAddress(
+ getFactoryMacAddress_cb hidl_status_cb) override;
private:
// Corresponding worker functions for the HIDL methods.
@@ -61,6 +65,9 @@
WifiStatus setCountryCodeInternal(const std::array<int8_t, 2>& code);
std::pair<WifiStatus, std::vector<WifiChannelInMhz>>
getValidFrequenciesForBandInternal(WifiBand band);
+ WifiStatus setMacAddressInternal(const std::array<uint8_t, 6>& mac);
+ std::pair<WifiStatus, std::array<uint8_t, 6>>
+ getFactoryMacAddressInternal();
std::string ifname_;
std::weak_ptr<legacy_hal::WifiLegacyHal> legacy_hal_;
diff --git a/wifi/1.4/vts/OWNERS b/wifi/1.4/vts/OWNERS
new file mode 100644
index 0000000..8bfb148
--- /dev/null
+++ b/wifi/1.4/vts/OWNERS
@@ -0,0 +1,2 @@
+rpius@google.com
+etancohen@google.com
diff --git a/wifi/1.4/vts/functional/Android.bp b/wifi/1.4/vts/functional/Android.bp
new file mode 100644
index 0000000..42c60f2
--- /dev/null
+++ b/wifi/1.4/vts/functional/Android.bp
@@ -0,0 +1,33 @@
+//
+// 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.
+//
+
+// SoftAP-specific tests, similar to VtsHalWifiApV1_0TargetTest.
+cc_test {
+ name: "VtsHalWifiApV1_4TargetTest",
+ defaults: ["VtsHalTargetTestDefaults"],
+ srcs: [
+ "VtsHalWifiV1_4TargetTest.cpp",
+ "wifi_ap_iface_hidl_test.cpp",
+ ],
+ static_libs: [
+ "VtsHalWifiV1_0TargetTestUtil",
+ "android.hardware.wifi@1.0",
+ "android.hardware.wifi@1.1",
+ "android.hardware.wifi@1.2",
+ "android.hardware.wifi@1.3",
+ "android.hardware.wifi@1.4",
+ ],
+}
diff --git a/wifi/1.4/vts/functional/VtsHalWifiV1_4TargetTest.cpp b/wifi/1.4/vts/functional/VtsHalWifiV1_4TargetTest.cpp
new file mode 100644
index 0000000..deac0fa
--- /dev/null
+++ b/wifi/1.4/vts/functional/VtsHalWifiV1_4TargetTest.cpp
@@ -0,0 +1,50 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <android-base/logging.h>
+#include <android/hardware/wifi/1.4/IWifi.h>
+
+#include "wifi_hidl_test_utils.h"
+
+using ::android::hardware::wifi::V1_4::IWifi;
+
+// Test environment for Wifi HIDL HAL.
+class WifiHidlEnvironment_1_4 : public WifiHidlEnvironment {
+ public:
+ // get the test environment singleton
+ static WifiHidlEnvironment_1_4* Instance() {
+ static WifiHidlEnvironment_1_4* instance = new WifiHidlEnvironment_1_4;
+ return instance;
+ }
+
+ virtual void registerTestServices() override {
+ registerTestService<android::hardware::wifi::V1_4::IWifi>();
+ }
+
+ private:
+ WifiHidlEnvironment_1_4() {}
+};
+
+WifiHidlEnvironment_1_4* gEnv = WifiHidlEnvironment_1_4::Instance();
+
+int main(int argc, char** argv) {
+ ::testing::AddGlobalTestEnvironment(gEnv);
+ ::testing::InitGoogleTest(&argc, argv);
+ gEnv->init(&argc, argv);
+ int status = RUN_ALL_TESTS();
+ LOG(INFO) << "Test result = " << status;
+ return status;
+}
diff --git a/wifi/1.4/vts/functional/wifi_ap_iface_hidl_test.cpp b/wifi/1.4/vts/functional/wifi_ap_iface_hidl_test.cpp
new file mode 100644
index 0000000..68e9bbb
--- /dev/null
+++ b/wifi/1.4/vts/functional/wifi_ap_iface_hidl_test.cpp
@@ -0,0 +1,72 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Staache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <android/hardware/wifi/1.4/IWifiApIface.h>
+
+#include <VtsHalHidlTargetTestBase.h>
+
+#include "wifi_hidl_call_util.h"
+#include "wifi_hidl_test_utils.h"
+
+using ::android::sp;
+using ::android::hardware::hidl_array;
+using ::android::hardware::wifi::V1_0::WifiStatus;
+using ::android::hardware::wifi::V1_0::WifiStatusCode;
+using ::android::hardware::wifi::V1_4::IWifiApIface;
+
+extern WifiHidlEnvironment* gEnv;
+
+/**
+ * Fixture to use for all STA Iface HIDL interface tests.
+ */
+class WifiApIfaceHidlTest : public ::testing::VtsHalHidlTargetTestBase {
+ public:
+ virtual void SetUp() override {
+ wifi_ap_iface_ = IWifiApIface::castFrom(getWifiApIface());
+ ASSERT_NE(nullptr, wifi_ap_iface_.get());
+ }
+
+ virtual void TearDown() override {
+ stopWifi();
+ }
+
+ protected:
+ sp<IWifiApIface> wifi_ap_iface_;
+};
+
+/*
+ * SetMacAddress:
+ * Ensures that calls to set MAC address will return a success status
+ * code.
+ */
+TEST_F(WifiApIfaceHidlTest, SetMacAddress) {
+ const hidl_array<uint8_t, 6> kMac{{0x12, 0x22, 0x33, 0x52, 0x10, 0x41}};
+ EXPECT_EQ(WifiStatusCode::SUCCESS,
+ HIDL_INVOKE(wifi_ap_iface_, setMacAddress, kMac).code);
+}
+
+/*
+ * GetFactoryMacAddress:
+ * Ensures that calls to get factory MAC address will retrieve a non-zero MAC
+ * and return a success status code.
+ */
+TEST_F(WifiApIfaceHidlTest, GetFactoryMacAddress) {
+ std::pair<WifiStatus, hidl_array<uint8_t, 6> > status_and_mac =
+ HIDL_INVOKE(wifi_ap_iface_, getFactoryMacAddress);
+ EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_mac.first.code);
+ hidl_array<uint8_t, 6> all_zero{};
+ EXPECT_NE(all_zero, status_and_mac.second);
+}
diff --git a/wifi/supplicant/1.3/Android.bp b/wifi/supplicant/1.3/Android.bp
index 6633d9d..3f20531 100644
--- a/wifi/supplicant/1.3/Android.bp
+++ b/wifi/supplicant/1.3/Android.bp
@@ -9,6 +9,8 @@
srcs: [
"types.hal",
"ISupplicant.hal",
+ "ISupplicantStaIface.hal",
+ "ISupplicantStaIfaceCallback.hal",
"ISupplicantStaNetwork.hal",
],
interfaces: [
diff --git a/wifi/supplicant/1.3/ISupplicantStaIface.hal b/wifi/supplicant/1.3/ISupplicantStaIface.hal
new file mode 100644
index 0000000..62b4033
--- /dev/null
+++ b/wifi/supplicant/1.3/ISupplicantStaIface.hal
@@ -0,0 +1,46 @@
+/*
+ * Copyright 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.wifi.supplicant@1.3;
+
+import @1.0::SupplicantStatus;
+import @1.2::ISupplicantStaIface;
+import @1.3::ISupplicantStaIfaceCallback;
+
+/**
+ * Interface exposed by the supplicant for each station mode network
+ * interface (e.g wlan0) it controls.
+ */
+interface ISupplicantStaIface extends @1.2::ISupplicantStaIface {
+ /**
+ * Register for callbacks from this interface.
+ *
+ * These callbacks are invoked for events that are specific to this interface.
+ * Registration of multiple callback objects is supported. These objects must
+ * be automatically deleted when the corresponding client process is dead or
+ * if this interface is removed.
+ *
+ * @param callback An instance of the |ISupplicantStaIfaceCallback| HIDL
+ * interface object.
+ * @return status Status of the operation.
+ * Possible status codes:
+ * |SupplicantStatusCode.SUCCESS|,
+ * |SupplicantStatusCode.FAILURE_UNKNOWN|,
+ * |SupplicantStatusCode.FAILURE_IFACE_INVALID|
+ */
+ registerCallback_1_3(ISupplicantStaIfaceCallback callback)
+ generates (SupplicantStatus status);
+};
diff --git a/wifi/supplicant/1.3/ISupplicantStaIfaceCallback.hal b/wifi/supplicant/1.3/ISupplicantStaIfaceCallback.hal
new file mode 100644
index 0000000..107e0fc
--- /dev/null
+++ b/wifi/supplicant/1.3/ISupplicantStaIfaceCallback.hal
@@ -0,0 +1,38 @@
+/*
+ * Copyright 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.wifi.supplicant@1.3;
+
+import @1.2::ISupplicantStaIfaceCallback;
+
+/**
+ * Callback Interface exposed by the supplicant service
+ * for each station mode interface (ISupplicantStaIface).
+ *
+ * Clients need to host an instance of this HIDL interface object and
+ * pass a reference of the object to the supplicant via the
+ * corresponding |ISupplicantStaIface.registerCallback_1_3| method.
+ */
+interface ISupplicantStaIfaceCallback extends @1.2::ISupplicantStaIfaceCallback {
+ /**
+ * Indicates PMK cache added event.
+ *
+ * @param expirationTimeInSec expiration time in seconds
+ * @param serializedEntry is serialized PMK cache entry, the content is
+ * opaque for the framework and depends on the native implementation.
+ */
+ oneway onPmkCacheAdded(int64_t expirationTimeInSec, vec<uint8_t> serializedEntry);
+};
diff --git a/wifi/supplicant/1.3/ISupplicantStaNetwork.hal b/wifi/supplicant/1.3/ISupplicantStaNetwork.hal
index eb9de9a..5e265c6 100644
--- a/wifi/supplicant/1.3/ISupplicantStaNetwork.hal
+++ b/wifi/supplicant/1.3/ISupplicantStaNetwork.hal
@@ -47,4 +47,19 @@
* @return ocspType ocsp type.
*/
getOcsp() generates (SupplicantStatus status, OcspType ocspType);
+
+ /**
+ * Add a PMK into supplicant PMK cache.
+ *
+ * @param serializedEntry is serialized PMK cache entry, the content is
+ * opaque for the framework and depends on the native implementation.
+ * @return status Status of the operation
+ * Possible status codes:
+ * |SupplicantStatusCode.SUCCESS|,
+ * |SupplicantStatusCode.FAILURE_ARGS_INVALID|,
+ * |SupplicantStatusCode.FAILURE_UNKNOWN|,
+ * |SupplicantStatusCode.FAILURE_NETWORK_INVALID|
+ */
+ setPmkCache(vec<uint8_t> serializedEntry)
+ generates (SupplicantStatus status);
};
diff --git a/wifi/supplicant/1.3/vts/functional/Android.bp b/wifi/supplicant/1.3/vts/functional/Android.bp
index 67c7348..abb8600 100644
--- a/wifi/supplicant/1.3/vts/functional/Android.bp
+++ b/wifi/supplicant/1.3/vts/functional/Android.bp
@@ -42,6 +42,7 @@
defaults: ["VtsHalTargetTestDefaults"],
srcs: [
"VtsHalWifiSupplicantV1_3TargetTest.cpp",
+ "supplicant_sta_iface_hidl_test.cpp",
"supplicant_sta_network_hidl_test.cpp",
],
static_libs: [
diff --git a/wifi/supplicant/1.3/vts/functional/supplicant_hidl_test_utils_1_3.cpp b/wifi/supplicant/1.3/vts/functional/supplicant_hidl_test_utils_1_3.cpp
index 86959eb..308808d 100644
--- a/wifi/supplicant/1.3/vts/functional/supplicant_hidl_test_utils_1_3.cpp
+++ b/wifi/supplicant/1.3/vts/functional/supplicant_hidl_test_utils_1_3.cpp
@@ -21,8 +21,13 @@
#include "supplicant_hidl_test_utils_1_3.h"
using ::android::sp;
+using ::android::hardware::wifi::supplicant::V1_3::ISupplicantStaIface;
using ::android::hardware::wifi::supplicant::V1_3::ISupplicantStaNetwork;
+sp<ISupplicantStaIface> getSupplicantStaIface_1_3() {
+ return ISupplicantStaIface::castFrom(getSupplicantStaIface());
+}
+
sp<ISupplicantStaNetwork> createSupplicantStaNetwork_1_3() {
return ISupplicantStaNetwork::castFrom(createSupplicantStaNetwork());
}
diff --git a/wifi/supplicant/1.3/vts/functional/supplicant_hidl_test_utils_1_3.h b/wifi/supplicant/1.3/vts/functional/supplicant_hidl_test_utils_1_3.h
index 8e64162..39dbb8f 100644
--- a/wifi/supplicant/1.3/vts/functional/supplicant_hidl_test_utils_1_3.h
+++ b/wifi/supplicant/1.3/vts/functional/supplicant_hidl_test_utils_1_3.h
@@ -17,8 +17,11 @@
#ifndef SUPPLICANT_HIDL_TEST_UTILS_1_3_H
#define SUPPLICANT_HIDL_TEST_UTILS_1_3_H
+#include <android/hardware/wifi/supplicant/1.3/ISupplicantStaIface.h>
#include <android/hardware/wifi/supplicant/1.3/ISupplicantStaNetwork.h>
+android::sp<android::hardware::wifi::supplicant::V1_3::ISupplicantStaIface>
+getSupplicantStaIface_1_3();
android::sp<android::hardware::wifi::supplicant::V1_3::ISupplicantStaNetwork>
createSupplicantStaNetwork_1_3();
diff --git a/wifi/supplicant/1.3/vts/functional/supplicant_sta_iface_hidl_test.cpp b/wifi/supplicant/1.3/vts/functional/supplicant_sta_iface_hidl_test.cpp
new file mode 100644
index 0000000..9b68a47
--- /dev/null
+++ b/wifi/supplicant/1.3/vts/functional/supplicant_sta_iface_hidl_test.cpp
@@ -0,0 +1,181 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <VtsHalHidlTargetTestBase.h>
+#include <android/hardware/wifi/supplicant/1.2/types.h>
+#include <android/hardware/wifi/supplicant/1.3/ISupplicantStaIface.h>
+#include <android/hardware/wifi/supplicant/1.3/ISupplicantStaIfaceCallback.h>
+#include <android/hardware/wifi/supplicant/1.3/ISupplicantStaNetwork.h>
+#include <android/hardware/wifi/supplicant/1.3/types.h>
+#include <hidl/HidlSupport.h>
+#include <hidl/Status.h>
+
+#include "supplicant_hidl_test_utils.h"
+#include "supplicant_hidl_test_utils_1_3.h"
+
+using ::android::sp;
+using ::android::hardware::hidl_array;
+using ::android::hardware::hidl_string;
+using ::android::hardware::hidl_vec;
+using ::android::hardware::Return;
+using ::android::hardware::Void;
+using ::android::hardware::wifi::supplicant::V1_0::SupplicantStatus;
+using ::android::hardware::wifi::supplicant::V1_0::SupplicantStatusCode;
+using ::android::hardware::wifi::supplicant::V1_2::DppAkm;
+using ::android::hardware::wifi::supplicant::V1_2::DppFailureCode;
+using ::android::hardware::wifi::supplicant::V1_2::DppProgressCode;
+using ::android::hardware::wifi::supplicant::V1_3::ISupplicantStaIface;
+using ::android::hardware::wifi::supplicant::V1_3::ISupplicantStaIfaceCallback;
+using ::android::hardware::wifi::supplicant::V1_3::ISupplicantStaNetwork;
+
+class SupplicantStaIfaceHidlTest : public ::testing::VtsHalHidlTargetTestBase {
+ public:
+ virtual void SetUp() override {
+ startSupplicantAndWaitForHidlService();
+ EXPECT_TRUE(turnOnExcessiveLogging());
+ sta_iface_ = getSupplicantStaIface_1_3();
+ ASSERT_NE(sta_iface_.get(), nullptr);
+ }
+
+ virtual void TearDown() override { stopSupplicant(); }
+
+ int64_t pmkCacheExpirationTimeInSec;
+ std::vector<uint8_t> serializedPmkCacheEntry;
+
+ protected:
+ // ISupplicantStaIface object used for all tests in this fixture.
+ sp<ISupplicantStaIface> sta_iface_;
+};
+
+class IfaceCallback : public ISupplicantStaIfaceCallback {
+ Return<void> onNetworkAdded(uint32_t /* id */) override { return Void(); }
+ Return<void> onNetworkRemoved(uint32_t /* id */) override { return Void(); }
+ Return<void> onStateChanged(
+ ISupplicantStaIfaceCallback::State /* newState */,
+ const hidl_array<uint8_t, 6>& /*bssid */, uint32_t /* id */,
+ const hidl_vec<uint8_t>& /* ssid */) override {
+ return Void();
+ }
+ Return<void> onAnqpQueryDone(
+ const hidl_array<uint8_t, 6>& /* bssid */,
+ const ISupplicantStaIfaceCallback::AnqpData& /* data */,
+ const ISupplicantStaIfaceCallback::Hs20AnqpData& /* hs20Data */)
+ override {
+ return Void();
+ }
+ virtual Return<void> onHs20IconQueryDone(
+ const hidl_array<uint8_t, 6>& /* bssid */,
+ const hidl_string& /* fileName */,
+ const hidl_vec<uint8_t>& /* data */) override {
+ return Void();
+ }
+ virtual Return<void> onHs20SubscriptionRemediation(
+ const hidl_array<uint8_t, 6>& /* bssid */,
+ ISupplicantStaIfaceCallback::OsuMethod /* osuMethod */,
+ const hidl_string& /* url*/) override {
+ return Void();
+ }
+ Return<void> onHs20DeauthImminentNotice(
+ const hidl_array<uint8_t, 6>& /* bssid */, uint32_t /* reasonCode */,
+ uint32_t /* reAuthDelayInSec */,
+ const hidl_string& /* url */) override {
+ return Void();
+ }
+ Return<void> onDisconnected(const hidl_array<uint8_t, 6>& /* bssid */,
+ bool /* locallyGenerated */,
+ ISupplicantStaIfaceCallback::ReasonCode
+ /* reasonCode */) override {
+ return Void();
+ }
+ Return<void> onAssociationRejected(
+ const hidl_array<uint8_t, 6>& /* bssid */,
+ ISupplicantStaIfaceCallback::StatusCode /* statusCode */,
+ bool /*timedOut */) override {
+ return Void();
+ }
+ Return<void> onAuthenticationTimeout(
+ const hidl_array<uint8_t, 6>& /* bssid */) override {
+ return Void();
+ }
+ Return<void> onBssidChanged(
+ ISupplicantStaIfaceCallback::BssidChangeReason /* reason */,
+ const hidl_array<uint8_t, 6>& /* bssid */) override {
+ return Void();
+ }
+ Return<void> onEapFailure() override { return Void(); }
+ Return<void> onEapFailure_1_1(
+ ISupplicantStaIfaceCallback::EapErrorCode /* eapErrorCode */) override {
+ return Void();
+ }
+ Return<void> onWpsEventSuccess() override { return Void(); }
+ Return<void> onWpsEventFail(
+ const hidl_array<uint8_t, 6>& /* bssid */,
+ ISupplicantStaIfaceCallback::WpsConfigError /* configError */,
+ ISupplicantStaIfaceCallback::WpsErrorIndication /* errorInd */)
+ override {
+ return Void();
+ }
+ Return<void> onWpsEventPbcOverlap() override { return Void(); }
+ Return<void> onExtRadioWorkStart(uint32_t /* id */) override {
+ return Void();
+ }
+ Return<void> onExtRadioWorkTimeout(uint32_t /* id*/) override {
+ return Void();
+ }
+ Return<void> onDppSuccessConfigReceived(
+ const hidl_vec<uint8_t>& /* ssid */, const hidl_string& /* password */,
+ const hidl_array<uint8_t, 32>& /* psk */,
+ DppAkm /* securityAkm */) override {
+ return Void();
+ }
+ Return<void> onDppSuccessConfigSent() override { return Void(); }
+ Return<void> onDppProgress(DppProgressCode /* code */) override {
+ return Void();
+ }
+ Return<void> onDppFailure(DppFailureCode /* code */) override {
+ return Void();
+ }
+ Return<void> onPmkCacheAdded(
+ int64_t /* expirationTimeInSec */,
+ const hidl_vec<uint8_t>& /* serializedEntry */) override {
+ return Void();
+ }
+};
+
+class IfacePmkCacheCallback : public IfaceCallback {
+ SupplicantStaIfaceHidlTest& parent_;
+ Return<void> onPmkCacheAdded(
+ int64_t expirationTimeInSec,
+ const hidl_vec<uint8_t>& serializedEntry) override {
+ parent_.pmkCacheExpirationTimeInSec = expirationTimeInSec;
+ parent_.serializedPmkCacheEntry = serializedEntry;
+ return Void();
+ }
+
+ public:
+ IfacePmkCacheCallback(SupplicantStaIfaceHidlTest& parent)
+ : parent_(parent) {}
+};
+
+/*
+ * RegisterCallback_1_3
+ */
+TEST_F(SupplicantStaIfaceHidlTest, RegisterCallback_1_3) {
+ sta_iface_->registerCallback_1_3(
+ new IfaceCallback(), [](const SupplicantStatus& status) {
+ EXPECT_EQ(SupplicantStatusCode::SUCCESS, status.code);
+ });
+}
diff --git a/wifi/supplicant/1.3/vts/functional/supplicant_sta_network_hidl_test.cpp b/wifi/supplicant/1.3/vts/functional/supplicant_sta_network_hidl_test.cpp
index e5be0cc..07bc9d8 100644
--- a/wifi/supplicant/1.3/vts/functional/supplicant_sta_network_hidl_test.cpp
+++ b/wifi/supplicant/1.3/vts/functional/supplicant_sta_network_hidl_test.cpp
@@ -71,3 +71,16 @@
EXPECT_EQ(testOcspType, ocspType);
});
}
+
+/*
+ * SetPmkCacheEntry
+ */
+TEST_F(SupplicantStaNetworkHidlTest, SetPmkCache) {
+ uint8_t bytes[128] = {0};
+ std::vector<uint8_t> serializedEntry(bytes, bytes + sizeof(bytes));
+
+ sta_network_->setPmkCache(
+ serializedEntry, [](const SupplicantStatus &status) {
+ EXPECT_EQ(SupplicantStatusCode::SUCCESS, status.code);
+ });
+}