Merge "health 2.1 impl: log values properly."
diff --git a/audio/5.0/config/api/current.txt b/audio/5.0/config/api/current.txt
index 8458a56..dbb5d3b 100644
--- a/audio/5.0/config/api/current.txt
+++ b/audio/5.0/config/api/current.txt
@@ -199,7 +199,7 @@
public static class DevicePorts.DevicePort {
ctor public DevicePorts.DevicePort();
method public String getAddress();
- method public java.util.List<audio.policy.configuration.V5_0.AudioFormat> getEncodedFormats();
+ method public java.util.List<java.lang.String> getEncodedFormats();
method public audio.policy.configuration.V5_0.Gains getGains();
method public java.util.List<audio.policy.configuration.V5_0.Profile> getProfile();
method public audio.policy.configuration.V5_0.Role getRole();
@@ -207,7 +207,7 @@
method public String getType();
method public boolean get_default();
method public void setAddress(String);
- method public void setEncodedFormats(java.util.List<audio.policy.configuration.V5_0.AudioFormat>);
+ method public void setEncodedFormats(java.util.List<java.lang.String>);
method public void setGains(audio.policy.configuration.V5_0.Gains);
method public void setRole(audio.policy.configuration.V5_0.Role);
method public void setTagName(String);
@@ -380,10 +380,10 @@
public static class SurroundFormats.Format {
ctor public SurroundFormats.Format();
- method public audio.policy.configuration.V5_0.AudioFormat getName();
- method public java.util.List<audio.policy.configuration.V5_0.AudioFormat> getSubformats();
- method public void setName(audio.policy.configuration.V5_0.AudioFormat);
- method public void setSubformats(java.util.List<audio.policy.configuration.V5_0.AudioFormat>);
+ method public String getName();
+ method public java.util.List<java.lang.String> getSubformats();
+ method public void setName(String);
+ method public void setSubformats(java.util.List<java.lang.String>);
}
public class SurroundSound {
diff --git a/audio/5.0/config/audio_policy_configuration.xsd b/audio/5.0/config/audio_policy_configuration.xsd
index b0d1e20..f92136c 100644
--- a/audio/5.0/config/audio_policy_configuration.xsd
+++ b/audio/5.0/config/audio_policy_configuration.xsd
@@ -611,13 +611,13 @@
</xs:sequence>
</xs:complexType>
<xs:simpleType name="audioFormatsList">
- <xs:list itemType="audioFormat" />
+ <xs:list itemType="extendableAudioFormat" />
</xs:simpleType>
<xs:complexType name="surroundFormats">
<xs:sequence>
<xs:element name="format" minOccurs="0" maxOccurs="unbounded">
<xs:complexType>
- <xs:attribute name="name" type="audioFormat" use="required"/>
+ <xs:attribute name="name" type="extendableAudioFormat" use="required"/>
<xs:attribute name="subformats" type="audioFormatsList" />
</xs:complexType>
</xs:element>
diff --git a/audio/6.0/config/api/current.txt b/audio/6.0/config/api/current.txt
index f5d4798..01db90e 100644
--- a/audio/6.0/config/api/current.txt
+++ b/audio/6.0/config/api/current.txt
@@ -199,7 +199,7 @@
public static class DevicePorts.DevicePort {
ctor public DevicePorts.DevicePort();
method public String getAddress();
- method public java.util.List<audio.policy.configuration.V6_0.AudioFormat> getEncodedFormats();
+ method public java.util.List<java.lang.String> getEncodedFormats();
method public audio.policy.configuration.V6_0.Gains getGains();
method public java.util.List<audio.policy.configuration.V6_0.Profile> getProfile();
method public audio.policy.configuration.V6_0.Role getRole();
@@ -207,7 +207,7 @@
method public String getType();
method public boolean get_default();
method public void setAddress(String);
- method public void setEncodedFormats(java.util.List<audio.policy.configuration.V6_0.AudioFormat>);
+ method public void setEncodedFormats(java.util.List<java.lang.String>);
method public void setGains(audio.policy.configuration.V6_0.Gains);
method public void setRole(audio.policy.configuration.V6_0.Role);
method public void setTagName(String);
@@ -391,10 +391,10 @@
public static class SurroundFormats.Format {
ctor public SurroundFormats.Format();
- method public audio.policy.configuration.V6_0.AudioFormat getName();
- method public java.util.List<audio.policy.configuration.V6_0.AudioFormat> getSubformats();
- method public void setName(audio.policy.configuration.V6_0.AudioFormat);
- method public void setSubformats(java.util.List<audio.policy.configuration.V6_0.AudioFormat>);
+ method public String getName();
+ method public java.util.List<java.lang.String> getSubformats();
+ method public void setName(String);
+ method public void setSubformats(java.util.List<java.lang.String>);
}
public class SurroundSound {
diff --git a/audio/6.0/config/audio_policy_configuration.xsd b/audio/6.0/config/audio_policy_configuration.xsd
index ead1cc2..c2b8c5d 100644
--- a/audio/6.0/config/audio_policy_configuration.xsd
+++ b/audio/6.0/config/audio_policy_configuration.xsd
@@ -614,13 +614,13 @@
</xs:sequence>
</xs:complexType>
<xs:simpleType name="audioFormatsList">
- <xs:list itemType="audioFormat" />
+ <xs:list itemType="extendableAudioFormat" />
</xs:simpleType>
<xs:complexType name="surroundFormats">
<xs:sequence>
<xs:element name="format" minOccurs="0" maxOccurs="unbounded">
<xs:complexType>
- <xs:attribute name="name" type="audioFormat" use="required"/>
+ <xs:attribute name="name" type="extendableAudioFormat" use="required"/>
<xs:attribute name="subformats" type="audioFormatsList" />
</xs:complexType>
</xs:element>
diff --git a/audio/7.0/config/api/current.txt b/audio/7.0/config/api/current.txt
index 8a4662f..48093c5 100644
--- a/audio/7.0/config/api/current.txt
+++ b/audio/7.0/config/api/current.txt
@@ -212,6 +212,7 @@
enum_constant public static final android.audio.policy.configuration.V7_0.AudioFormat AUDIO_FORMAT_FLAC;
enum_constant public static final android.audio.policy.configuration.V7_0.AudioFormat AUDIO_FORMAT_HE_AAC_V1;
enum_constant public static final android.audio.policy.configuration.V7_0.AudioFormat AUDIO_FORMAT_HE_AAC_V2;
+ enum_constant public static final android.audio.policy.configuration.V7_0.AudioFormat AUDIO_FORMAT_IEC60958;
enum_constant public static final android.audio.policy.configuration.V7_0.AudioFormat AUDIO_FORMAT_IEC61937;
enum_constant public static final android.audio.policy.configuration.V7_0.AudioFormat AUDIO_FORMAT_LC3;
enum_constant public static final android.audio.policy.configuration.V7_0.AudioFormat AUDIO_FORMAT_LDAC;
@@ -223,6 +224,10 @@
enum_constant public static final android.audio.policy.configuration.V7_0.AudioFormat AUDIO_FORMAT_MAT_2_1;
enum_constant public static final android.audio.policy.configuration.V7_0.AudioFormat AUDIO_FORMAT_MP2;
enum_constant public static final android.audio.policy.configuration.V7_0.AudioFormat AUDIO_FORMAT_MP3;
+ enum_constant public static final android.audio.policy.configuration.V7_0.AudioFormat AUDIO_FORMAT_MPEGH_BL_L3;
+ enum_constant public static final android.audio.policy.configuration.V7_0.AudioFormat AUDIO_FORMAT_MPEGH_BL_L4;
+ enum_constant public static final android.audio.policy.configuration.V7_0.AudioFormat AUDIO_FORMAT_MPEGH_LC_L3;
+ enum_constant public static final android.audio.policy.configuration.V7_0.AudioFormat AUDIO_FORMAT_MPEGH_LC_L4;
enum_constant public static final android.audio.policy.configuration.V7_0.AudioFormat AUDIO_FORMAT_OPUS;
enum_constant public static final android.audio.policy.configuration.V7_0.AudioFormat AUDIO_FORMAT_PCM_16_BIT;
enum_constant public static final android.audio.policy.configuration.V7_0.AudioFormat AUDIO_FORMAT_PCM_24_BIT_PACKED;
@@ -251,7 +256,6 @@
enum_constant public static final android.audio.policy.configuration.V7_0.AudioInOutFlag AUDIO_INPUT_FLAG_HW_AV_SYNC;
enum_constant public static final android.audio.policy.configuration.V7_0.AudioInOutFlag AUDIO_INPUT_FLAG_HW_HOTWORD;
enum_constant public static final android.audio.policy.configuration.V7_0.AudioInOutFlag AUDIO_INPUT_FLAG_MMAP_NOIRQ;
- enum_constant public static final android.audio.policy.configuration.V7_0.AudioInOutFlag AUDIO_INPUT_FLAG_NONE;
enum_constant public static final android.audio.policy.configuration.V7_0.AudioInOutFlag AUDIO_INPUT_FLAG_RAW;
enum_constant public static final android.audio.policy.configuration.V7_0.AudioInOutFlag AUDIO_INPUT_FLAG_SYNC;
enum_constant public static final android.audio.policy.configuration.V7_0.AudioInOutFlag AUDIO_INPUT_FLAG_VOIP_TX;
@@ -264,7 +268,6 @@
enum_constant public static final android.audio.policy.configuration.V7_0.AudioInOutFlag AUDIO_OUTPUT_FLAG_IEC958_NONAUDIO;
enum_constant public static final android.audio.policy.configuration.V7_0.AudioInOutFlag AUDIO_OUTPUT_FLAG_INCALL_MUSIC;
enum_constant public static final android.audio.policy.configuration.V7_0.AudioInOutFlag AUDIO_OUTPUT_FLAG_MMAP_NOIRQ;
- enum_constant public static final android.audio.policy.configuration.V7_0.AudioInOutFlag AUDIO_OUTPUT_FLAG_NONE;
enum_constant public static final android.audio.policy.configuration.V7_0.AudioInOutFlag AUDIO_OUTPUT_FLAG_NON_BLOCKING;
enum_constant public static final android.audio.policy.configuration.V7_0.AudioInOutFlag AUDIO_OUTPUT_FLAG_PRIMARY;
enum_constant public static final android.audio.policy.configuration.V7_0.AudioInOutFlag AUDIO_OUTPUT_FLAG_RAW;
@@ -542,7 +545,7 @@
public enum Version {
method @NonNull public String getRawName();
- enum_constant public static final android.audio.policy.configuration.V7_0.Version _1_0;
+ enum_constant public static final android.audio.policy.configuration.V7_0.Version _7_0;
}
public class Volume {
diff --git a/audio/7.0/config/audio_policy_configuration.xsd b/audio/7.0/config/audio_policy_configuration.xsd
index 685e84a..531572b 100644
--- a/audio/7.0/config/audio_policy_configuration.xsd
+++ b/audio/7.0/config/audio_policy_configuration.xsd
@@ -20,7 +20,7 @@
<!-- List the config versions supported by audio policy. -->
<xs:simpleType name="version">
<xs:restriction base="xs:decimal">
- <xs:enumeration value="1.0"/>
+ <xs:enumeration value="7.0"/>
</xs:restriction>
</xs:simpleType>
<xs:simpleType name="halVersion">
@@ -159,13 +159,9 @@
<xs:annotation>
<xs:documentation xml:lang="en">
The flags indicate suggested stream attributes supported by the profile.
- Use of AUDIO_{INPUT|OUTPUT}_FLAG_NONE in the XML file isn't required
- as empty flag lists are allowed. However these constants are useful for
- representing an empty enum value.
</xs:documentation>
</xs:annotation>
<xs:restriction base="xs:string">
- <xs:enumeration value="AUDIO_OUTPUT_FLAG_NONE" />
<xs:enumeration value="AUDIO_OUTPUT_FLAG_DIRECT" />
<xs:enumeration value="AUDIO_OUTPUT_FLAG_PRIMARY" />
<xs:enumeration value="AUDIO_OUTPUT_FLAG_FAST" />
@@ -181,7 +177,6 @@
<xs:enumeration value="AUDIO_OUTPUT_FLAG_MMAP_NOIRQ" />
<xs:enumeration value="AUDIO_OUTPUT_FLAG_VOIP_RX" />
<xs:enumeration value="AUDIO_OUTPUT_FLAG_INCALL_MUSIC" />
- <xs:enumeration value="AUDIO_INPUT_FLAG_NONE" />
<xs:enumeration value="AUDIO_INPUT_FLAG_FAST" />
<xs:enumeration value="AUDIO_INPUT_FLAG_HW_HOTWORD" />
<xs:enumeration value="AUDIO_INPUT_FLAG_RAW" />
@@ -407,6 +402,11 @@
<xs:enumeration value="AUDIO_FORMAT_LHDC_LL"/>
<xs:enumeration value="AUDIO_FORMAT_APTX_TWSP"/>
<xs:enumeration value="AUDIO_FORMAT_LC3"/>
+ <xs:enumeration value="AUDIO_FORMAT_MPEGH_BL_L3"/>
+ <xs:enumeration value="AUDIO_FORMAT_MPEGH_BL_L4"/>
+ <xs:enumeration value="AUDIO_FORMAT_MPEGH_LC_L3"/>
+ <xs:enumeration value="AUDIO_FORMAT_MPEGH_LC_L4"/>
+ <xs:enumeration value="AUDIO_FORMAT_IEC60958"/>
</xs:restriction>
</xs:simpleType>
<xs:simpleType name="extendableAudioFormat">
diff --git a/audio/7.0/config/update_audio_policy_config.sh b/audio/7.0/config/update_audio_policy_config.sh
index 051a0df..159fa35 100755
--- a/audio/7.0/config/update_audio_policy_config.sh
+++ b/audio/7.0/config/update_audio_policy_config.sh
@@ -113,6 +113,9 @@
echo "Press Ctrl-C to cancel, Enter to continue"
read
+# Update 'audioPolicyConfiguration version="1.0"' -> 7.0 in the main file
+sed -i -r -e 's/(audioPolicyConfiguration version=")1.0/\17.0/' ${SOURCE_CONFIG}
+
updateFile() {
FILE=$1
ATTR=$2
diff --git a/audio/README.md b/audio/README.md
index b77b9ba..1938ad4 100644
--- a/audio/README.md
+++ b/audio/README.md
@@ -7,47 +7,49 @@
## Directory Structure
-* `2.0` -- version 2.0 of the core HIDL API. Note that `.hal` files
+* `2.0` — version 2.0 of the core HIDL API. Note that `.hal` files
can not be moved into the `core` directory because that would change
its namespace and include path.
- - `config` -- the XSD schema for the Audio Policy Manager
+ - `config` — the XSD schema for the Audio Policy Manager
configuration file.
-* `4.0` -- version 4.0 of the core HIDL API.
+* `4.0` — version 4.0 of the core HIDL API.
* ...
-* `common` -- common types for audio core and effect HIDL API.
- - `2.0` -- version 2.0 of the common types HIDL API.
- - `4.0` -- version 4.0.
+* `common` — common types for audio core and effect HIDL API.
+ - `2.0` — version 2.0 of the common types HIDL API.
+ - `4.0` — version 4.0.
- ...
- - `7.0` -- version 7.0.
- - `example` -- example implementation of the core and effect
+ - `7.0` — version 7.0.
+ - `example` — example implementation of the core and effect
V7.0 API. It represents a "fake" audio HAL that doesn't
actually communicate with hardware.
- - `all-versions` -- code common to all version of both core and effect API.
- - `default` -- shared code of the default implementation.
- - `service` -- vendor HAL service for hosting the default
+ - `all-versions` — code common to all version of both core and effect API.
+ - `default` — shared code of the default implementation.
+ - `service` — vendor HAL service for hosting the default
implementation.
- - `test` -- utilities used by tests.
- - `util` -- utilities used by both implementation and tests.
-* `core` -- VTS tests and the default implementation of the core API
+ - `test` — utilities used by tests.
+ - `util` — utilities used by both implementation and tests.
+* `core` — VTS tests and the default implementation of the core API
(not HIDL API, it's in `audio/N.M`).
- - `7.0` -- code specific to version V7.0 of the core HIDL API
- - `all-versions` -- the code is common between all versions,
+ - `7.0` — code specific to version V7.0 of the core HIDL API
+ - `all-versions` — the code is common between all versions,
version-specific parts are enclosed into conditional directives
of preprocessor or reside in dedicated files.
- - `default` -- code that wraps the legacy API (from
+ - `default` — code that wraps the legacy API (from
`hardware/libhardware`).
+ - `util` — utilities for the default implementation.
- `vts` VTS tests for the core HIDL API.
-* `effect` -- same for the effect HIDL API.
+* `effect` — same for the effect HIDL API.
- `2.0`
- - `config` -- the XSD schema for the Audio Effects configuration
- file.
+ - `config` — the XSD schema for the Audio Effects configuration file.
- `4.0`
- ...
- `all-versions`
- - `default`
- - `vts`
-* `policy` -- Configurable Audio Policy schemes.
- - `1.0` -- note that versions of CAP are not linked to the versions
+ - `default` — code that wraps the legacy API (from
+ `hardware/libhardware`).
+ - `util` — utilities for the default implementation.
+ - `vts` VTS tests for the effect HIDL API.
+* `policy` — Configurable Audio Policy schemes.
+ - `1.0` — note that versions of CAP are not linked to the versions
of audio HAL.
- - `vts` -- VTS tests for validating actual configuration files.
- - `xml` -- XSD schemas for CAP configuration files.
+ - `vts` — VTS tests for validating actual configuration files.
+ - `xml` — XSD schemas for CAP configuration files.
diff --git a/audio/common/7.0/enums/include/android_audio_policy_configuration_V7_0-enums.h b/audio/common/7.0/enums/include/android_audio_policy_configuration_V7_0-enums.h
index c0042db..b427f3a 100644
--- a/audio/common/7.0/enums/include/android_audio_policy_configuration_V7_0-enums.h
+++ b/audio/common/7.0/enums/include/android_audio_policy_configuration_V7_0-enums.h
@@ -212,10 +212,16 @@
return isOutputDevice(stringToAudioDevice(device));
}
+static inline bool maybeVendorExtension(const std::string& s) {
+ // Only checks whether the string starts with the "vendor prefix".
+ static const std::string vendorPrefix = "VX_";
+ return s.size() > vendorPrefix.size() && s.substr(0, vendorPrefix.size()) == vendorPrefix;
+}
+
static inline bool isVendorExtension(const std::string& s) {
// Must match the "vendorExtension" rule from the XSD file.
static const std::string vendorPrefix = "VX_";
- return s.size() > vendorPrefix.size() && s.substr(0, vendorPrefix.size()) == vendorPrefix &&
+ return maybeVendorExtension(s) &&
std::all_of(s.begin() + vendorPrefix.size(), s.end(),
[](unsigned char c) { return c == '_' || std::isalnum(c); });
}
diff --git a/audio/common/7.0/types.hal b/audio/common/7.0/types.hal
index 3903a0b..bea0705 100644
--- a/audio/common/7.0/types.hal
+++ b/audio/common/7.0/types.hal
@@ -61,6 +61,8 @@
* Audio stream type describing the intended use case of a stream.
* See 'audioStreamType' in audio_policy_configuration.xsd for the
* list of allowed values.
+ *
+ * An empty string is used to specify the "default" stream type.
*/
typedef string AudioStreamType;
@@ -309,15 +311,15 @@
struct PlaybackTrackMetadata {
AudioUsage usage;
AudioContentType contentType;
- /** Tags from AudioTrack audio atttributes */
- vec<AudioTag> tags;
- AudioChannelMask channelMask;
/**
* Positive linear gain applied to the track samples. 0 being muted and 1 is no attenuation,
* 2 means double amplification...
* Must not be negative.
*/
float gain;
+ AudioChannelMask channelMask;
+ /** Tags from AudioTrack audio atttributes */
+ vec<AudioTag> tags;
};
/** Metadatas of the source of a StreamOut. */
@@ -328,9 +330,6 @@
/** Metadata of a record track for a StreamIn. */
struct RecordTrackMetadata {
AudioSource source;
- /** Tags from AudioTrack audio atttributes */
- vec<AudioTag> tags;
- AudioChannelMask channelMask;
/**
* Positive linear gain applied to the track samples. 0 being muted and 1 is no attenuation,
* 2 means double amplification...
@@ -344,6 +343,9 @@
Monostate unspecified;
DeviceAddress device;
} destination;
+ AudioChannelMask channelMask;
+ /** Tags from AudioTrack audio atttributes */
+ vec<AudioTag> tags;
};
/** Metadatas of the sink of a StreamIn. */
diff --git a/audio/common/all-versions/default/7.0/HidlUtils.cpp b/audio/common/all-versions/default/7.0/HidlUtils.cpp
index f09db5e..2949fac 100644
--- a/audio/common/all-versions/default/7.0/HidlUtils.cpp
+++ b/audio/common/all-versions/default/7.0/HidlUtils.cpp
@@ -16,6 +16,7 @@
#include <stdio.h>
#include <string.h>
+#include <algorithm>
#define LOG_TAG "HidlUtils"
#include <log/log.h>
@@ -281,7 +282,7 @@
hidl_vec<AudioGainMode>* gainModeMask) {
status_t status = NO_ERROR;
std::vector<AudioGainMode> result;
- for (uint32_t bit = 0; bit < sizeof(audio_gain_mode_t) * 8; ++bit) {
+ for (uint32_t bit = 0; halGainModeMask != 0 && bit < sizeof(audio_gain_mode_t) * 8; ++bit) {
audio_gain_mode_t flag = static_cast<audio_gain_mode_t>(1u << bit);
if ((flag & halGainModeMask) == flag) {
AudioGainMode flagStr = audio_gain_mode_to_string(flag);
@@ -291,6 +292,7 @@
ALOGE("Unknown audio gain mode value 0x%X", flag);
status = BAD_VALUE;
}
+ halGainModeMask = static_cast<audio_gain_mode_t>(halGainModeMask & ~flag);
}
}
*gainModeMask = result;
@@ -333,25 +335,35 @@
return BAD_VALUE;
}
+// The "default" value of audio_stream_type_t is represented by an empty string.
status_t HidlUtils::audioStreamTypeFromHal(audio_stream_type_t halStreamType,
AudioStreamType* streamType) {
- *streamType = audio_stream_type_to_string(halStreamType);
- if (!streamType->empty() && !xsd::isUnknownAudioStreamType(*streamType)) {
+ if (halStreamType != AUDIO_STREAM_DEFAULT) {
+ *streamType = audio_stream_type_to_string(halStreamType);
+ if (!streamType->empty() && !xsd::isUnknownAudioStreamType(*streamType)) {
+ return NO_ERROR;
+ }
+ ALOGE("Unknown audio stream type value 0x%X", halStreamType);
+ return BAD_VALUE;
+ } else {
+ *streamType = "";
return NO_ERROR;
}
- ALOGE("Unknown audio stream type value 0x%X", halStreamType);
- return BAD_VALUE;
}
status_t HidlUtils::audioStreamTypeToHal(const AudioStreamType& streamType,
audio_stream_type_t* halStreamType) {
- if (!xsd::isUnknownAudioStreamType(streamType) &&
- audio_stream_type_from_string(streamType.c_str(), halStreamType)) {
+ if (!streamType.empty()) {
+ if (!xsd::isUnknownAudioStreamType(streamType) &&
+ audio_stream_type_from_string(streamType.c_str(), halStreamType)) {
+ return NO_ERROR;
+ }
+ ALOGE("Unknown audio stream type \"%s\"", streamType.c_str());
+ return BAD_VALUE;
+ } else {
+ *halStreamType = AUDIO_STREAM_DEFAULT;
return NO_ERROR;
}
- ALOGE("Unknown audio stream type \"%s\"", streamType.c_str());
- *halStreamType = AUDIO_STREAM_DEFAULT;
- return BAD_VALUE;
}
status_t HidlUtils::audioConfigFromHal(const audio_config_t& halConfig, bool isInput,
@@ -858,15 +870,17 @@
return result;
}
-status_t HidlUtils::audioTagsFromHal(const char* halTags, hidl_vec<AudioTag>* tags) {
- std::vector<std::string> strTags = utils::splitString(halTags, sAudioTagSeparator);
+status_t HidlUtils::audioTagsFromHal(const std::vector<std::string>& strTags,
+ hidl_vec<AudioTag>* tags) {
status_t result = NO_ERROR;
tags->resize(strTags.size());
size_t to = 0;
for (size_t from = 0; from < strTags.size(); ++from) {
- if (xsd::isVendorExtension(strTags[from])) {
- (*tags)[to++] = strTags[from];
+ const auto& tag = strTags[from];
+ if (xsd::isVendorExtension(tag)) {
+ (*tags)[to++] = tag;
} else {
+ ALOGE("Vendor extension tag is ill-formed: \"%s\"", tag.c_str());
result = BAD_VALUE;
}
}
@@ -889,6 +903,7 @@
halTagsBuffer << tag;
hasValue = true;
} else {
+ ALOGE("Vendor extension tag is ill-formed: \"%s\"", tag.c_str());
result = BAD_VALUE;
}
}
@@ -899,6 +914,31 @@
return result;
}
+hidl_vec<AudioTag> HidlUtils::filterOutNonVendorTags(const hidl_vec<AudioTag>& tags) {
+ hidl_vec<AudioTag> result;
+ result.resize(tags.size());
+ size_t resultIdx = 0;
+ for (const auto& tag : tags) {
+ if (xsd::maybeVendorExtension(tag)) {
+ result[resultIdx++] = tag;
+ }
+ }
+ if (resultIdx != result.size()) {
+ result.resize(resultIdx);
+ }
+ return result;
+}
+
+std::vector<std::string> HidlUtils::filterOutNonVendorTags(const std::vector<std::string>& tags) {
+ std::vector<std::string> result;
+ std::copy_if(tags.begin(), tags.end(), std::back_inserter(result), xsd::maybeVendorExtension);
+ return result;
+}
+
+std::vector<std::string> HidlUtils::splitAudioTags(const char* halTags) {
+ return utils::splitString(halTags, sAudioTagSeparator);
+}
+
status_t HidlUtils::deviceAddressFromHal(audio_devices_t halDeviceType,
const char* halDeviceAddress, DeviceAddress* device) {
status_t result = NO_ERROR;
diff --git a/audio/common/all-versions/default/HidlUtils.h b/audio/common/all-versions/default/HidlUtils.h
index cc4fbf2..22b7152 100644
--- a/audio/common/all-versions/default/HidlUtils.h
+++ b/audio/common/all-versions/default/HidlUtils.h
@@ -20,6 +20,8 @@
#include PATH(android/hardware/audio/common/FILE_VERSION/types.h)
#include <memory>
+#include <string>
+#include <vector>
#include <system/audio.h>
@@ -118,8 +120,12 @@
AudioStreamType* streamType);
static status_t audioStreamTypeToHal(const AudioStreamType& streamType,
audio_stream_type_t* halStreamType);
- static status_t audioTagsFromHal(const char* halTags, hidl_vec<AudioTag>* tags);
+ static status_t audioTagsFromHal(const std::vector<std::string>& strTags,
+ hidl_vec<AudioTag>* tags);
static status_t audioTagsToHal(const hidl_vec<AudioTag>& tags, char* halTags);
+ static hidl_vec<AudioTag> filterOutNonVendorTags(const hidl_vec<AudioTag>& tags);
+ static std::vector<std::string> filterOutNonVendorTags(const std::vector<std::string>& tags);
+ static std::vector<std::string> splitAudioTags(const char* halTags);
private:
static status_t audioIndexChannelMaskFromHal(audio_channel_mask_t halChannelMask,
diff --git a/audio/common/all-versions/default/UuidUtils.cpp b/audio/common/all-versions/default/UuidUtils.cpp
index 85edc7b..6c4c94d 100644
--- a/audio/common/all-versions/default/UuidUtils.cpp
+++ b/audio/common/all-versions/default/UuidUtils.cpp
@@ -42,6 +42,14 @@
memcpy(halUuid->node, uuid.node.data(), uuid.node.size());
}
+std::string UuidUtils::uuidToString(const audio_uuid_t& halUuid) {
+ char str[64];
+ snprintf(str, sizeof(str), "%08x-%04x-%04x-%04x-%02x%02x%02x%02x%02x%02x", halUuid.timeLow,
+ halUuid.timeMid, halUuid.timeHiAndVersion, halUuid.clockSeq, halUuid.node[0],
+ halUuid.node[1], halUuid.node[2], halUuid.node[3], halUuid.node[4], halUuid.node[5]);
+ return str;
+}
+
} // namespace implementation
} // namespace CPP_VERSION
} // namespace common
diff --git a/audio/common/all-versions/default/UuidUtils.h b/audio/common/all-versions/default/UuidUtils.h
index 38db48a..cd04fb0 100644
--- a/audio/common/all-versions/default/UuidUtils.h
+++ b/audio/common/all-versions/default/UuidUtils.h
@@ -17,14 +17,14 @@
#ifndef android_hardware_audio_Uuid_Utils_H_
#define android_hardware_audio_Uuid_Utils_H_
+#include <string>
+
// clang-format off
#include PATH(android/hardware/audio/common/FILE_VERSION/types.h)
// clang-format on
#include <system/audio.h>
-using ::android::hardware::hidl_vec;
-
namespace android {
namespace hardware {
namespace audio {
@@ -38,6 +38,7 @@
public:
static void uuidFromHal(const audio_uuid_t& halUuid, Uuid* uuid);
static void uuidToHal(const Uuid& uuid, audio_uuid_t* halUuid);
+ static std::string uuidToString(const audio_uuid_t& halUuid);
};
} // namespace implementation
diff --git a/audio/common/all-versions/default/service/service.cpp b/audio/common/all-versions/default/service/service.cpp
index 710ddce..bbc14ad 100644
--- a/audio/common/all-versions/default/service/service.cpp
+++ b/audio/common/all-versions/default/service/service.cpp
@@ -63,17 +63,17 @@
const std::vector<InterfacesList> mandatoryInterfaces = {
{
"Audio Core API",
+ "android.hardware.audio@7.0::IDevicesFactory",
"android.hardware.audio@6.0::IDevicesFactory",
"android.hardware.audio@5.0::IDevicesFactory",
"android.hardware.audio@4.0::IDevicesFactory",
- "android.hardware.audio@2.0::IDevicesFactory"
},
{
"Audio Effect API",
+ "android.hardware.audio.effect@7.0::IEffectsFactory",
"android.hardware.audio.effect@6.0::IEffectsFactory",
"android.hardware.audio.effect@5.0::IEffectsFactory",
"android.hardware.audio.effect@4.0::IEffectsFactory",
- "android.hardware.audio.effect@2.0::IEffectsFactory",
}
};
diff --git a/audio/common/all-versions/default/tests/hidlutils_tests.cpp b/audio/common/all-versions/default/tests/hidlutils_tests.cpp
index 642ece3..99d2e72 100644
--- a/audio/common/all-versions/default/tests/hidlutils_tests.cpp
+++ b/audio/common/all-versions/default/tests/hidlutils_tests.cpp
@@ -44,8 +44,8 @@
static_cast<audio_gain_mode_t>(0xFFFFFFFFU);
// AUDIO_SOURCE_INVALID is framework-only.
static constexpr audio_source_t kInvalidHalSource = static_cast<audio_source_t>(-1);
-static constexpr audio_stream_type_t kInvalidHalStreamType =
- static_cast<audio_stream_type_t>(0xFFFFFFFFU);
+// AUDIO_STREAM_DEFAULT is framework-only
+static constexpr audio_stream_type_t kInvalidHalStreamType = static_cast<audio_stream_type_t>(-2);
static constexpr audio_usage_t kInvalidHalUsage = static_cast<audio_usage_t>(0xFFFFFFFFU);
TEST(HidlUtils, ConvertInvalidChannelMask) {
@@ -660,10 +660,18 @@
AudioStreamType invalid;
EXPECT_EQ(BAD_VALUE, HidlUtils::audioStreamTypeFromHal(kInvalidHalStreamType, &invalid));
audio_stream_type_t halInvalid;
- EXPECT_EQ(BAD_VALUE, HidlUtils::audioStreamTypeToHal("", &halInvalid));
EXPECT_EQ(BAD_VALUE, HidlUtils::audioStreamTypeToHal("random string", &halInvalid));
}
+TEST(HidlUtils, ConvertDefaultStreamType) {
+ AudioStreamType streamDefault = "";
+ audio_stream_type_t halStreamDefault;
+ EXPECT_EQ(NO_ERROR, HidlUtils::audioStreamTypeToHal(streamDefault, &halStreamDefault));
+ AudioStreamType streamDefaultBack;
+ EXPECT_EQ(NO_ERROR, HidlUtils::audioStreamTypeFromHal(halStreamDefault, &streamDefaultBack));
+ EXPECT_EQ(streamDefault, streamDefaultBack);
+}
+
TEST(HidlUtils, ConvertStreamType) {
for (const auto enumVal : xsdc_enum_range<xsd::AudioStreamType>{}) {
const AudioStreamType streamType = toString(enumVal);
@@ -762,9 +770,7 @@
TEST(HidlUtils, ConvertOffloadInfo) {
AudioOffloadInfo offloadInfo = {};
- offloadInfo.base.sampleRateHz = 44100;
- offloadInfo.base.channelMask = toString(xsd::AudioChannelMask::AUDIO_CHANNEL_OUT_STEREO);
- offloadInfo.base.format = toString(xsd::AudioFormat::AUDIO_FORMAT_PCM_16_BIT);
+ offloadInfo.base = generateValidConfigBase(false /*isInput*/);
offloadInfo.streamType = toString(xsd::AudioStreamType::AUDIO_STREAM_MUSIC);
offloadInfo.bitRatePerSecond = 320;
offloadInfo.durationMicroseconds = -1;
@@ -783,33 +789,76 @@
TEST(HidlUtils, ConvertInvalidConfig) {
AudioConfig invalid;
- audio_config_t halInvalid = AUDIO_CONFIG_INITIALIZER;
- halInvalid.channel_mask = kInvalidHalChannelMask;
- halInvalid.format = kInvalidHalFormat;
- EXPECT_EQ(BAD_VALUE, HidlUtils::audioConfigFromHal(halInvalid, false /*isInput*/, &invalid));
- EXPECT_EQ(BAD_VALUE, HidlUtils::audioConfigFromHal(halInvalid, true /*isInput*/, &invalid));
- invalid.base.channelMask = "random string";
- invalid.base.format = "random string";
- EXPECT_EQ(BAD_VALUE, HidlUtils::audioConfigToHal(invalid, &halInvalid));
+ audio_config_t halInvalidChannelMask = AUDIO_CONFIG_INITIALIZER;
+ halInvalidChannelMask.channel_mask = kInvalidHalChannelMask;
+ EXPECT_EQ(BAD_VALUE,
+ HidlUtils::audioConfigFromHal(halInvalidChannelMask, false /*isInput*/, &invalid));
+ EXPECT_EQ(BAD_VALUE,
+ HidlUtils::audioConfigFromHal(halInvalidChannelMask, true /*isInput*/, &invalid));
+ audio_config_t halInvalidFormat = AUDIO_CONFIG_INITIALIZER;
+ halInvalidFormat.format = kInvalidHalFormat;
+ EXPECT_EQ(BAD_VALUE,
+ HidlUtils::audioConfigFromHal(halInvalidFormat, false /*isInput*/, &invalid));
+ EXPECT_EQ(BAD_VALUE,
+ HidlUtils::audioConfigFromHal(halInvalidFormat, true /*isInput*/, &invalid));
+
+ AudioConfig invalidChannelMask;
+ audio_config_t halInvalid;
+ invalidChannelMask.base.channelMask = "random string";
+ invalidChannelMask.base.format = toString(xsd::AudioFormat::AUDIO_FORMAT_DEFAULT);
+ EXPECT_EQ(BAD_VALUE, HidlUtils::audioConfigToHal(invalidChannelMask, &halInvalid));
+ AudioConfig invalidFormat;
+ invalidFormat.base.format = "random string";
+ invalidFormat.base.channelMask = toString(xsd::AudioChannelMask::AUDIO_CHANNEL_NONE);
+ EXPECT_EQ(BAD_VALUE, HidlUtils::audioConfigToHal(invalidFormat, &halInvalid));
+}
+
+TEST(HidlUtils, ConvertConfigDefault) {
+ audio_config_t halDefault = AUDIO_CONFIG_INITIALIZER;
+ AudioConfig defaultOut, defaultIn;
+ EXPECT_EQ(NO_ERROR, HidlUtils::audioConfigFromHal(halDefault, false /*isInput*/, &defaultOut));
+ EXPECT_EQ(NO_ERROR, HidlUtils::audioConfigFromHal(halDefault, true /*isInput*/, &defaultIn));
+ EXPECT_EQ(defaultOut, defaultIn);
+ EXPECT_EQ(NO_ERROR, HidlUtils::audioConfigToHal(defaultOut, &halDefault));
+
+ // Note: empty channel mask and config are not valid values.
+ AudioConfig defaultCfg{};
+ defaultCfg.base.channelMask = toString(xsd::AudioChannelMask::AUDIO_CHANNEL_NONE);
+ defaultCfg.base.format = toString(xsd::AudioFormat::AUDIO_FORMAT_DEFAULT);
+ audio_config_t halDefaultCfg;
+ EXPECT_EQ(NO_ERROR, HidlUtils::audioConfigToHal(defaultCfg, &halDefaultCfg));
+ AudioConfig defaultCfgBackOut, defaultCfgBackIn;
+ EXPECT_EQ(NO_ERROR,
+ HidlUtils::audioConfigFromHal(halDefaultCfg, false /*isInput*/, &defaultCfgBackOut));
+ EXPECT_EQ(NO_ERROR,
+ HidlUtils::audioConfigFromHal(halDefaultCfg, true /*isInput*/, &defaultCfgBackIn));
+ EXPECT_EQ(defaultCfgBackOut, defaultCfgBackIn);
+ EXPECT_EQ(defaultCfg, defaultCfgBackOut);
}
TEST(HidlUtils, ConvertConfig) {
- AudioConfig config = {};
- config.base.sampleRateHz = 44100;
- config.base.channelMask = toString(xsd::AudioChannelMask::AUDIO_CHANNEL_OUT_STEREO);
- config.base.format = toString(xsd::AudioFormat::AUDIO_FORMAT_PCM_16_BIT);
- audio_config_t halConfig;
- EXPECT_EQ(NO_ERROR, HidlUtils::audioConfigToHal(config, &halConfig));
- AudioConfig configBack;
- EXPECT_EQ(NO_ERROR, HidlUtils::audioConfigFromHal(halConfig, false /*isInput*/, &configBack));
- EXPECT_EQ(config, configBack);
+ AudioConfig configOut{};
+ configOut.base = generateValidConfigBase(false /*isInput*/);
+ audio_config_t halConfigOut;
+ EXPECT_EQ(NO_ERROR, HidlUtils::audioConfigToHal(configOut, &halConfigOut));
+ AudioConfig configOutBack;
+ EXPECT_EQ(NO_ERROR,
+ HidlUtils::audioConfigFromHal(halConfigOut, false /*isInput*/, &configOutBack));
+ EXPECT_EQ(configOut, configOutBack);
+
+ AudioConfig configIn{};
+ configIn.base = generateValidConfigBase(true /*isInput*/);
+ audio_config_t halConfigIn;
+ EXPECT_EQ(NO_ERROR, HidlUtils::audioConfigToHal(configIn, &halConfigIn));
+ AudioConfig configInBack;
+ EXPECT_EQ(NO_ERROR,
+ HidlUtils::audioConfigFromHal(halConfigIn, true /*isInput*/, &configInBack));
+ EXPECT_EQ(configIn, configInBack);
}
TEST(HidlUtils, ConvertConfigWithOffloadInfo) {
AudioConfig config = {};
- config.base.sampleRateHz = 44100;
- config.base.channelMask = toString(xsd::AudioChannelMask::AUDIO_CHANNEL_OUT_STEREO);
- config.base.format = toString(xsd::AudioFormat::AUDIO_FORMAT_PCM_16_BIT);
+ config.base = generateValidConfigBase(false /*isInput*/);
config.offloadInfo.info(
AudioOffloadInfo{.base = config.base,
.streamType = toString(xsd::AudioStreamType::AUDIO_STREAM_MUSIC),
@@ -952,20 +1001,51 @@
char halEmptyTags[AUDIO_ATTRIBUTES_TAGS_MAX_SIZE] = {};
EXPECT_EQ(NO_ERROR, HidlUtils::audioTagsToHal(emptyTags, halEmptyTags));
hidl_vec<AudioTag> emptyTagsBack;
- EXPECT_EQ(NO_ERROR, HidlUtils::audioTagsFromHal(halEmptyTags, &emptyTagsBack));
+ EXPECT_EQ(NO_ERROR,
+ HidlUtils::audioTagsFromHal(HidlUtils::splitAudioTags(halEmptyTags), &emptyTagsBack));
EXPECT_EQ(emptyTags, emptyTagsBack);
hidl_vec<AudioTag> oneTag = {{"VX_GOOGLE_VR"}};
char halOneTag[AUDIO_ATTRIBUTES_TAGS_MAX_SIZE] = {};
EXPECT_EQ(NO_ERROR, HidlUtils::audioTagsToHal(oneTag, halOneTag));
hidl_vec<AudioTag> oneTagBack;
- EXPECT_EQ(NO_ERROR, HidlUtils::audioTagsFromHal(halOneTag, &oneTagBack));
+ EXPECT_EQ(NO_ERROR,
+ HidlUtils::audioTagsFromHal(HidlUtils::splitAudioTags(halOneTag), &oneTagBack));
EXPECT_EQ(oneTag, oneTagBack);
hidl_vec<AudioTag> twoTags = {{"VX_GOOGLE_VR_42", "VX_GOOGLE_1E100"}};
char halTwoTags[AUDIO_ATTRIBUTES_TAGS_MAX_SIZE] = {};
EXPECT_EQ(NO_ERROR, HidlUtils::audioTagsToHal(twoTags, halTwoTags));
hidl_vec<AudioTag> twoTagsBack;
- EXPECT_EQ(NO_ERROR, HidlUtils::audioTagsFromHal(halTwoTags, &twoTagsBack));
+ EXPECT_EQ(NO_ERROR,
+ HidlUtils::audioTagsFromHal(HidlUtils::splitAudioTags(halTwoTags), &twoTagsBack));
EXPECT_EQ(twoTags, twoTagsBack);
}
+
+template <typename T>
+class FilterTest : public ::testing::Test {};
+using FilterTestTypeParams = ::testing::Types<hidl_vec<AudioTag>, std::vector<std::string>>;
+TYPED_TEST_SUITE(FilterTest, FilterTestTypeParams);
+
+TYPED_TEST(FilterTest, FilterOutNonVendorTags) {
+ TypeParam emptyTags;
+ EXPECT_EQ(emptyTags, HidlUtils::filterOutNonVendorTags(emptyTags));
+
+ TypeParam allVendorTags = {{"VX_GOOGLE_VR_42", "VX_GOOGLE_1E100"}};
+ EXPECT_EQ(allVendorTags, HidlUtils::filterOutNonVendorTags(allVendorTags));
+
+ TypeParam oneVendorTag = {{"", "VX_GOOGLE_VR", "random_string"}};
+ TypeParam oneVendorTagOnly = HidlUtils::filterOutNonVendorTags(oneVendorTag);
+ EXPECT_EQ(1, oneVendorTagOnly.size());
+ EXPECT_EQ(oneVendorTag[1], oneVendorTagOnly[0]);
+
+ // The vendor extension isn't valid, however it must not be filtered out
+ // so the converter can detect the issue.
+ TypeParam oneMaybeVendorTag = {{"", "random string", "VX_GOOGLE_$$"}};
+ TypeParam oneMaybeVendorTagOnly = HidlUtils::filterOutNonVendorTags(oneMaybeVendorTag);
+ EXPECT_EQ(1, oneMaybeVendorTagOnly.size());
+ EXPECT_EQ(oneMaybeVendorTag[2], oneMaybeVendorTagOnly[0]);
+
+ TypeParam noVendorTags = {{"", "random string", "V_"}};
+ EXPECT_EQ(emptyTags, HidlUtils::filterOutNonVendorTags(noVendorTags));
+}
diff --git a/audio/core/all-versions/default/Android.bp b/audio/core/all-versions/default/Android.bp
index e0f0860..dcff425 100644
--- a/audio/core/all-versions/default/Android.bp
+++ b/audio/core/all-versions/default/Android.bp
@@ -1,7 +1,6 @@
filegroup {
name: "android.hardware.audio-impl_srcs",
srcs: [
- "Conversions.cpp",
"Device.cpp",
"DevicesFactory.cpp",
"ParametersUtil.cpp",
@@ -63,6 +62,7 @@
defaults: ["android.hardware.audio-impl_default"],
shared_libs: [
"android.hardware.audio@2.0",
+ "android.hardware.audio@2.0-util",
"android.hardware.audio.common@2.0",
"android.hardware.audio.common@2.0-util",
],
@@ -79,6 +79,7 @@
shared_libs: [
"android.hardware.audio@4.0",
+ "android.hardware.audio@4.0-util",
"android.hardware.audio.common@4.0",
"android.hardware.audio.common@4.0-util",
],
@@ -94,6 +95,7 @@
defaults: ["android.hardware.audio-impl_default"],
shared_libs: [
"android.hardware.audio@5.0",
+ "android.hardware.audio@5.0-util",
"android.hardware.audio.common@5.0",
"android.hardware.audio.common@5.0-util",
],
@@ -109,6 +111,7 @@
defaults: ["android.hardware.audio-impl_default"],
shared_libs: [
"android.hardware.audio@6.0",
+ "android.hardware.audio@6.0-util",
"android.hardware.audio.common@6.0",
"android.hardware.audio.common@6.0-util",
],
@@ -129,6 +132,7 @@
defaults: ["android.hardware.audio-impl_default"],
shared_libs: [
"android.hardware.audio@7.0",
+ "android.hardware.audio@7.0-util",
"android.hardware.audio.common@7.0",
"android.hardware.audio.common@7.0-enums",
"android.hardware.audio.common@7.0-util",
diff --git a/audio/core/all-versions/default/Conversions.cpp b/audio/core/all-versions/default/Conversions.cpp
deleted file mode 100644
index f1752cc..0000000
--- a/audio/core/all-versions/default/Conversions.cpp
+++ /dev/null
@@ -1,230 +0,0 @@
-/*
- * Copyright (C) 2018 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "core/default/Conversions.h"
-
-#include <stdio.h>
-
-#if MAJOR_VERSION >= 7
-#include <android_audio_policy_configuration_V7_0-enums.h>
-#endif
-#include <HidlUtils.h>
-#include <log/log.h>
-
-namespace android {
-namespace hardware {
-namespace audio {
-namespace CPP_VERSION {
-namespace implementation {
-
-using ::android::hardware::audio::common::CPP_VERSION::implementation::HidlUtils;
-
-#define CONVERT_CHECKED(expr, result) \
- if (status_t status = (expr); status != NO_ERROR) { \
- result = status; \
- }
-
-status_t deviceAddressToHal(const DeviceAddress& device, audio_devices_t* halDeviceType,
- char* halDeviceAddress) {
-#if MAJOR_VERSION >= 5
- return HidlUtils::deviceAddressToHal(device, halDeviceType, halDeviceAddress);
-#else
- return HidlUtils::deviceAddressToHalImpl(device, halDeviceType, halDeviceAddress);
-#endif
-}
-
-status_t deviceAddressFromHal(audio_devices_t halDeviceType, const char* halDeviceAddress,
- DeviceAddress* device) {
-#if MAJOR_VERSION >= 5
- return HidlUtils::deviceAddressFromHal(halDeviceType, halDeviceAddress, device);
-#else
- return HidlUtils::deviceAddressFromHalImpl(halDeviceType, halDeviceAddress, device);
-#endif
-}
-
-#if MAJOR_VERSION >= 4
-bool halToMicrophoneCharacteristics(MicrophoneInfo* pDst,
- const struct audio_microphone_characteristic_t& src) {
- bool status = false;
- if (pDst != NULL) {
- pDst->deviceId = src.device_id;
-
- if (deviceAddressFromHal(src.device, src.address, &pDst->deviceAddress) != OK) {
- return false;
- }
- pDst->channelMapping.resize(AUDIO_CHANNEL_COUNT_MAX);
- for (size_t ch = 0; ch < pDst->channelMapping.size(); ch++) {
- pDst->channelMapping[ch] = AudioMicrophoneChannelMapping(src.channel_mapping[ch]);
- }
- pDst->location = AudioMicrophoneLocation(src.location);
- pDst->group = (AudioMicrophoneGroup)src.group;
- pDst->indexInTheGroup = (uint32_t)src.index_in_the_group;
- pDst->sensitivity = src.sensitivity;
- pDst->maxSpl = src.max_spl;
- pDst->minSpl = src.min_spl;
- pDst->directionality = AudioMicrophoneDirectionality(src.directionality);
- pDst->frequencyResponse.resize(src.num_frequency_responses);
- for (size_t k = 0; k < src.num_frequency_responses; k++) {
- pDst->frequencyResponse[k].frequency = src.frequency_responses[0][k];
- pDst->frequencyResponse[k].level = src.frequency_responses[1][k];
- }
- pDst->position.x = src.geometric_location.x;
- pDst->position.y = src.geometric_location.y;
- pDst->position.z = src.geometric_location.z;
-
- pDst->orientation.x = src.orientation.x;
- pDst->orientation.y = src.orientation.y;
- pDst->orientation.z = src.orientation.z;
-
- status = true;
- }
- return status;
-}
-
-status_t sinkMetadataToHal(const SinkMetadata& sinkMetadata,
- std::vector<record_track_metadata>* halTracks) {
- status_t result = NO_ERROR;
- if (halTracks != nullptr) {
- halTracks->reserve(sinkMetadata.tracks.size());
- }
- for (auto& metadata : sinkMetadata.tracks) {
- record_track_metadata halTrackMetadata{.gain = metadata.gain};
- CONVERT_CHECKED(HidlUtils::audioSourceToHal(metadata.source, &halTrackMetadata.source),
- result);
-#if MAJOR_VERSION >= 5
- if (metadata.destination.getDiscriminator() ==
- RecordTrackMetadata::Destination::hidl_discriminator::device) {
- CONVERT_CHECKED(
- deviceAddressToHal(metadata.destination.device(), &halTrackMetadata.dest_device,
- halTrackMetadata.dest_device_address),
- result);
- }
-#endif
- if (halTracks != nullptr) {
- halTracks->push_back(std::move(halTrackMetadata));
- }
- }
- return result;
-}
-
-status_t sourceMetadataToHal(const SourceMetadata& sourceMetadata,
- std::vector<playback_track_metadata_t>* halTracks) {
- status_t result = NO_ERROR;
- if (halTracks != nullptr) {
- halTracks->reserve(sourceMetadata.tracks.size());
- }
- for (auto& metadata : sourceMetadata.tracks) {
- playback_track_metadata_t halTrackMetadata{.gain = metadata.gain};
- CONVERT_CHECKED(HidlUtils::audioUsageToHal(metadata.usage, &halTrackMetadata.usage),
- result);
- CONVERT_CHECKED(HidlUtils::audioContentTypeToHal(metadata.contentType,
- &halTrackMetadata.content_type),
- result);
- if (halTracks != nullptr) {
- halTracks->push_back(std::move(halTrackMetadata));
- }
- }
- return result;
-}
-#endif // MAJOR_VERSION >= 4
-
-#if MAJOR_VERSION >= 7
-namespace xsd {
-using namespace ::android::audio::policy::configuration::V7_0;
-}
-
-bool audioInputFlagsToHal(const hidl_vec<AudioInOutFlag>& flags, audio_input_flags_t* halFlags) {
- bool success = true;
- *halFlags = {};
- for (const auto& flag : flags) {
- audio_input_flags_t halFlag;
- if (!xsd::isUnknownAudioInOutFlag(flag) &&
- audio_input_flag_from_string(flag.c_str(), &halFlag)) {
- *halFlags = static_cast<audio_input_flags_t>(*halFlags | halFlag);
- } else {
- ALOGE("Unknown audio input flag \"%s\"", flag.c_str());
- success = false;
- }
- }
- return success;
-}
-
-bool audioOutputFlagsToHal(const hidl_vec<AudioInOutFlag>& flags, audio_output_flags_t* halFlags) {
- bool success = true;
- *halFlags = {};
- for (const auto& flag : flags) {
- audio_output_flags_t halFlag;
- if (!xsd::isUnknownAudioInOutFlag(flag) &&
- audio_output_flag_from_string(flag.c_str(), &halFlag)) {
- *halFlags = static_cast<audio_output_flags_t>(*halFlags | halFlag);
- } else {
- ALOGE("Unknown audio output flag \"%s\"", flag.c_str());
- success = false;
- }
- }
- return success;
-}
-
-status_t sinkMetadataToHalV7(const SinkMetadata& sinkMetadata,
- std::vector<record_track_metadata_v7_t>* halTracks) {
- std::vector<record_track_metadata> bases;
- status_t result = sinkMetadataToHal(sinkMetadata, halTracks != nullptr ? &bases : nullptr);
- if (halTracks != nullptr) {
- halTracks->reserve(bases.size());
- }
- auto baseIter = std::make_move_iterator(bases.begin());
- for (auto& metadata : sinkMetadata.tracks) {
- record_track_metadata_v7_t halTrackMetadata;
- CONVERT_CHECKED(HidlUtils::audioChannelMaskToHal(metadata.channelMask,
- &halTrackMetadata.channel_mask),
- result);
- CONVERT_CHECKED(HidlUtils::audioTagsToHal(metadata.tags, halTrackMetadata.tags), result);
- if (halTracks != nullptr) {
- halTrackMetadata.base = std::move(*baseIter++);
- halTracks->push_back(std::move(halTrackMetadata));
- }
- }
- return result;
-}
-
-status_t sourceMetadataToHalV7(const SourceMetadata& sourceMetadata,
- std::vector<playback_track_metadata_v7_t>* halTracks) {
- std::vector<playback_track_metadata_t> bases;
- status_t result = sourceMetadataToHal(sourceMetadata, halTracks != nullptr ? &bases : nullptr);
- if (halTracks != nullptr) {
- halTracks->reserve(bases.size());
- }
- auto baseIter = std::make_move_iterator(bases.begin());
- for (auto& metadata : sourceMetadata.tracks) {
- playback_track_metadata_v7_t halTrackMetadata;
- CONVERT_CHECKED(HidlUtils::audioChannelMaskToHal(metadata.channelMask,
- &halTrackMetadata.channel_mask),
- result);
- CONVERT_CHECKED(HidlUtils::audioTagsToHal(metadata.tags, halTrackMetadata.tags), result);
- if (halTracks != nullptr) {
- halTrackMetadata.base = std::move(*baseIter++);
- halTracks->push_back(std::move(halTrackMetadata));
- }
- }
- return result;
-}
-#endif
-
-} // namespace implementation
-} // namespace CPP_VERSION
-} // namespace audio
-} // namespace hardware
-} // namespace android
diff --git a/audio/core/all-versions/default/Device.cpp b/audio/core/all-versions/default/Device.cpp
index 7caed44..70a1a4d 100644
--- a/audio/core/all-versions/default/Device.cpp
+++ b/audio/core/all-versions/default/Device.cpp
@@ -17,9 +17,7 @@
#define LOG_TAG "DeviceHAL"
#include "core/default/Device.h"
-#include <HidlUtils.h>
#include "common/all-versions/default/EffectMap.h"
-#include "core/default/Conversions.h"
#include "core/default/StreamIn.h"
#include "core/default/StreamOut.h"
#include "core/default/Util.h"
@@ -33,6 +31,8 @@
#include <android/log.h>
+#include <HidlUtils.h>
+
namespace android {
namespace hardware {
namespace audio {
@@ -160,11 +160,11 @@
audio_stream_out_t* halStream;
audio_devices_t halDevice;
char halDeviceAddress[AUDIO_DEVICE_MAX_ADDRESS_LEN];
- if (deviceAddressToHal(device, &halDevice, halDeviceAddress) != NO_ERROR) {
+ if (CoreUtils::deviceAddressToHal(device, &halDevice, halDeviceAddress) != NO_ERROR) {
return {Result::INVALID_ARGUMENTS, nullptr};
}
audio_output_flags_t halFlags;
- if (!audioOutputFlagsToHal(flags, &halFlags)) {
+ if (CoreUtils::audioOutputFlagsToHal(flags, &halFlags) != NO_ERROR) {
return {Result::INVALID_ARGUMENTS, nullptr};
}
ALOGV("open_output_stream handle: %d devices: %x flags: %#x "
@@ -195,12 +195,12 @@
audio_stream_in_t* halStream;
audio_devices_t halDevice;
char halDeviceAddress[AUDIO_DEVICE_MAX_ADDRESS_LEN];
- if (deviceAddressToHal(device, &halDevice, halDeviceAddress) != NO_ERROR) {
+ if (CoreUtils::deviceAddressToHal(device, &halDevice, halDeviceAddress) != NO_ERROR) {
return {Result::INVALID_ARGUMENTS, nullptr};
}
audio_input_flags_t halFlags;
audio_source_t halSource;
- if (!audioInputFlagsToHal(flags, &halFlags) ||
+ if (CoreUtils::audioInputFlagsToHal(flags, &halFlags) != NO_ERROR ||
HidlUtils::audioSourceToHal(source, &halSource) != NO_ERROR) {
return {Result::INVALID_ARGUMENTS, nullptr};
}
@@ -254,9 +254,12 @@
const SourceMetadata& sourceMetadata,
openOutputStream_cb _hidl_cb) {
#if MAJOR_VERSION <= 6
- if (status_t status = sourceMetadataToHal(sourceMetadata, nullptr); status != NO_ERROR) {
+ if (status_t status = CoreUtils::sourceMetadataToHal(sourceMetadata, nullptr);
+ status != NO_ERROR) {
#else
- if (status_t status = sourceMetadataToHalV7(sourceMetadata, nullptr); status != NO_ERROR) {
+ if (status_t status = CoreUtils::sourceMetadataToHalV7(sourceMetadata,
+ false /*ignoreNonVendorTags*/, nullptr);
+ status != NO_ERROR) {
#endif
_hidl_cb(analyzeStatus("sourceMetadataToHal", status), nullptr, AudioConfig{});
return Void();
@@ -288,9 +291,11 @@
return Void();
}
#if MAJOR_VERSION <= 6
- if (status_t status = sinkMetadataToHal(sinkMetadata, nullptr); status != NO_ERROR) {
+ if (status_t status = CoreUtils::sinkMetadataToHal(sinkMetadata, nullptr); status != NO_ERROR) {
#else
- if (status_t status = sinkMetadataToHalV7(sinkMetadata, nullptr); status != NO_ERROR) {
+ if (status_t status = CoreUtils::sinkMetadataToHalV7(sinkMetadata,
+ false /*ignoreNonVendorTags*/, nullptr);
+ status != NO_ERROR) {
#endif
_hidl_cb(analyzeStatus("sinkMetadataToHal", status), nullptr, AudioConfig{});
return Void();
@@ -444,7 +449,7 @@
mDevice->get_microphones(mDevice, &mic_array[0], &actual_mics) == 0) {
microphones.resize(actual_mics);
for (size_t i = 0; i < actual_mics; ++i) {
- halToMicrophoneCharacteristics(µphones[i], mic_array[i]);
+ (void)CoreUtils::microphoneInfoFromHal(mic_array[i], µphones[i]);
}
retval = Result::OK;
}
diff --git a/audio/core/all-versions/default/ParametersUtil.cpp b/audio/core/all-versions/default/ParametersUtil.cpp
index 694eb73..4d53645 100644
--- a/audio/core/all-versions/default/ParametersUtil.cpp
+++ b/audio/core/all-versions/default/ParametersUtil.cpp
@@ -15,11 +15,12 @@
*/
#include "core/default/ParametersUtil.h"
-#include "core/default/Conversions.h"
#include "core/default/Util.h"
#include <system/audio.h>
+#include <util/CoreUtils.h>
+
namespace android {
namespace hardware {
namespace audio {
@@ -153,7 +154,7 @@
Result ParametersUtil::setParam(const char* name, const DeviceAddress& address) {
audio_devices_t halDeviceType;
char halDeviceAddress[AUDIO_DEVICE_MAX_ADDRESS_LEN];
- if (deviceAddressToHal(address, &halDeviceType, halDeviceAddress) != NO_ERROR) {
+ if (CoreUtils::deviceAddressToHal(address, &halDeviceType, halDeviceAddress) != NO_ERROR) {
return Result::INVALID_ARGUMENTS;
}
AudioParameter params{String8(halDeviceAddress)};
diff --git a/audio/core/all-versions/default/Stream.cpp b/audio/core/all-versions/default/Stream.cpp
index 3f8efd2..7e32573 100644
--- a/audio/core/all-versions/default/Stream.cpp
+++ b/audio/core/all-versions/default/Stream.cpp
@@ -19,7 +19,6 @@
#include "core/default/Stream.h"
#include "common/all-versions/HidlSupport.h"
#include "common/all-versions/default/EffectMap.h"
-#include "core/default/Conversions.h"
#include "core/default/Util.h"
#include <inttypes.h>
@@ -30,6 +29,7 @@
#include <hardware/audio_effect.h>
#include <media/AudioContainers.h>
#include <media/TypeConverter.h>
+#include <util/CoreUtils.h>
namespace android {
namespace hardware {
@@ -373,9 +373,10 @@
hidl_vec<DeviceAddress> devices;
if (retval == Result::OK) {
devices.resize(1);
- retval = Stream::analyzeStatus("get_devices",
- deviceAddressFromHal(static_cast<audio_devices_t>(halDevice),
- nullptr, &devices[0]));
+ retval = Stream::analyzeStatus(
+ "get_devices",
+ CoreUtils::deviceAddressFromHal(static_cast<audio_devices_t>(halDevice), nullptr,
+ &devices[0]));
}
_hidl_cb(retval, devices);
return Void();
diff --git a/audio/core/all-versions/default/StreamIn.cpp b/audio/core/all-versions/default/StreamIn.cpp
index c670a4d..599f3c3 100644
--- a/audio/core/all-versions/default/StreamIn.cpp
+++ b/audio/core/all-versions/default/StreamIn.cpp
@@ -17,7 +17,6 @@
#define LOG_TAG "StreamInHAL"
#include "core/default/StreamIn.h"
-#include "core/default/Conversions.h"
#include "core/default/Util.h"
#include "common/all-versions/HidlSupport.h"
@@ -27,6 +26,7 @@
#include <HidlUtils.h>
#include <android/log.h>
#include <hardware/audio.h>
+#include <util/CoreUtils.h>
#include <utils/Trace.h>
#include <cmath>
#include <memory>
@@ -481,13 +481,15 @@
Result StreamIn::doUpdateSinkMetadata(const SinkMetadata& sinkMetadata) {
std::vector<record_track_metadata> halTracks;
#if MAJOR_VERSION <= 6
- (void)sinkMetadataToHal(sinkMetadata, &halTracks);
+ (void)CoreUtils::sinkMetadataToHal(sinkMetadata, &halTracks);
#else
// Validate whether a conversion to V7 is possible. This is needed
// to have a consistent behavior of the HAL regardless of the API
// version of the legacy HAL (and also to be consistent with openInputStream).
std::vector<record_track_metadata_v7> halTracksV7;
- if (status_t status = sinkMetadataToHalV7(sinkMetadata, &halTracksV7); status == NO_ERROR) {
+ if (status_t status = CoreUtils::sinkMetadataToHalV7(
+ sinkMetadata, false /*ignoreNonVendorTags*/, &halTracksV7);
+ status == NO_ERROR) {
halTracks.reserve(halTracksV7.size());
for (auto metadata_v7 : halTracksV7) {
halTracks.push_back(std::move(metadata_v7.base));
@@ -507,7 +509,9 @@
#if MAJOR_VERSION >= 7
Result StreamIn::doUpdateSinkMetadataV7(const SinkMetadata& sinkMetadata) {
std::vector<record_track_metadata_v7> halTracks;
- if (status_t status = sinkMetadataToHalV7(sinkMetadata, &halTracks); status != NO_ERROR) {
+ if (status_t status = CoreUtils::sinkMetadataToHalV7(sinkMetadata,
+ false /*ignoreNonVendorTags*/, &halTracks);
+ status != NO_ERROR) {
return Stream::analyzeStatus("sinkMetadataToHal", status);
}
const sink_metadata_v7_t halMetadata = {
@@ -553,7 +557,7 @@
mStream->get_active_microphones(mStream, &mic_array[0], &actual_mics) == 0) {
microphones.resize(actual_mics);
for (size_t i = 0; i < actual_mics; ++i) {
- halToMicrophoneCharacteristics(µphones[i], mic_array[i]);
+ (void)CoreUtils::microphoneInfoFromHal(mic_array[i], µphones[i]);
}
retval = Result::OK;
}
diff --git a/audio/core/all-versions/default/StreamOut.cpp b/audio/core/all-versions/default/StreamOut.cpp
index 233575c..a089f6b 100644
--- a/audio/core/all-versions/default/StreamOut.cpp
+++ b/audio/core/all-versions/default/StreamOut.cpp
@@ -17,7 +17,6 @@
#define LOG_TAG "StreamOutHAL"
#include "core/default/StreamOut.h"
-#include "core/default/Conversions.h"
#include "core/default/Util.h"
//#define LOG_NDEBUG 0
@@ -30,6 +29,7 @@
#include <HidlUtils.h>
#include <android/log.h>
#include <hardware/audio.h>
+#include <util/CoreUtils.h>
#include <utils/Trace.h>
namespace android {
@@ -589,13 +589,15 @@
Result StreamOut::doUpdateSourceMetadata(const SourceMetadata& sourceMetadata) {
std::vector<playback_track_metadata_t> halTracks;
#if MAJOR_VERSION <= 6
- (void)sourceMetadataToHal(sourceMetadata, &halTracks);
+ (void)CoreUtils::sourceMetadataToHal(sourceMetadata, &halTracks);
#else
// Validate whether a conversion to V7 is possible. This is needed
// to have a consistent behavior of the HAL regardless of the API
// version of the legacy HAL (and also to be consistent with openOutputStream).
std::vector<playback_track_metadata_v7> halTracksV7;
- if (status_t status = sourceMetadataToHalV7(sourceMetadata, &halTracksV7); status == NO_ERROR) {
+ if (status_t status = CoreUtils::sourceMetadataToHalV7(
+ sourceMetadata, false /*ignoreNonVendorTags*/, &halTracksV7);
+ status == NO_ERROR) {
halTracks.reserve(halTracksV7.size());
for (auto metadata_v7 : halTracksV7) {
halTracks.push_back(std::move(metadata_v7.base));
@@ -615,7 +617,9 @@
#if MAJOR_VERSION >= 7
Result StreamOut::doUpdateSourceMetadataV7(const SourceMetadata& sourceMetadata) {
std::vector<playback_track_metadata_v7> halTracks;
- if (status_t status = sourceMetadataToHalV7(sourceMetadata, &halTracks); status != NO_ERROR) {
+ if (status_t status = CoreUtils::sourceMetadataToHalV7(
+ sourceMetadata, false /*ignoreNonVendorTags*/, &halTracks);
+ status != NO_ERROR) {
return Stream::analyzeStatus("sourceMetadataToHal", status);
}
const source_metadata_v7_t halMetadata = {
diff --git a/audio/core/all-versions/default/TEST_MAPPING b/audio/core/all-versions/default/TEST_MAPPING
new file mode 100644
index 0000000..d53c97a
--- /dev/null
+++ b/audio/core/all-versions/default/TEST_MAPPING
@@ -0,0 +1,7 @@
+{
+ "presubmit": [
+ {
+ "name": "android.hardware.audio@7.0-util_tests"
+ }
+ ]
+}
diff --git a/audio/core/all-versions/default/include/core/default/Conversions.h b/audio/core/all-versions/default/include/core/default/Conversions.h
deleted file mode 100644
index 61720d5..0000000
--- a/audio/core/all-versions/default/include/core/default/Conversions.h
+++ /dev/null
@@ -1,83 +0,0 @@
-/*
- * Copyright (C) 2018 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef ANDROID_HARDWARE_AUDIO_CONVERSIONS_H_
-#define ANDROID_HARDWARE_AUDIO_CONVERSIONS_H_
-
-#include PATH(android/hardware/audio/FILE_VERSION/types.h)
-
-#include <string>
-
-#include <system/audio.h>
-
-#include <VersionUtils.h>
-
-namespace android {
-namespace hardware {
-namespace audio {
-namespace CPP_VERSION {
-namespace implementation {
-
-using ::android::hardware::hidl_vec;
-using namespace ::android::hardware::audio::common::CPP_VERSION;
-using namespace ::android::hardware::audio::CPP_VERSION;
-
-status_t deviceAddressToHal(const DeviceAddress& device, audio_devices_t* halDeviceType,
- char* halDeviceAddress);
-status_t deviceAddressFromHal(audio_devices_t halDeviceType, const char* halDeviceAddress,
- DeviceAddress* device);
-
-#if MAJOR_VERSION >= 4
-bool halToMicrophoneCharacteristics(MicrophoneInfo* pDst,
- const struct audio_microphone_characteristic_t& src);
-status_t sinkMetadataToHal(const SinkMetadata& sinkMetadata,
- std::vector<record_track_metadata>* halTracks);
-status_t sourceMetadataToHal(const SourceMetadata& sourceMetadata,
- std::vector<playback_track_metadata_t>* halTracks);
-#endif
-
-#if MAJOR_VERSION <= 6
-using AudioInputFlags =
- ::android::hardware::audio::common::CPP_VERSION::implementation::AudioInputFlagBitfield;
-using AudioOutputFlags =
- ::android::hardware::audio::common::CPP_VERSION::implementation::AudioOutputFlagBitfield;
-
-inline bool audioInputFlagsToHal(AudioInputFlags flags, audio_input_flags_t* halFlags) {
- *halFlags = static_cast<audio_input_flags_t>(flags);
- return true;
-}
-
-inline bool audioOutputFlagsToHal(AudioOutputFlags flags, audio_output_flags_t* halFlags) {
- *halFlags = static_cast<audio_output_flags_t>(flags);
- return true;
-}
-#else
-bool audioInputFlagsToHal(const hidl_vec<AudioInOutFlag>& flags, audio_input_flags_t* halFlags);
-bool audioOutputFlagsToHal(const hidl_vec<AudioInOutFlag>& flags, audio_output_flags_t* halFlags);
-// Overloading isn't convenient when passing a nullptr.
-status_t sinkMetadataToHalV7(const SinkMetadata& sinkMetadata,
- std::vector<record_track_metadata_v7_t>* halTracks);
-status_t sourceMetadataToHalV7(const SourceMetadata& sourceMetadata,
- std::vector<playback_track_metadata_v7_t>* halTracks);
-#endif
-
-} // namespace implementation
-} // namespace CPP_VERSION
-} // namespace audio
-} // namespace hardware
-} // namespace android
-
-#endif // ANDROID_HARDWARE_AUDIO_CONVERSIONS_H_
diff --git a/audio/core/all-versions/default/include/core/default/Device.h b/audio/core/all-versions/default/include/core/default/Device.h
index 2a4d226..5851fc9 100644
--- a/audio/core/all-versions/default/include/core/default/Device.h
+++ b/audio/core/all-versions/default/include/core/default/Device.h
@@ -31,6 +31,7 @@
#include <hidl/MQDescriptor.h>
#include <VersionUtils.h>
+#include <util/CoreUtils.h>
namespace android {
namespace hardware {
@@ -43,17 +44,10 @@
using ::android::hardware::hidl_vec;
using ::android::hardware::Return;
using ::android::hardware::Void;
-#if MAJOR_VERSION <= 6
-using AudioInputFlags =
- ::android::hardware::audio::common::CPP_VERSION::implementation::AudioInputFlagBitfield;
-using AudioOutputFlags =
- ::android::hardware::audio::common::CPP_VERSION::implementation::AudioOutputFlagBitfield;
-#else
-using AudioInputFlags = hidl_vec<::android::hardware::audio::CPP_VERSION::AudioInOutFlag>;
-using AudioOutputFlags = hidl_vec<::android::hardware::audio::CPP_VERSION::AudioInOutFlag>;
-#endif
using namespace ::android::hardware::audio::common::CPP_VERSION;
using namespace ::android::hardware::audio::CPP_VERSION;
+using AudioInputFlags = CoreUtils::AudioInputFlags;
+using AudioOutputFlags = CoreUtils::AudioOutputFlags;
struct Device : public IDevice, public ParametersUtil {
explicit Device(audio_hw_device_t* device);
diff --git a/audio/core/all-versions/default/util/Android.bp b/audio/core/all-versions/default/util/Android.bp
new file mode 100644
index 0000000..447184b
--- /dev/null
+++ b/audio/core/all-versions/default/util/Android.bp
@@ -0,0 +1,139 @@
+cc_defaults {
+ name: "android.hardware.audio-util_default",
+ defaults: ["hidl_defaults"],
+
+ vendor_available: true,
+
+ export_include_dirs: ["include"],
+
+ srcs: [
+ "CoreUtils.cpp",
+ ],
+
+ shared_libs: [
+ "liblog",
+ "libutils",
+ "libhidlbase",
+ "android.hardware.audio.common-util",
+ ],
+ export_shared_lib_headers: [
+ "android.hardware.audio.common-util",
+ ],
+
+ header_libs: [
+ "libaudio_system_headers",
+ "libhardware_headers",
+ ],
+}
+
+cc_library_shared {
+ name: "android.hardware.audio@2.0-util",
+ defaults: ["android.hardware.audio-util_default"],
+ shared_libs: [
+ "android.hardware.audio.common@2.0",
+ "android.hardware.audio.common@2.0-util",
+ "android.hardware.audio@2.0",
+ ],
+ cflags: [
+ "-DMAJOR_VERSION=2",
+ "-DMINOR_VERSION=0",
+ "-include common/all-versions/VersionMacro.h",
+ ],
+}
+
+cc_library_shared {
+ name: "android.hardware.audio@4.0-util",
+ defaults: ["android.hardware.audio-util_default"],
+ shared_libs: [
+ "android.hardware.audio.common@4.0",
+ "android.hardware.audio.common@4.0-util",
+ "android.hardware.audio@4.0",
+ ],
+ cflags: [
+ "-DMAJOR_VERSION=4",
+ "-DMINOR_VERSION=0",
+ "-include common/all-versions/VersionMacro.h",
+ ],
+}
+
+cc_library_shared {
+ name: "android.hardware.audio@5.0-util",
+ defaults: ["android.hardware.audio-util_default"],
+ shared_libs: [
+ "android.hardware.audio.common@5.0",
+ "android.hardware.audio.common@5.0-util",
+ "android.hardware.audio@5.0",
+ ],
+ cflags: [
+ "-DMAJOR_VERSION=5",
+ "-DMINOR_VERSION=0",
+ "-include common/all-versions/VersionMacro.h",
+ ],
+}
+
+cc_library_shared {
+ name: "android.hardware.audio@6.0-util",
+ defaults: ["android.hardware.audio-util_default"],
+ shared_libs: [
+ "android.hardware.audio.common@6.0",
+ "android.hardware.audio.common@6.0-util",
+ "android.hardware.audio@6.0",
+ ],
+ cflags: [
+ "-DMAJOR_VERSION=6",
+ "-DMINOR_VERSION=0",
+ "-include common/all-versions/VersionMacro.h",
+ ],
+}
+
+cc_library {
+ name: "android.hardware.audio@7.0-util",
+ defaults: ["android.hardware.audio-util_default"],
+ shared_libs: [
+ "android.hardware.audio.common@7.0",
+ "android.hardware.audio.common@7.0-enums",
+ "android.hardware.audio.common@7.0-util",
+ "android.hardware.audio@7.0",
+ "libbase",
+ "libxml2",
+ ],
+ cflags: [
+ "-DMAJOR_VERSION=7",
+ "-DMINOR_VERSION=0",
+ "-include common/all-versions/VersionMacro.h",
+ ],
+}
+
+// Note: this isn't a VTS test, but rather a unit test
+// to verify correctness of conversion utilities.
+cc_test {
+ name: "android.hardware.audio@7.0-util_tests",
+ defaults: ["android.hardware.audio-util_default"],
+
+ srcs: ["tests/coreutils_tests.cpp"],
+
+ // Use static linking to allow running in presubmit on
+ // targets that don't have HAL V7.
+ static_libs: [
+ "android.hardware.audio.common@7.0",
+ "android.hardware.audio.common@7.0-enums",
+ "android.hardware.audio.common@7.0-util",
+ "android.hardware.audio@7.0",
+ "android.hardware.audio@7.0-util",
+ ],
+
+ shared_libs: [
+ "libbase",
+ "libxml2",
+ ],
+
+ cflags: [
+ "-Werror",
+ "-Wall",
+ "-DMAJOR_VERSION=7",
+ "-DMINOR_VERSION=0",
+ "-include common/all-versions/VersionMacro.h",
+ ],
+
+ test_suites: ["device-tests"],
+}
diff --git a/audio/core/all-versions/default/util/CoreUtils.cpp b/audio/core/all-versions/default/util/CoreUtils.cpp
new file mode 100644
index 0000000..14f76f3
--- /dev/null
+++ b/audio/core/all-versions/default/util/CoreUtils.cpp
@@ -0,0 +1,476 @@
+/*
+ * Copyright (C) 2021 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.
+ */
+
+#if MAJOR_VERSION >= 7
+#include <android_audio_policy_configuration_V7_0-enums.h>
+#endif
+#include <HidlUtils.h>
+#include <log/log.h>
+
+#include "util/CoreUtils.h"
+
+using ::android::hardware::audio::common::CPP_VERSION::implementation::HidlUtils;
+#if MAJOR_VERSION >= 7
+namespace xsd {
+using namespace ::android::audio::policy::configuration::V7_0;
+}
+#endif
+
+namespace android {
+namespace hardware {
+namespace audio {
+namespace CPP_VERSION {
+namespace implementation {
+
+#define CONVERT_CHECKED(expr, result) \
+ if (status_t status = (expr); status != NO_ERROR) { \
+ result = status; \
+ }
+
+status_t CoreUtils::deviceAddressToHal(const DeviceAddress& device, audio_devices_t* halDeviceType,
+ char* halDeviceAddress) {
+#if MAJOR_VERSION >= 5
+ return HidlUtils::deviceAddressToHal(device, halDeviceType, halDeviceAddress);
+#else
+ return HidlUtils::deviceAddressToHalImpl(device, halDeviceType, halDeviceAddress);
+#endif
+}
+
+status_t CoreUtils::deviceAddressFromHal(audio_devices_t halDeviceType,
+ const char* halDeviceAddress, DeviceAddress* device) {
+#if MAJOR_VERSION >= 5
+ return HidlUtils::deviceAddressFromHal(halDeviceType, halDeviceAddress, device);
+#else
+ return HidlUtils::deviceAddressFromHalImpl(halDeviceType, halDeviceAddress, device);
+#endif
+}
+
+#if MAJOR_VERSION >= 4
+status_t CoreUtils::microphoneInfoFromHal(
+ const struct audio_microphone_characteristic_t& halMicInfo, MicrophoneInfo* micInfo) {
+ status_t result = NO_ERROR;
+ micInfo->deviceId = halMicInfo.device_id;
+ CONVERT_CHECKED(
+ deviceAddressFromHal(halMicInfo.device, halMicInfo.address, &micInfo->deviceAddress),
+ result);
+ size_t chCount;
+ for (chCount = 0; chCount < AUDIO_CHANNEL_COUNT_MAX; ++chCount) {
+ if (halMicInfo.channel_mapping[chCount] == AUDIO_MICROPHONE_CHANNEL_MAPPING_UNUSED) {
+ break;
+ }
+ }
+ micInfo->channelMapping.resize(chCount);
+ for (size_t ch = 0; ch < micInfo->channelMapping.size(); ch++) {
+ micInfo->channelMapping[ch] = AudioMicrophoneChannelMapping(halMicInfo.channel_mapping[ch]);
+ }
+ micInfo->location = AudioMicrophoneLocation(halMicInfo.location);
+ micInfo->group = AudioMicrophoneGroup(halMicInfo.group);
+ micInfo->indexInTheGroup = static_cast<uint32_t>(halMicInfo.index_in_the_group);
+ micInfo->sensitivity = halMicInfo.sensitivity;
+ micInfo->maxSpl = halMicInfo.max_spl;
+ micInfo->minSpl = halMicInfo.min_spl;
+ micInfo->directionality = AudioMicrophoneDirectionality(halMicInfo.directionality);
+ micInfo->frequencyResponse.resize(halMicInfo.num_frequency_responses);
+ for (size_t k = 0; k < halMicInfo.num_frequency_responses; k++) {
+ micInfo->frequencyResponse[k].frequency = halMicInfo.frequency_responses[0][k];
+ micInfo->frequencyResponse[k].level = halMicInfo.frequency_responses[1][k];
+ }
+ micInfo->position.x = halMicInfo.geometric_location.x;
+ micInfo->position.y = halMicInfo.geometric_location.y;
+ micInfo->position.z = halMicInfo.geometric_location.z;
+ micInfo->orientation.x = halMicInfo.orientation.x;
+ micInfo->orientation.y = halMicInfo.orientation.y;
+ micInfo->orientation.z = halMicInfo.orientation.z;
+ return result;
+}
+
+status_t CoreUtils::microphoneInfoToHal(const MicrophoneInfo& micInfo,
+ audio_microphone_characteristic_t* halMicInfo) {
+ status_t result = NO_ERROR;
+ strncpy(halMicInfo->device_id, micInfo.deviceId.c_str(), AUDIO_MICROPHONE_ID_MAX_LEN);
+ halMicInfo->device_id[AUDIO_MICROPHONE_ID_MAX_LEN - 1] = '\0';
+ if (micInfo.deviceId.size() >= AUDIO_MICROPHONE_ID_MAX_LEN) {
+ ALOGE("HIDL MicrophoneInfo device ID is too long: %zu", micInfo.deviceId.size());
+ result = BAD_VALUE;
+ }
+ CONVERT_CHECKED(
+ deviceAddressToHal(micInfo.deviceAddress, &halMicInfo->device, halMicInfo->address),
+ result);
+ if (micInfo.channelMapping.size() > AUDIO_CHANNEL_COUNT_MAX) {
+ ALOGE("HIDL MicrophoneInfo has too many channelMapping elements: %zu",
+ micInfo.channelMapping.size());
+ result = BAD_VALUE;
+ }
+ size_t ch;
+ for (ch = 0; ch < micInfo.channelMapping.size() && ch < AUDIO_CHANNEL_COUNT_MAX; ch++) {
+ halMicInfo->channel_mapping[ch] =
+ static_cast<audio_microphone_channel_mapping_t>(micInfo.channelMapping[ch]);
+ }
+ for (; ch < AUDIO_CHANNEL_COUNT_MAX; ch++) {
+ halMicInfo->channel_mapping[ch] = AUDIO_MICROPHONE_CHANNEL_MAPPING_UNUSED;
+ }
+ halMicInfo->location = static_cast<audio_microphone_location_t>(micInfo.location);
+ halMicInfo->group = static_cast<audio_microphone_group_t>(micInfo.group);
+ halMicInfo->index_in_the_group = static_cast<unsigned int>(micInfo.indexInTheGroup);
+ halMicInfo->sensitivity = micInfo.sensitivity;
+ halMicInfo->max_spl = micInfo.maxSpl;
+ halMicInfo->min_spl = micInfo.minSpl;
+ halMicInfo->directionality =
+ static_cast<audio_microphone_directionality_t>(micInfo.directionality);
+ halMicInfo->num_frequency_responses =
+ static_cast<unsigned int>(micInfo.frequencyResponse.size());
+ if (halMicInfo->num_frequency_responses > AUDIO_MICROPHONE_MAX_FREQUENCY_RESPONSES) {
+ ALOGE("HIDL MicrophoneInfo has too many frequency responses: %u",
+ halMicInfo->num_frequency_responses);
+ halMicInfo->num_frequency_responses = AUDIO_MICROPHONE_MAX_FREQUENCY_RESPONSES;
+ result = BAD_VALUE;
+ }
+ for (size_t k = 0; k < halMicInfo->num_frequency_responses; k++) {
+ halMicInfo->frequency_responses[0][k] = micInfo.frequencyResponse[k].frequency;
+ halMicInfo->frequency_responses[1][k] = micInfo.frequencyResponse[k].level;
+ }
+ halMicInfo->geometric_location.x = micInfo.position.x;
+ halMicInfo->geometric_location.y = micInfo.position.y;
+ halMicInfo->geometric_location.z = micInfo.position.z;
+ halMicInfo->orientation.x = micInfo.orientation.x;
+ halMicInfo->orientation.y = micInfo.orientation.y;
+ halMicInfo->orientation.z = micInfo.orientation.z;
+ return result;
+}
+
+status_t CoreUtils::sinkMetadataFromHal(const std::vector<record_track_metadata_t>& halTracks,
+ SinkMetadata* sinkMetadata) {
+ status_t result = NO_ERROR;
+ sinkMetadata->tracks.resize(halTracks.size());
+ for (size_t i = 0; i < sinkMetadata->tracks.size(); ++i) {
+ const auto& halTrackMetadata = halTracks[i];
+ RecordTrackMetadata trackMetadata{};
+ CONVERT_CHECKED(
+ HidlUtils::audioSourceFromHal(halTrackMetadata.source, &trackMetadata.source),
+ result);
+ trackMetadata.gain = halTrackMetadata.gain;
+#if MAJOR_VERSION >= 5
+ if (halTrackMetadata.dest_device != AUDIO_DEVICE_NONE) {
+ DeviceAddress address;
+ if (status_t status =
+ deviceAddressFromHal(halTrackMetadata.dest_device,
+ halTrackMetadata.dest_device_address, &address);
+ status == NO_ERROR) {
+ trackMetadata.destination.device(std::move(address));
+ } else {
+ result = status;
+ }
+ }
+#if MAJOR_VERSION >= 7
+ trackMetadata.channelMask = toString(xsd::AudioChannelMask::AUDIO_CHANNEL_NONE);
+#endif
+#endif // MAJOR_VERSION >= 5
+ sinkMetadata->tracks[i] = std::move(trackMetadata);
+ }
+ return result;
+}
+
+status_t CoreUtils::sinkMetadataFromHalV7(const std::vector<record_track_metadata_v7_t>& halTracks,
+ bool ignoreNonVendorTags, SinkMetadata* sinkMetadata) {
+ std::vector<record_track_metadata_t> bases;
+ bases.reserve(halTracks.size());
+ std::transform(halTracks.begin(), halTracks.end(), std::back_inserter(bases),
+ [](const record_track_metadata_v7_t& src) -> record_track_metadata_t {
+ record_track_metadata_t result;
+ record_track_metadata_from_v7(&result, &src);
+ return result;
+ });
+ status_t result = sinkMetadataFromHal(bases, sinkMetadata);
+#if MAJOR_VERSION >= 7
+ for (size_t i = 0; i < halTracks.size(); ++i) {
+ auto& trackMetadata = sinkMetadata->tracks[i];
+ const auto& halTrackMetadata = halTracks[i];
+ CONVERT_CHECKED(
+ HidlUtils::audioChannelMaskFromHal(halTrackMetadata.channel_mask, true /*isInput*/,
+ &trackMetadata.channelMask),
+ result);
+ std::vector<std::string> strTags = HidlUtils::splitAudioTags(halTrackMetadata.tags);
+ if (ignoreNonVendorTags) {
+ strTags = HidlUtils::filterOutNonVendorTags(strTags);
+ }
+ CONVERT_CHECKED(HidlUtils::audioTagsFromHal(strTags, &trackMetadata.tags), result);
+ }
+#else
+ (void)ignoreNonVendorTags;
+#endif
+ return result;
+}
+
+status_t CoreUtils::sinkMetadataToHal(const SinkMetadata& sinkMetadata,
+ std::vector<record_track_metadata_t>* halTracks) {
+ status_t result = NO_ERROR;
+ if (halTracks != nullptr) {
+ halTracks->reserve(sinkMetadata.tracks.size());
+ }
+ for (auto& trackMetadata : sinkMetadata.tracks) {
+ record_track_metadata halTrackMetadata{.gain = trackMetadata.gain};
+ CONVERT_CHECKED(HidlUtils::audioSourceToHal(trackMetadata.source, &halTrackMetadata.source),
+ result);
+#if MAJOR_VERSION >= 5
+ if (trackMetadata.destination.getDiscriminator() ==
+ RecordTrackMetadata::Destination::hidl_discriminator::device) {
+ CONVERT_CHECKED(deviceAddressToHal(trackMetadata.destination.device(),
+ &halTrackMetadata.dest_device,
+ halTrackMetadata.dest_device_address),
+ result);
+ }
+#endif
+ if (halTracks != nullptr) {
+ halTracks->push_back(std::move(halTrackMetadata));
+ }
+ }
+ return result;
+}
+
+status_t CoreUtils::sinkMetadataToHalV7(const SinkMetadata& sinkMetadata, bool ignoreNonVendorTags,
+ std::vector<record_track_metadata_v7_t>* halTracks) {
+ std::vector<record_track_metadata> bases;
+ status_t result = sinkMetadataToHal(sinkMetadata, halTracks != nullptr ? &bases : nullptr);
+ if (halTracks != nullptr) {
+ halTracks->reserve(sinkMetadata.tracks.size());
+ }
+ for (size_t i = 0; i < sinkMetadata.tracks.size(); ++i) {
+ record_track_metadata_v7_t halTrackMetadata;
+ if (halTracks != nullptr) {
+ record_track_metadata_to_v7(&halTrackMetadata, &bases[i]);
+ }
+#if MAJOR_VERSION >= 7
+ const auto& trackMetadata = sinkMetadata.tracks[i];
+ CONVERT_CHECKED(HidlUtils::audioChannelMaskToHal(trackMetadata.channelMask,
+ &halTrackMetadata.channel_mask),
+ result);
+ if (ignoreNonVendorTags) {
+ CONVERT_CHECKED(
+ HidlUtils::audioTagsToHal(HidlUtils::filterOutNonVendorTags(trackMetadata.tags),
+ halTrackMetadata.tags),
+ result);
+ } else {
+ CONVERT_CHECKED(HidlUtils::audioTagsToHal(trackMetadata.tags, halTrackMetadata.tags),
+ result);
+ }
+#else
+ (void)ignoreNonVendorTags;
+#endif
+ if (halTracks != nullptr) {
+ halTracks->push_back(std::move(halTrackMetadata));
+ }
+ }
+ return result;
+}
+
+status_t CoreUtils::sourceMetadataFromHal(const std::vector<playback_track_metadata_t>& halTracks,
+ SourceMetadata* sourceMetadata) {
+ status_t result = NO_ERROR;
+ sourceMetadata->tracks.resize(halTracks.size());
+ for (size_t i = 0; i < sourceMetadata->tracks.size(); ++i) {
+ const auto& halTrackMetadata = halTracks[i];
+ PlaybackTrackMetadata trackMetadata{};
+ CONVERT_CHECKED(HidlUtils::audioUsageFromHal(halTrackMetadata.usage, &trackMetadata.usage),
+ result);
+ CONVERT_CHECKED(HidlUtils::audioContentTypeFromHal(halTrackMetadata.content_type,
+ &trackMetadata.contentType),
+ result);
+ trackMetadata.gain = halTrackMetadata.gain;
+#if MAJOR_VERSION >= 7
+ trackMetadata.channelMask = toString(xsd::AudioChannelMask::AUDIO_CHANNEL_NONE);
+#endif
+ sourceMetadata->tracks[i] = std::move(trackMetadata);
+ }
+ return result;
+}
+
+status_t CoreUtils::sourceMetadataFromHalV7(
+ const std::vector<playback_track_metadata_v7_t>& halTracks, bool ignoreNonVendorTags,
+ SourceMetadata* sourceMetadata) {
+ std::vector<playback_track_metadata_t> bases;
+ bases.reserve(halTracks.size());
+ std::transform(halTracks.begin(), halTracks.end(), std::back_inserter(bases),
+ [](const playback_track_metadata_v7_t& src) -> playback_track_metadata_t {
+ playback_track_metadata_t result;
+ playback_track_metadata_from_v7(&result, &src);
+ return result;
+ });
+ status_t result = sourceMetadataFromHal(bases, sourceMetadata);
+#if MAJOR_VERSION >= 7
+ for (size_t i = 0; i < halTracks.size(); ++i) {
+ auto& trackMetadata = sourceMetadata->tracks[i];
+ const auto& halTrackMetadata = halTracks[i];
+ CONVERT_CHECKED(
+ HidlUtils::audioChannelMaskFromHal(halTrackMetadata.channel_mask, false /*isInput*/,
+ &trackMetadata.channelMask),
+ result);
+ std::vector<std::string> strTags = HidlUtils::splitAudioTags(halTrackMetadata.tags);
+ if (ignoreNonVendorTags) {
+ strTags = HidlUtils::filterOutNonVendorTags(strTags);
+ }
+ CONVERT_CHECKED(HidlUtils::audioTagsFromHal(strTags, &trackMetadata.tags), result);
+ }
+#else
+ (void)ignoreNonVendorTags;
+#endif
+ return result;
+}
+
+status_t CoreUtils::sourceMetadataToHal(const SourceMetadata& sourceMetadata,
+ std::vector<playback_track_metadata_t>* halTracks) {
+ status_t result = NO_ERROR;
+ if (halTracks != nullptr) {
+ halTracks->reserve(sourceMetadata.tracks.size());
+ }
+ for (auto& trackMetadata : sourceMetadata.tracks) {
+ playback_track_metadata_t halTrackMetadata{.gain = trackMetadata.gain};
+ CONVERT_CHECKED(HidlUtils::audioUsageToHal(trackMetadata.usage, &halTrackMetadata.usage),
+ result);
+ CONVERT_CHECKED(HidlUtils::audioContentTypeToHal(trackMetadata.contentType,
+ &halTrackMetadata.content_type),
+ result);
+ if (halTracks != nullptr) {
+ halTracks->push_back(std::move(halTrackMetadata));
+ }
+ }
+ return result;
+}
+
+status_t CoreUtils::sourceMetadataToHalV7(const SourceMetadata& sourceMetadata,
+ bool ignoreNonVendorTags,
+ std::vector<playback_track_metadata_v7_t>* halTracks) {
+ std::vector<playback_track_metadata_t> bases;
+ status_t result = sourceMetadataToHal(sourceMetadata, halTracks != nullptr ? &bases : nullptr);
+ if (halTracks != nullptr) {
+ halTracks->reserve(sourceMetadata.tracks.size());
+ }
+ for (size_t i = 0; i < sourceMetadata.tracks.size(); ++i) {
+ playback_track_metadata_v7_t halTrackMetadata;
+ if (halTracks != nullptr) {
+ playback_track_metadata_to_v7(&halTrackMetadata, &bases[i]);
+ }
+#if MAJOR_VERSION >= 7
+ const auto& trackMetadata = sourceMetadata.tracks[i];
+ CONVERT_CHECKED(HidlUtils::audioChannelMaskToHal(trackMetadata.channelMask,
+ &halTrackMetadata.channel_mask),
+ result);
+ if (ignoreNonVendorTags) {
+ CONVERT_CHECKED(
+ HidlUtils::audioTagsToHal(HidlUtils::filterOutNonVendorTags(trackMetadata.tags),
+ halTrackMetadata.tags),
+ result);
+ } else {
+ CONVERT_CHECKED(HidlUtils::audioTagsToHal(trackMetadata.tags, halTrackMetadata.tags),
+ result);
+ }
+#else
+ (void)ignoreNonVendorTags;
+#endif
+ if (halTracks != nullptr) {
+ halTracks->push_back(std::move(halTrackMetadata));
+ }
+ }
+ return result;
+}
+#endif // MAJOR_VERSION >= 4
+
+#if MAJOR_VERSION >= 7
+namespace xsd {
+using namespace ::android::audio::policy::configuration::V7_0;
+}
+
+status_t CoreUtils::audioInputFlagsFromHal(audio_input_flags_t halFlagMask,
+ AudioInputFlags* flags) {
+ status_t status = NO_ERROR;
+ std::vector<AudioInOutFlag> result;
+ for (uint32_t bit = 0; halFlagMask != 0 && bit < sizeof(audio_input_flags_t) * 8; ++bit) {
+ audio_input_flags_t flag = static_cast<audio_input_flags_t>(1u << bit);
+ if ((flag & halFlagMask) == flag) {
+ AudioInOutFlag flagStr = audio_input_flag_to_string(flag);
+ if (!flagStr.empty() && !xsd::isUnknownAudioInOutFlag(flagStr)) {
+ result.push_back(flagStr);
+ } else {
+ ALOGE("Unknown audio input flag value 0x%X", flag);
+ status = BAD_VALUE;
+ }
+ halFlagMask = static_cast<audio_input_flags_t>(halFlagMask & ~flag);
+ }
+ }
+ *flags = result;
+ return status;
+}
+
+status_t CoreUtils::audioInputFlagsToHal(const AudioInputFlags& flags,
+ audio_input_flags_t* halFlagMask) {
+ status_t status = NO_ERROR;
+ *halFlagMask = {};
+ for (const auto& flag : flags) {
+ audio_input_flags_t halFlag;
+ if (!xsd::isUnknownAudioInOutFlag(flag) &&
+ audio_input_flag_from_string(flag.c_str(), &halFlag)) {
+ *halFlagMask = static_cast<audio_input_flags_t>(*halFlagMask | halFlag);
+ } else {
+ ALOGE("Unknown audio input flag \"%s\"", flag.c_str());
+ status = BAD_VALUE;
+ }
+ }
+ return status;
+}
+
+status_t CoreUtils::audioOutputFlagsFromHal(audio_output_flags_t halFlagMask,
+ AudioOutputFlags* flags) {
+ status_t status = NO_ERROR;
+ std::vector<AudioInOutFlag> result;
+ for (uint32_t bit = 0; halFlagMask != 0 && bit < sizeof(audio_output_flags_t) * 8; ++bit) {
+ audio_output_flags_t flag = static_cast<audio_output_flags_t>(1u << bit);
+ if ((flag & halFlagMask) == flag) {
+ AudioInOutFlag flagStr = audio_output_flag_to_string(flag);
+ if (!flagStr.empty() && !xsd::isUnknownAudioInOutFlag(flagStr)) {
+ result.push_back(flagStr);
+ } else {
+ ALOGE("Unknown audio output flag value 0x%X", flag);
+ status = BAD_VALUE;
+ }
+ halFlagMask = static_cast<audio_output_flags_t>(halFlagMask & ~flag);
+ }
+ }
+ *flags = result;
+ return status;
+}
+
+status_t CoreUtils::audioOutputFlagsToHal(const AudioOutputFlags& flags,
+ audio_output_flags_t* halFlagMask) {
+ status_t status = NO_ERROR;
+ *halFlagMask = {};
+ for (const auto& flag : flags) {
+ audio_output_flags_t halFlag;
+ if (!xsd::isUnknownAudioInOutFlag(flag) &&
+ audio_output_flag_from_string(flag.c_str(), &halFlag)) {
+ *halFlagMask = static_cast<audio_output_flags_t>(*halFlagMask | halFlag);
+ } else {
+ ALOGE("Unknown audio output flag \"%s\"", flag.c_str());
+ status = BAD_VALUE;
+ }
+ }
+ return status;
+}
+#endif
+
+} // namespace implementation
+} // namespace CPP_VERSION
+} // namespace audio
+} // namespace hardware
+} // namespace android
diff --git a/audio/core/all-versions/default/util/include/util/CoreUtils.h b/audio/core/all-versions/default/util/include/util/CoreUtils.h
new file mode 100644
index 0000000..1e5272a
--- /dev/null
+++ b/audio/core/all-versions/default/util/include/util/CoreUtils.h
@@ -0,0 +1,110 @@
+/*
+ * Copyright (C) 2021 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.
+ */
+
+#pragma once
+
+// clang-format off
+#include PATH(android/hardware/audio/FILE_VERSION/types.h)
+// clang-format off
+
+#include <vector>
+
+#include <system/audio.h>
+
+#include <common/all-versions/VersionUtils.h>
+#include <VersionUtils.h>
+
+namespace android {
+namespace hardware {
+namespace audio {
+namespace CPP_VERSION {
+namespace implementation {
+
+using ::android::hardware::audio::common::utils::EnumBitfield;
+using ::android::hardware::hidl_vec;
+using namespace ::android::hardware::audio::common::CPP_VERSION;
+using namespace ::android::hardware::audio::CPP_VERSION;
+
+struct CoreUtils {
+ // Note: the converters for DeviceAddress have to be in CoreUtils for HAL V4
+ // because DeviceAddress used to be defined in the core HAL. For V5 and above
+ // these functions simply delegate to HidlUtils.
+ static status_t deviceAddressToHal(const DeviceAddress& device, audio_devices_t* halDeviceType,
+ char* halDeviceAddress);
+ static status_t deviceAddressFromHal(audio_devices_t halDeviceType, const char* halDeviceAddress,
+ DeviceAddress* device);
+#if MAJOR_VERSION >= 4
+ static status_t microphoneInfoFromHal(const struct audio_microphone_characteristic_t& halMicInfo,
+ MicrophoneInfo* micInfo);
+ static status_t microphoneInfoToHal(const MicrophoneInfo& micInfo, audio_microphone_characteristic_t* halMicInfo);
+ // Note: {Sink|Source}Metadata types are defined in 'common' (since V5), so they can be used
+ // by the BT HAL. However, the converters are defined here, not in HidlUtils to avoid adding
+ // conditionals to handle V4. The converters are only used by 'core' HAL anyways.
+ static status_t sinkMetadataFromHal(const std::vector<record_track_metadata_t>& halTracks,
+ SinkMetadata* sinkMetadata);
+ static status_t sinkMetadataFromHalV7(const std::vector<record_track_metadata_v7_t>& halTracks,
+ bool ignoreNonVendorTags, SinkMetadata* sinkMetadata);
+ static status_t sinkMetadataToHal(const SinkMetadata& sinkMetadata,
+ std::vector<record_track_metadata_t>* halTracks);
+ static status_t sinkMetadataToHalV7(const SinkMetadata& sinkMetadata, bool ignoreNonVendorTags,
+ std::vector<record_track_metadata_v7_t>* halTracks);
+ static status_t sourceMetadataFromHal(const std::vector<playback_track_metadata_t>& halTracks,
+ SourceMetadata* sourceMetadata);
+ static status_t sourceMetadataFromHalV7(const std::vector<playback_track_metadata_v7_t>& halTracks,
+ bool ignoreNonVendorTags, SourceMetadata* sourceMetadata);
+ static status_t sourceMetadataToHal(const SourceMetadata& sourceMetadata,
+ std::vector<playback_track_metadata_t>* halTracks);
+ static status_t sourceMetadataToHalV7(const SourceMetadata& sourceMetadata, bool ignoreNonVendorTags,
+ std::vector<playback_track_metadata_v7_t>* halTracks);
+#endif
+
+#if MAJOR_VERSION <= 6
+ using AudioInputFlags =
+ ::android::hardware::audio::common::CPP_VERSION::implementation::AudioInputFlagBitfield;
+ using AudioOutputFlags =
+ ::android::hardware::audio::common::CPP_VERSION::implementation::AudioOutputFlagBitfield;
+ static inline status_t audioInputFlagsFromHal(audio_input_flags_t halFlagMask, AudioInputFlags* flags) {
+ *flags = EnumBitfield<AudioInputFlag>(halFlagMask);
+ return NO_ERROR;
+ }
+ static inline status_t audioInputFlagsToHal(AudioInputFlags flags, audio_input_flags_t* halFlagMask) {
+ *halFlagMask = static_cast<audio_input_flags_t>(flags);
+ return NO_ERROR;
+ }
+ static inline status_t audioOutputFlagsFromHal(audio_output_flags_t halFlagMask, AudioOutputFlags* flags) {
+ *flags = EnumBitfield<AudioOutputFlag>(halFlagMask);
+ return NO_ERROR;
+ }
+ static inline status_t audioOutputFlagsToHal(AudioOutputFlags flags, audio_output_flags_t* halFlagMask) {
+ *halFlagMask = static_cast<audio_output_flags_t>(flags);
+ return NO_ERROR;
+ }
+#else
+ using AudioInputFlags = hidl_vec<::android::hardware::audio::CPP_VERSION::AudioInOutFlag>;
+ using AudioOutputFlags = hidl_vec<::android::hardware::audio::CPP_VERSION::AudioInOutFlag>;
+ static status_t audioInputFlagsFromHal(audio_input_flags_t halFlagMask, AudioInputFlags* flags);
+ static status_t audioInputFlagsToHal(const AudioInputFlags& flags, audio_input_flags_t* halFlagMask);
+ static status_t audioOutputFlagsFromHal(audio_output_flags_t halFlagMask, AudioOutputFlags* flags);
+ static status_t audioOutputFlagsToHal(const AudioOutputFlags& flags, audio_output_flags_t* halFlagMask);
+#endif
+
+};
+
+} // namespace implementation
+} // namespace CPP_VERSION
+} // namespace audio
+} // namespace hardware
+} // namespace android
diff --git a/audio/core/all-versions/default/util/tests/coreutils_tests.cpp b/audio/core/all-versions/default/util/tests/coreutils_tests.cpp
new file mode 100644
index 0000000..0c18482
--- /dev/null
+++ b/audio/core/all-versions/default/util/tests/coreutils_tests.cpp
@@ -0,0 +1,525 @@
+/*
+ * Copyright (C) 2021 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 <string>
+#include <vector>
+
+#include <gtest/gtest.h>
+
+#define LOG_TAG "CoreUtils_Test"
+#include <log/log.h>
+
+#include <android_audio_policy_configuration_V7_0-enums.h>
+#include <system/audio.h>
+#include <util/CoreUtils.h>
+#include <xsdc/XsdcSupport.h>
+
+using namespace android;
+using namespace ::android::hardware::audio::common::CPP_VERSION;
+using namespace ::android::hardware::audio::CPP_VERSION;
+using ::android::hardware::hidl_vec;
+using ::android::hardware::audio::CPP_VERSION::implementation::CoreUtils;
+namespace xsd {
+using namespace ::android::audio::policy::configuration::V7_0;
+}
+
+static constexpr audio_channel_mask_t kInvalidHalChannelMask = AUDIO_CHANNEL_INVALID;
+static constexpr audio_content_type_t kInvalidHalContentType =
+ static_cast<audio_content_type_t>(0xFFFFFFFFU);
+static constexpr audio_devices_t kInvalidHalDevice = static_cast<audio_devices_t>(0xFFFFFFFFU);
+static constexpr audio_input_flags_t kInvalidInputFlags =
+ static_cast<audio_input_flags_t>(0xFFFFFFFFU);
+static constexpr audio_output_flags_t kInvalidOutputFlags =
+ static_cast<audio_output_flags_t>(0xFFFFFFFFU);
+// AUDIO_SOURCE_INVALID is framework-only.
+static constexpr audio_source_t kInvalidHalSource = static_cast<audio_source_t>(-1);
+static constexpr audio_usage_t kInvalidHalUsage = static_cast<audio_usage_t>(0xFFFFFFFFU);
+
+static bool isInputFlag(xsd::AudioInOutFlag flag) {
+ return toString(flag).find("_INPUT_FLAG_") != std::string::npos;
+}
+
+static bool isOutputFlag(xsd::AudioInOutFlag flag) {
+ return toString(flag).find("_OUTPUT_FLAG_") != std::string::npos;
+}
+
+TEST(CoreUtils, ConvertInvalidInputFlagMask) {
+ CoreUtils::AudioInputFlags invalid;
+ EXPECT_EQ(BAD_VALUE, CoreUtils::audioInputFlagsFromHal(kInvalidInputFlags, &invalid));
+ audio_input_flags_t halInvalid;
+ invalid.resize(1);
+ invalid[0] = "random string";
+ EXPECT_EQ(BAD_VALUE, CoreUtils::audioInputFlagsToHal(invalid, &halInvalid));
+}
+
+TEST(CoreUtils, ConvertInputFlagMask) {
+ CoreUtils::AudioInputFlags emptyInputFlags;
+ audio_input_flags_t halEmptyInputFlags;
+ EXPECT_EQ(NO_ERROR, CoreUtils::audioInputFlagsToHal(emptyInputFlags, &halEmptyInputFlags));
+ EXPECT_EQ(AUDIO_INPUT_FLAG_NONE, halEmptyInputFlags);
+ CoreUtils::AudioInputFlags emptyInputFlagsBack;
+ EXPECT_EQ(NO_ERROR,
+ CoreUtils::audioInputFlagsFromHal(halEmptyInputFlags, &emptyInputFlagsBack));
+ EXPECT_EQ(emptyInputFlags, emptyInputFlagsBack);
+ CoreUtils::AudioInputFlags emptyInputFlagsFromNone;
+ EXPECT_EQ(NO_ERROR,
+ CoreUtils::audioInputFlagsFromHal(AUDIO_INPUT_FLAG_NONE, &emptyInputFlagsFromNone));
+ EXPECT_EQ(emptyInputFlags, emptyInputFlagsFromNone);
+
+ std::vector<std::string> allEnumValues;
+ for (const auto enumVal : xsdc_enum_range<xsd::AudioInOutFlag>{}) {
+ if (isInputFlag(enumVal)) {
+ allEnumValues.push_back(toString(enumVal));
+ }
+ }
+ CoreUtils::AudioInputFlags allInputFlags;
+ allInputFlags.resize(allEnumValues.size());
+ for (size_t i = 0; i < allEnumValues.size(); ++i) {
+ allInputFlags[i] = allEnumValues[i];
+ }
+ audio_input_flags_t halAllInputFlags;
+ EXPECT_EQ(NO_ERROR, CoreUtils::audioInputFlagsToHal(allInputFlags, &halAllInputFlags));
+ CoreUtils::AudioInputFlags allInputFlagsBack;
+ EXPECT_EQ(NO_ERROR, CoreUtils::audioInputFlagsFromHal(halAllInputFlags, &allInputFlagsBack));
+ EXPECT_EQ(allInputFlags, allInputFlagsBack);
+}
+
+TEST(CoreUtils, ConvertInvalidOutputFlagMask) {
+ CoreUtils::AudioOutputFlags invalid;
+ EXPECT_EQ(BAD_VALUE, CoreUtils::audioOutputFlagsFromHal(kInvalidOutputFlags, &invalid));
+ audio_output_flags_t halInvalid;
+ invalid.resize(1);
+ invalid[0] = "random string";
+ EXPECT_EQ(BAD_VALUE, CoreUtils::audioOutputFlagsToHal(invalid, &halInvalid));
+}
+
+TEST(CoreUtils, ConvertOutputFlagMask) {
+ CoreUtils::AudioOutputFlags emptyOutputFlags;
+ audio_output_flags_t halEmptyOutputFlags;
+ EXPECT_EQ(NO_ERROR, CoreUtils::audioOutputFlagsToHal(emptyOutputFlags, &halEmptyOutputFlags));
+ EXPECT_EQ(AUDIO_OUTPUT_FLAG_NONE, halEmptyOutputFlags);
+ CoreUtils::AudioOutputFlags emptyOutputFlagsBack;
+ EXPECT_EQ(NO_ERROR,
+ CoreUtils::audioOutputFlagsFromHal(halEmptyOutputFlags, &emptyOutputFlagsBack));
+ EXPECT_EQ(emptyOutputFlags, emptyOutputFlagsBack);
+ CoreUtils::AudioOutputFlags emptyOutputFlagsFromNone;
+ EXPECT_EQ(NO_ERROR, CoreUtils::audioOutputFlagsFromHal(AUDIO_OUTPUT_FLAG_NONE,
+ &emptyOutputFlagsFromNone));
+ EXPECT_EQ(emptyOutputFlags, emptyOutputFlagsFromNone);
+
+ std::vector<std::string> allEnumValues;
+ for (const auto enumVal : xsdc_enum_range<xsd::AudioInOutFlag>{}) {
+ if (isOutputFlag(enumVal)) {
+ allEnumValues.push_back(toString(enumVal));
+ }
+ }
+ CoreUtils::AudioOutputFlags allOutputFlags;
+ allOutputFlags.resize(allEnumValues.size());
+ for (size_t i = 0; i < allEnumValues.size(); ++i) {
+ allOutputFlags[i] = allEnumValues[i];
+ }
+ audio_output_flags_t halAllOutputFlags;
+ EXPECT_EQ(NO_ERROR, CoreUtils::audioOutputFlagsToHal(allOutputFlags, &halAllOutputFlags));
+ CoreUtils::AudioOutputFlags allOutputFlagsBack;
+ EXPECT_EQ(NO_ERROR, CoreUtils::audioOutputFlagsFromHal(halAllOutputFlags, &allOutputFlagsBack));
+ EXPECT_EQ(allOutputFlags, allOutputFlagsBack);
+}
+
+static MicrophoneInfo generateValidMicrophoneInfo() {
+ MicrophoneInfo micInfo{};
+ micInfo.deviceAddress.deviceType = toString(xsd::AudioDevice::AUDIO_DEVICE_IN_BUILTIN_MIC);
+ micInfo.channelMapping.resize(1);
+ micInfo.channelMapping[0] = AudioMicrophoneChannelMapping::DIRECT;
+ micInfo.location = AudioMicrophoneLocation::MAINBODY_MOVABLE;
+ micInfo.group = 42;
+ micInfo.indexInTheGroup = 13;
+ micInfo.sensitivity = 65.5;
+ micInfo.maxSpl = 100.5;
+ micInfo.minSpl = 36.6;
+ micInfo.directionality = AudioMicrophoneDirectionality::HYPER_CARDIOID;
+ micInfo.frequencyResponse.resize(1);
+ micInfo.frequencyResponse[0].frequency = 1000;
+ micInfo.frequencyResponse[0].level = 85;
+ micInfo.position.x = 0;
+ micInfo.position.y = 1;
+ micInfo.position.z = 0;
+ micInfo.orientation.x = 0;
+ micInfo.orientation.y = 0;
+ micInfo.orientation.z = 1;
+ return micInfo;
+}
+
+TEST(CoreUtils, ConvertInvalidMicrophoneInfo) {
+ MicrophoneInfo invalid;
+ audio_microphone_characteristic_t halInvalid{};
+ halInvalid.device = kInvalidHalDevice;
+ EXPECT_EQ(BAD_VALUE, CoreUtils::microphoneInfoFromHal(halInvalid, &invalid));
+
+ MicrophoneInfo oversizeDeviceId = generateValidMicrophoneInfo();
+ oversizeDeviceId.deviceId = std::string(AUDIO_MICROPHONE_ID_MAX_LEN + 1, 'A');
+ EXPECT_EQ(BAD_VALUE, CoreUtils::microphoneInfoToHal(oversizeDeviceId, &halInvalid));
+ MicrophoneInfo invalidDeviceType = generateValidMicrophoneInfo();
+ invalidDeviceType.deviceAddress.deviceType = "random string";
+ EXPECT_EQ(BAD_VALUE, CoreUtils::microphoneInfoToHal(invalidDeviceType, &halInvalid));
+ MicrophoneInfo oversizeChannelMapping = generateValidMicrophoneInfo();
+ oversizeChannelMapping.channelMapping.resize(AUDIO_CHANNEL_COUNT_MAX + 1);
+ EXPECT_EQ(BAD_VALUE, CoreUtils::microphoneInfoToHal(oversizeChannelMapping, &halInvalid));
+ MicrophoneInfo oversizeFrequencyResponses = generateValidMicrophoneInfo();
+ oversizeFrequencyResponses.frequencyResponse.resize(AUDIO_MICROPHONE_MAX_FREQUENCY_RESPONSES +
+ 1);
+ EXPECT_EQ(BAD_VALUE, CoreUtils::microphoneInfoToHal(oversizeFrequencyResponses, &halInvalid));
+}
+
+TEST(CoreUtils, ConvertMicrophoneInfo) {
+ MicrophoneInfo micInfo = generateValidMicrophoneInfo();
+ audio_microphone_characteristic_t halMicInfo;
+ EXPECT_EQ(NO_ERROR, CoreUtils::microphoneInfoToHal(micInfo, &halMicInfo));
+ MicrophoneInfo micInfoBack;
+ EXPECT_EQ(NO_ERROR, CoreUtils::microphoneInfoFromHal(halMicInfo, &micInfoBack));
+ EXPECT_EQ(micInfo, micInfoBack);
+}
+
+static RecordTrackMetadata generateMinimalRecordTrackMetadata() {
+ RecordTrackMetadata metadata{};
+ metadata.source = toString(xsd::AudioSource::AUDIO_SOURCE_DEFAULT);
+ metadata.channelMask = toString(xsd::AudioChannelMask::AUDIO_CHANNEL_NONE);
+ return metadata;
+}
+
+static RecordTrackMetadata generateValidRecordTrackMetadata() {
+ RecordTrackMetadata metadata = generateMinimalRecordTrackMetadata();
+ metadata.tags.resize(1);
+ metadata.tags[0] = "VX_GOOGLE_42";
+ metadata.channelMask = toString(xsd::AudioChannelMask::AUDIO_CHANNEL_IN_MONO);
+ metadata.gain = 1.0;
+ return metadata;
+}
+
+static RecordTrackMetadata generateValidRecordTrackMetadataWithDevice() {
+ RecordTrackMetadata metadata = generateValidRecordTrackMetadata();
+ metadata.destination.device({});
+ metadata.destination.device().deviceType = toString(xsd::AudioDevice::AUDIO_DEVICE_OUT_SPEAKER);
+ return metadata;
+}
+
+using SinkTracks = hidl_vec<RecordTrackMetadata>;
+
+TEST(CoreUtils, ConvertInvalidSinkMetadata) {
+ SinkMetadata invalidSource;
+ invalidSource.tracks = SinkTracks{generateMinimalRecordTrackMetadata()};
+ invalidSource.tracks[0].source = "random string";
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sinkMetadataToHal(invalidSource, nullptr));
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sinkMetadataToHalV7(invalidSource,
+ false /*ignoreNonVendorTags*/, nullptr));
+ EXPECT_EQ(BAD_VALUE,
+ CoreUtils::sinkMetadataToHalV7(invalidSource, true /*ignoreNonVendorTags*/, nullptr));
+ SinkMetadata invalidDeviceType;
+ invalidDeviceType.tracks = SinkTracks{generateValidRecordTrackMetadataWithDevice()};
+ invalidDeviceType.tracks[0].destination.device().deviceType = "random string";
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sinkMetadataToHal(invalidDeviceType, nullptr));
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sinkMetadataToHalV7(invalidDeviceType,
+ false /*ignoreNonVendorTags*/, nullptr));
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sinkMetadataToHalV7(invalidDeviceType,
+ true /*ignoreNonVendorTags*/, nullptr));
+ SinkMetadata invalidChannelMask;
+ invalidChannelMask.tracks = SinkTracks{generateValidRecordTrackMetadata()};
+ invalidChannelMask.tracks[0].channelMask = "random string";
+ // Channel mask is sliced away by 'sinkMetadataToHal'
+ EXPECT_EQ(NO_ERROR, CoreUtils::sinkMetadataToHal(invalidChannelMask, nullptr));
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sinkMetadataToHalV7(invalidChannelMask,
+ false /*ignoreNonVendorTags*/, nullptr));
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sinkMetadataToHalV7(invalidChannelMask,
+ true /*ignoreNonVendorTags*/, nullptr));
+ SinkMetadata invalidTags;
+ invalidTags.tracks = SinkTracks{generateValidRecordTrackMetadata()};
+ invalidTags.tracks[0].tags[0] = "random string";
+ // Tags are sliced away by 'sinkMetadataToHal'
+ EXPECT_EQ(NO_ERROR, CoreUtils::sinkMetadataToHal(invalidTags, nullptr));
+ EXPECT_EQ(BAD_VALUE,
+ CoreUtils::sinkMetadataToHalV7(invalidTags, false /*ignoreNonVendorTags*/, nullptr));
+ // Non-vendor tags should be filtered out.
+ EXPECT_EQ(NO_ERROR,
+ CoreUtils::sinkMetadataToHalV7(invalidTags, true /*ignoreNonVendorTags*/, nullptr));
+
+ // Verify that a default-initialized metadata is valid.
+ std::vector<record_track_metadata_t> halValid(1, record_track_metadata_t{});
+ std::vector<record_track_metadata_v7_t> halValidV7(1, record_track_metadata_v7_t{});
+ SinkMetadata valid;
+ EXPECT_EQ(NO_ERROR, CoreUtils::sinkMetadataFromHal(halValid, &valid));
+ EXPECT_EQ(NO_ERROR,
+ CoreUtils::sinkMetadataFromHalV7(halValidV7, false /*ignoreNonVendorTags*/, &valid));
+ EXPECT_EQ(NO_ERROR,
+ CoreUtils::sinkMetadataFromHalV7(halValidV7, true /*ignoreNonVendorTags*/, &valid));
+
+ std::vector<record_track_metadata_t> halInvalidSource = {{.source = kInvalidHalSource}};
+ std::vector<record_track_metadata_v7_t> halInvalidSourceV7 = {
+ {.base = {.source = kInvalidHalSource}}};
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sinkMetadataFromHal(halInvalidSource, &invalidSource));
+ EXPECT_EQ(BAD_VALUE,
+ CoreUtils::sinkMetadataFromHalV7(halInvalidSourceV7, false /*ignoreNonVendorTags*/,
+ &invalidSource));
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sinkMetadataFromHalV7(
+ halInvalidSourceV7, true /*ignoreNonVendorTags*/, &invalidSource));
+ std::vector<record_track_metadata_t> halInvalidDeviceType = {
+ {.dest_device = kInvalidHalDevice}};
+ std::vector<record_track_metadata_v7_t> halInvalidDeviceTypeV7 = {
+ {.base = {.dest_device = kInvalidHalDevice}}};
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sinkMetadataFromHal(halInvalidDeviceType, &invalidDeviceType));
+ EXPECT_EQ(BAD_VALUE,
+ CoreUtils::sinkMetadataFromHalV7(halInvalidDeviceTypeV7,
+ false /*ignoreNonVendorTags*/, &invalidDeviceType));
+ EXPECT_EQ(BAD_VALUE,
+ CoreUtils::sinkMetadataFromHalV7(halInvalidDeviceTypeV7, true /*ignoreNonVendorTags*/,
+ &invalidDeviceType));
+ std::vector<record_track_metadata_v7_t> halInvalidChannelMaskV7 = {
+ {.channel_mask = kInvalidHalChannelMask}};
+ EXPECT_EQ(BAD_VALUE,
+ CoreUtils::sinkMetadataFromHalV7(halInvalidChannelMaskV7,
+ false /*ignoreNonVendorTags*/, &invalidChannelMask));
+ EXPECT_EQ(BAD_VALUE,
+ CoreUtils::sinkMetadataFromHalV7(halInvalidChannelMaskV7,
+ true /*ignoreNonVendorTags*/, &invalidChannelMask));
+ std::vector<record_track_metadata_v7_t> halInvalidTagsV7 = {{.tags = "random string"}};
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sinkMetadataFromHalV7(
+ halInvalidTagsV7, false /*ignoreNonVendorTags*/, &invalidTags));
+ // Non-vendor tags should be filtered out.
+ EXPECT_EQ(NO_ERROR, CoreUtils::sinkMetadataFromHalV7(
+ halInvalidTagsV7, true /*ignoreNonVendorTags*/, &invalidTags));
+}
+
+TEST(CoreUtils, ConvertEmptySinkMetadata) {
+ SinkMetadata emptySinkMetadata;
+ std::vector<record_track_metadata_t> halEmptySinkMetadata;
+ EXPECT_EQ(NO_ERROR, CoreUtils::sinkMetadataToHal(emptySinkMetadata, &halEmptySinkMetadata));
+ EXPECT_TRUE(halEmptySinkMetadata.empty());
+ SinkMetadata emptySinkMetadataBack;
+ EXPECT_EQ(NO_ERROR,
+ CoreUtils::sinkMetadataFromHal(halEmptySinkMetadata, &emptySinkMetadataBack));
+ EXPECT_EQ(emptySinkMetadata, emptySinkMetadataBack);
+ std::vector<record_track_metadata_v7_t> halEmptySinkMetadataV7;
+ EXPECT_EQ(NO_ERROR,
+ CoreUtils::sinkMetadataToHalV7(emptySinkMetadata, false /*ignoreNonVendorTags*/,
+ &halEmptySinkMetadataV7));
+ EXPECT_TRUE(halEmptySinkMetadataV7.empty());
+ SinkMetadata emptySinkMetadataBackFromV7;
+ EXPECT_EQ(NO_ERROR, CoreUtils::sinkMetadataFromHalV7(halEmptySinkMetadataV7,
+ false /*ignoreNonVendorTags*/,
+ &emptySinkMetadataBackFromV7));
+ EXPECT_EQ(emptySinkMetadata, emptySinkMetadataBackFromV7);
+}
+
+class SinkMetadataConvertTest : public ::testing::TestWithParam<SinkTracks> {};
+
+TEST_P(SinkMetadataConvertTest, ToFromHal) {
+ SinkMetadata sinkMetadata;
+ sinkMetadata.tracks = GetParam();
+ std::vector<record_track_metadata_t> halSinkMetadata;
+ EXPECT_EQ(NO_ERROR, CoreUtils::sinkMetadataToHal(sinkMetadata, &halSinkMetadata));
+ EXPECT_EQ(sinkMetadata.tracks.size(), halSinkMetadata.size());
+ SinkMetadata sinkMetadataBackTrimmed;
+ EXPECT_EQ(NO_ERROR, CoreUtils::sinkMetadataFromHal(halSinkMetadata, &sinkMetadataBackTrimmed));
+ // Can't compare 'sinkMetadata' to 'sinkMetadataBackTrimmed'
+ std::vector<record_track_metadata_v7_t> halSinkMetadataV7;
+ EXPECT_EQ(NO_ERROR, CoreUtils::sinkMetadataToHalV7(sinkMetadata, false /*ignoreNonVendorTags*/,
+ &halSinkMetadataV7));
+ EXPECT_EQ(sinkMetadata.tracks.size(), halSinkMetadataV7.size());
+ SinkMetadata sinkMetadataBackFromV7;
+ EXPECT_EQ(NO_ERROR,
+ CoreUtils::sinkMetadataFromHalV7(halSinkMetadataV7, false /*ignoreNonVendorTags*/,
+ &sinkMetadataBackFromV7));
+ EXPECT_EQ(sinkMetadata, sinkMetadataBackFromV7);
+ std::vector<record_track_metadata_v7_t> halSinkMetadataV7FromTrimmed;
+ EXPECT_EQ(NO_ERROR,
+ CoreUtils::sinkMetadataToHalV7(sinkMetadataBackTrimmed, false /*ignoreNonVendorTags*/,
+ &halSinkMetadataV7FromTrimmed));
+ EXPECT_EQ(sinkMetadata.tracks.size(), halSinkMetadataV7FromTrimmed.size());
+ SinkMetadata sinkMetadataBackFromV7Trimmed;
+ EXPECT_EQ(NO_ERROR, CoreUtils::sinkMetadataFromHalV7(halSinkMetadataV7FromTrimmed,
+ false /*ignoreNonVendorTags*/,
+ &sinkMetadataBackFromV7Trimmed));
+ EXPECT_EQ(sinkMetadataBackTrimmed, sinkMetadataBackFromV7Trimmed);
+}
+
+INSTANTIATE_TEST_SUITE_P(ValidRecordTrackMetadatas, SinkMetadataConvertTest,
+ ::testing::Values(SinkTracks{generateMinimalRecordTrackMetadata()},
+ SinkTracks{generateValidRecordTrackMetadata()},
+ SinkTracks{generateValidRecordTrackMetadataWithDevice()},
+ SinkTracks{
+ generateMinimalRecordTrackMetadata(),
+ generateValidRecordTrackMetadata(),
+ generateValidRecordTrackMetadataWithDevice()}));
+
+static PlaybackTrackMetadata generateMinimalPlaybackTrackMetadata() {
+ PlaybackTrackMetadata metadata{};
+ metadata.usage = toString(xsd::AudioUsage::AUDIO_USAGE_UNKNOWN);
+ metadata.contentType = toString(xsd::AudioContentType::AUDIO_CONTENT_TYPE_UNKNOWN);
+ metadata.channelMask = toString(xsd::AudioChannelMask::AUDIO_CHANNEL_NONE);
+ return metadata;
+}
+
+static PlaybackTrackMetadata generateValidPlaybackTrackMetadata() {
+ PlaybackTrackMetadata metadata = generateMinimalPlaybackTrackMetadata();
+ metadata.tags.resize(1);
+ metadata.tags[0] = "VX_GOOGLE_42";
+ metadata.channelMask = toString(xsd::AudioChannelMask::AUDIO_CHANNEL_OUT_MONO);
+ metadata.gain = 1.0;
+ return metadata;
+}
+
+using SourceTracks = hidl_vec<PlaybackTrackMetadata>;
+
+TEST(CoreUtils, ConvertInvalidSourceMetadata) {
+ SourceMetadata invalidUsage;
+ invalidUsage.tracks = SourceTracks{generateMinimalPlaybackTrackMetadata()};
+ invalidUsage.tracks[0].usage = "random string";
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sourceMetadataToHal(invalidUsage, nullptr));
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sourceMetadataToHalV7(invalidUsage,
+ false /*ignoreNonVendorTags*/, nullptr));
+ SourceMetadata invalidContentType;
+ invalidContentType.tracks = SourceTracks{generateMinimalPlaybackTrackMetadata()};
+ invalidContentType.tracks[0].contentType = "random string";
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sourceMetadataToHal(invalidContentType, nullptr));
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sourceMetadataToHalV7(invalidContentType,
+ false /*ignoreNonVendorTags*/, nullptr));
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sourceMetadataToHalV7(invalidContentType,
+ true /*ignoreNonVendorTags*/, nullptr));
+ SourceMetadata invalidChannelMask;
+ invalidChannelMask.tracks = SourceTracks{generateValidPlaybackTrackMetadata()};
+ invalidChannelMask.tracks[0].channelMask = "random string";
+ // Channel mask is sliced away by 'sourceMetadataToHal'
+ EXPECT_EQ(NO_ERROR, CoreUtils::sourceMetadataToHal(invalidChannelMask, nullptr));
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sourceMetadataToHalV7(invalidChannelMask,
+ false /*ignoreNonVendorTags*/, nullptr));
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sourceMetadataToHalV7(invalidChannelMask,
+ true /*ignoreNonVendorTags*/, nullptr));
+ SourceMetadata invalidTags;
+ invalidTags.tracks = SourceTracks{generateValidPlaybackTrackMetadata()};
+ invalidTags.tracks[0].tags[0] = "random string";
+ // Tags are sliced away by 'sourceMetadataToHal'
+ EXPECT_EQ(NO_ERROR, CoreUtils::sourceMetadataToHal(invalidTags, nullptr));
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sourceMetadataToHalV7(invalidTags,
+ false /*ignoreNonVendorTags*/, nullptr));
+ // Non-vendor tags should be filtered out.
+ EXPECT_EQ(NO_ERROR,
+ CoreUtils::sourceMetadataToHalV7(invalidTags, true /*ignoreNonVendorTags*/, nullptr));
+
+ // Verify that a default-initialized metadata is valid.
+ std::vector<playback_track_metadata_t> halValid(1, playback_track_metadata_t{});
+ std::vector<playback_track_metadata_v7_t> halValidV7(1, playback_track_metadata_v7_t{});
+ SourceMetadata valid;
+ EXPECT_EQ(NO_ERROR, CoreUtils::sourceMetadataFromHal(halValid, &valid));
+ EXPECT_EQ(NO_ERROR, CoreUtils::sourceMetadataFromHalV7(halValidV7,
+ false /*ignoreNonVendorTags*/, &valid));
+ EXPECT_EQ(NO_ERROR,
+ CoreUtils::sourceMetadataFromHalV7(halValidV7, true /*ignoreNonVendorTags*/, &valid));
+
+ std::vector<playback_track_metadata_t> halInvalidUsage = {{.usage = kInvalidHalUsage}};
+ std::vector<playback_track_metadata_v7_t> halInvalidUsageV7 = {
+ {.base = {.usage = kInvalidHalUsage}}};
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sourceMetadataFromHal(halInvalidUsage, &invalidUsage));
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sourceMetadataFromHalV7(
+ halInvalidUsageV7, false /*ignoreNonVendorTags*/, &invalidUsage));
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sourceMetadataFromHalV7(
+ halInvalidUsageV7, true /*ignoreNonVendorTags*/, &invalidUsage));
+ std::vector<playback_track_metadata_t> halInvalidContentType = {
+ {.content_type = kInvalidHalContentType}};
+ std::vector<playback_track_metadata_v7_t> halInvalidContentTypeV7 = {
+ {.base = {.content_type = kInvalidHalContentType}}};
+ EXPECT_EQ(BAD_VALUE,
+ CoreUtils::sourceMetadataFromHal(halInvalidContentType, &invalidContentType));
+ EXPECT_EQ(BAD_VALUE,
+ CoreUtils::sourceMetadataFromHalV7(
+ halInvalidContentTypeV7, false /*ignoreNonVendorTags*/, &invalidContentType));
+ EXPECT_EQ(BAD_VALUE,
+ CoreUtils::sourceMetadataFromHalV7(
+ halInvalidContentTypeV7, true /*ignoreNonVendorTags*/, &invalidContentType));
+ std::vector<playback_track_metadata_v7_t> halInvalidChannelMaskV7 = {
+ {.channel_mask = kInvalidHalChannelMask}};
+ EXPECT_EQ(BAD_VALUE,
+ CoreUtils::sourceMetadataFromHalV7(
+ halInvalidChannelMaskV7, false /*ignoreNonVendorTags*/, &invalidChannelMask));
+ EXPECT_EQ(BAD_VALUE,
+ CoreUtils::sourceMetadataFromHalV7(
+ halInvalidChannelMaskV7, true /*ignoreNonVendorTags*/, &invalidChannelMask));
+ std::vector<playback_track_metadata_v7_t> halInvalidTagsV7 = {{.tags = "random string"}};
+ EXPECT_EQ(BAD_VALUE, CoreUtils::sourceMetadataFromHalV7(
+ halInvalidTagsV7, false /*ignoreNonVendorTags*/, &invalidTags));
+ // Non-vendor tags should be filtered out.
+ EXPECT_EQ(NO_ERROR, CoreUtils::sourceMetadataFromHalV7(
+ halInvalidTagsV7, true /*ignoreNonVendorTags*/, &invalidTags));
+}
+
+TEST(CoreUtils, ConvertEmptySourceMetadata) {
+ SourceMetadata emptySourceMetadata;
+ std::vector<playback_track_metadata_t> halEmptySourceMetadata;
+ EXPECT_EQ(NO_ERROR,
+ CoreUtils::sourceMetadataToHal(emptySourceMetadata, &halEmptySourceMetadata));
+ EXPECT_TRUE(halEmptySourceMetadata.empty());
+ SourceMetadata emptySourceMetadataBack;
+ EXPECT_EQ(NO_ERROR,
+ CoreUtils::sourceMetadataFromHal(halEmptySourceMetadata, &emptySourceMetadataBack));
+ EXPECT_EQ(emptySourceMetadata, emptySourceMetadataBack);
+ std::vector<playback_track_metadata_v7_t> halEmptySourceMetadataV7;
+ EXPECT_EQ(NO_ERROR,
+ CoreUtils::sourceMetadataToHalV7(emptySourceMetadata, false /*ignoreNonVendorTags*/,
+ &halEmptySourceMetadataV7));
+ EXPECT_TRUE(halEmptySourceMetadataV7.empty());
+ SourceMetadata emptySourceMetadataBackFromV7;
+ EXPECT_EQ(NO_ERROR, CoreUtils::sourceMetadataFromHalV7(halEmptySourceMetadataV7,
+ false /*ignoreNonVendorTags*/,
+ &emptySourceMetadataBackFromV7));
+ EXPECT_EQ(emptySourceMetadata, emptySourceMetadataBackFromV7);
+}
+
+class SourceMetadataConvertTest : public ::testing::TestWithParam<SourceTracks> {};
+
+TEST_P(SourceMetadataConvertTest, ToFromHal) {
+ SourceMetadata sourceMetadata;
+ sourceMetadata.tracks = GetParam();
+ std::vector<playback_track_metadata_t> halSourceMetadata;
+ EXPECT_EQ(NO_ERROR, CoreUtils::sourceMetadataToHal(sourceMetadata, &halSourceMetadata));
+ EXPECT_EQ(sourceMetadata.tracks.size(), halSourceMetadata.size());
+ SourceMetadata sourceMetadataBackTrimmed;
+ EXPECT_EQ(NO_ERROR,
+ CoreUtils::sourceMetadataFromHal(halSourceMetadata, &sourceMetadataBackTrimmed));
+ // Can't compare 'sourceMetadata' to 'sourceMetadataBackTrimmed'
+ std::vector<playback_track_metadata_v7_t> halSourceMetadataV7;
+ EXPECT_EQ(NO_ERROR,
+ CoreUtils::sourceMetadataToHalV7(sourceMetadata, false /*ignoreNonVendorTags*/,
+ &halSourceMetadataV7));
+ EXPECT_EQ(sourceMetadata.tracks.size(), halSourceMetadataV7.size());
+ SourceMetadata sourceMetadataBackFromV7;
+ EXPECT_EQ(NO_ERROR,
+ CoreUtils::sourceMetadataFromHalV7(halSourceMetadataV7, false /*ignoreNonVendorTags*/,
+ &sourceMetadataBackFromV7));
+ EXPECT_EQ(sourceMetadata, sourceMetadataBackFromV7);
+ std::vector<playback_track_metadata_v7_t> halSourceMetadataV7FromTrimmed;
+ EXPECT_EQ(NO_ERROR, CoreUtils::sourceMetadataToHalV7(sourceMetadataBackTrimmed,
+ false /*ignoreNonVendorTags*/,
+ &halSourceMetadataV7FromTrimmed));
+ EXPECT_EQ(sourceMetadata.tracks.size(), halSourceMetadataV7FromTrimmed.size());
+ SourceMetadata sourceMetadataBackFromV7Trimmed;
+ EXPECT_EQ(NO_ERROR, CoreUtils::sourceMetadataFromHalV7(halSourceMetadataV7FromTrimmed,
+ false /*ignoreNonVendorTags*/,
+ &sourceMetadataBackFromV7Trimmed));
+ EXPECT_EQ(sourceMetadataBackTrimmed, sourceMetadataBackFromV7Trimmed);
+}
+
+INSTANTIATE_TEST_SUITE_P(ValidPlaybackTrackMetadatas, SourceMetadataConvertTest,
+ ::testing::Values(SourceTracks{generateMinimalPlaybackTrackMetadata()},
+ SourceTracks{generateValidPlaybackTrackMetadata()},
+ SourceTracks{generateMinimalPlaybackTrackMetadata(),
+ generateValidPlaybackTrackMetadata()}));
diff --git a/audio/core/all-versions/vts/functional/4.0/AudioPrimaryHidlHalTest.cpp b/audio/core/all-versions/vts/functional/4.0/AudioPrimaryHidlHalTest.cpp
index 5076dcf..f87e5ed 100644
--- a/audio/core/all-versions/vts/functional/4.0/AudioPrimaryHidlHalTest.cpp
+++ b/audio/core/all-versions/vts/functional/4.0/AudioPrimaryHidlHalTest.cpp
@@ -322,9 +322,9 @@
const SourceMetadata metadata = {
{{toString(usage),
toString(content),
- {} /* tags */,
+ volume,
toString(xsd::AudioChannelMask::AUDIO_CHANNEL_OUT_STEREO),
- volume}}};
+ {} /* tags */}}};
ASSERT_RESULT(okOrNotSupported, stream->updateSourceMetadata(metadata))
<< "usage=" << toString(usage) << ", content=" << toString(content)
<< ", volume=" << volume;
@@ -336,24 +336,24 @@
ASSERT_RESULT(okOrNotSupported, stream->updateSourceMetadata(
{{{toString(xsd::AudioUsage::AUDIO_USAGE_MEDIA),
toString(xsd::AudioContentType::AUDIO_CONTENT_TYPE_MUSIC),
- {},
+ 0.1, // gain
toString(xsd::AudioChannelMask::AUDIO_CHANNEL_OUT_STEREO),
- 0.1},
+ {}}, // tags
{toString(xsd::AudioUsage::AUDIO_USAGE_VOICE_COMMUNICATION),
toString(xsd::AudioContentType::AUDIO_CONTENT_TYPE_SPEECH),
- {}, // tags
+ 1.0,
toString(xsd::AudioChannelMask::AUDIO_CHANNEL_OUT_MONO),
- 1.0},
+ {}},
{toString(xsd::AudioUsage::AUDIO_USAGE_ALARM),
toString(xsd::AudioContentType::AUDIO_CONTENT_TYPE_SONIFICATION),
- {}, // tags
+ 0.0,
toString(xsd::AudioChannelMask::AUDIO_CHANNEL_OUT_STEREO),
- 0.0},
+ {}},
{toString(xsd::AudioUsage::AUDIO_USAGE_ASSISTANT),
toString(xsd::AudioContentType::AUDIO_CONTENT_TYPE_UNKNOWN),
- {},
+ 0.3,
toString(xsd::AudioChannelMask::AUDIO_CHANNEL_OUT_MONO),
- 0.3}}}
+ {}}}}
));
// clang-format on
// Set no metadata as if all stream track had stopped
diff --git a/audio/core/all-versions/vts/functional/AudioPrimaryHidlHalTest.h b/audio/core/all-versions/vts/functional/AudioPrimaryHidlHalTest.h
index 2b9e336..61e99e8 100644
--- a/audio/core/all-versions/vts/functional/AudioPrimaryHidlHalTest.h
+++ b/audio/core/all-versions/vts/functional/AudioPrimaryHidlHalTest.h
@@ -928,9 +928,9 @@
const SourceMetadata initMetadata = {
{ { toString(xsd::AudioUsage::AUDIO_USAGE_MEDIA),
toString(xsd::AudioContentType::AUDIO_CONTENT_TYPE_MUSIC),
- {},
+ 1 /* gain */,
toString(xsd::AudioChannelMask::AUDIO_CHANNEL_OUT_STEREO),
- 1 /* gain */ } }};
+ {} } }};
#endif
};
TEST_P(OutputStreamTest, OpenOutputStreamTest) {
diff --git a/audio/effect/all-versions/default/Android.bp b/audio/effect/all-versions/default/Android.bp
index a0cd612..99b1120 100644
--- a/audio/effect/all-versions/default/Android.bp
+++ b/audio/effect/all-versions/default/Android.bp
@@ -8,7 +8,6 @@
"AudioBufferManager.cpp",
"AutomaticGainControlEffect.cpp",
"BassBoostEffect.cpp",
- "Conversions.cpp",
"DownmixEffect.cpp",
"Effect.cpp",
"EffectsFactory.cpp",
@@ -51,6 +50,7 @@
"android.hardware.audio.common@2.0",
"android.hardware.audio.common@2.0-util",
"android.hardware.audio.effect@2.0",
+ "android.hardware.audio.effect@2.0-util",
],
cflags: [
"-DMAJOR_VERSION=2",
@@ -66,6 +66,7 @@
"android.hardware.audio.common@4.0",
"android.hardware.audio.common@4.0-util",
"android.hardware.audio.effect@4.0",
+ "android.hardware.audio.effect@4.0-util",
],
cflags: [
"-DMAJOR_VERSION=4",
@@ -81,6 +82,7 @@
"android.hardware.audio.common@5.0",
"android.hardware.audio.common@5.0-util",
"android.hardware.audio.effect@5.0",
+ "android.hardware.audio.effect@5.0-util",
],
cflags: [
"-DMAJOR_VERSION=5",
@@ -96,6 +98,7 @@
"android.hardware.audio.common@6.0",
"android.hardware.audio.common@6.0-util",
"android.hardware.audio.effect@6.0",
+ "android.hardware.audio.effect@6.0-util",
],
cflags: [
"-DMAJOR_VERSION=6",
@@ -111,6 +114,7 @@
"android.hardware.audio.common@7.0",
"android.hardware.audio.common@7.0-util",
"android.hardware.audio.effect@7.0",
+ "android.hardware.audio.effect@7.0-util",
],
cflags: [
"-DMAJOR_VERSION=7",
diff --git a/audio/effect/all-versions/default/Conversions.cpp b/audio/effect/all-versions/default/Conversions.cpp
deleted file mode 100644
index 0cc8767..0000000
--- a/audio/effect/all-versions/default/Conversions.cpp
+++ /dev/null
@@ -1,61 +0,0 @@
-/*
- * Copyright (C) 2018 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "Conversions.h"
-#include "UuidUtils.h"
-
-#include <memory.h>
-#include <stdio.h>
-
-#include <common/all-versions/VersionUtils.h>
-
-using ::android::hardware::audio::common::utils::EnumBitfield;
-
-namespace android {
-namespace hardware {
-namespace audio {
-namespace effect {
-namespace CPP_VERSION {
-namespace implementation {
-
-using ::android::hardware::audio::common::CPP_VERSION::implementation::UuidUtils;
-
-void effectDescriptorFromHal(const effect_descriptor_t& halDescriptor,
- EffectDescriptor* descriptor) {
- UuidUtils::uuidFromHal(halDescriptor.type, &descriptor->type);
- UuidUtils::uuidFromHal(halDescriptor.uuid, &descriptor->uuid);
- descriptor->flags = EnumBitfield<EffectFlags>(halDescriptor.flags);
- descriptor->cpuLoad = halDescriptor.cpuLoad;
- descriptor->memoryUsage = halDescriptor.memoryUsage;
- memcpy(descriptor->name.data(), halDescriptor.name, descriptor->name.size());
- memcpy(descriptor->implementor.data(), halDescriptor.implementor,
- descriptor->implementor.size());
-}
-
-std::string uuidToString(const effect_uuid_t& halUuid) {
- char str[64];
- snprintf(str, sizeof(str), "%08x-%04x-%04x-%04x-%02x%02x%02x%02x%02x%02x", halUuid.timeLow,
- halUuid.timeMid, halUuid.timeHiAndVersion, halUuid.clockSeq, halUuid.node[0],
- halUuid.node[1], halUuid.node[2], halUuid.node[3], halUuid.node[4], halUuid.node[5]);
- return str;
-}
-
-} // namespace implementation
-} // namespace CPP_VERSION
-} // namespace effect
-} // namespace audio
-} // namespace hardware
-} // namespace android
diff --git a/audio/effect/all-versions/default/Conversions.h b/audio/effect/all-versions/default/Conversions.h
deleted file mode 100644
index 75aab24..0000000
--- a/audio/effect/all-versions/default/Conversions.h
+++ /dev/null
@@ -1,46 +0,0 @@
-/*
- * Copyright (C) 2018 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef ANDROID_HARDWARE_AUDIO_EFFECT_CONVERSIONS_H_
-#define ANDROID_HARDWARE_AUDIO_EFFECT_CONVERSIONS_H_
-
-#include PATH(android/hardware/audio/effect/FILE_VERSION/types.h)
-
-#include <string>
-
-#include <system/audio_effect.h>
-
-namespace android {
-namespace hardware {
-namespace audio {
-namespace effect {
-namespace CPP_VERSION {
-namespace implementation {
-
-using ::android::hardware::audio::effect::CPP_VERSION::EffectDescriptor;
-
-void effectDescriptorFromHal(const effect_descriptor_t& halDescriptor,
- EffectDescriptor* descriptor);
-std::string uuidToString(const effect_uuid_t& halUuid);
-
-} // namespace implementation
-} // namespace CPP_VERSION
-} // namespace effect
-} // namespace audio
-} // namespace hardware
-} // namespace android
-
-#endif // ANDROID_HARDWARE_AUDIO_EFFECT_CONVERSIONS_H_
diff --git a/audio/effect/all-versions/default/Effect.cpp b/audio/effect/all-versions/default/Effect.cpp
index 58f1779..ccfc6b2 100644
--- a/audio/effect/all-versions/default/Effect.cpp
+++ b/audio/effect/all-versions/default/Effect.cpp
@@ -19,7 +19,6 @@
#define LOG_TAG "EffectHAL"
#define ATRACE_TAG ATRACE_TAG_AUDIO
-#include "Conversions.h"
#include "Effect.h"
#include "common/all-versions/default/EffectMap.h"
@@ -30,6 +29,7 @@
#include <HidlUtils.h>
#include <android/log.h>
#include <media/EffectsFactoryApi.h>
+#include <util/EffectUtils.h>
#include <utils/Trace.h>
#include "VersionUtils.h"
@@ -202,34 +202,6 @@
halConfig->aux_channels = static_cast<audio_channel_mask_t>(config.auxChannels);
}
-void Effect::effectBufferConfigFromHal(const buffer_config_t& halConfig,
- EffectBufferConfig* config) {
- config->buffer.id = 0;
- config->buffer.frameCount = 0;
- config->samplingRateHz = halConfig.samplingRate;
- config->channels = AudioChannelBitfield(halConfig.channels);
- config->format = AudioFormat(halConfig.format);
- config->accessMode = EffectBufferAccess(halConfig.accessMode);
- config->mask = static_cast<decltype(config->mask)>(halConfig.mask);
-}
-
-// static
-void Effect::effectBufferConfigToHal(const EffectBufferConfig& config, buffer_config_t* halConfig) {
- // Note: setting the buffers directly is considered obsolete. They need to be set
- // using 'setProcessBuffers'.
- halConfig->buffer.frameCount = 0;
- halConfig->buffer.raw = NULL;
- halConfig->samplingRate = config.samplingRateHz;
- halConfig->channels = static_cast<uint32_t>(config.channels);
- // Note: The framework code does not use BP.
- halConfig->bufferProvider.cookie = NULL;
- halConfig->bufferProvider.getBuffer = NULL;
- halConfig->bufferProvider.releaseBuffer = NULL;
- halConfig->format = static_cast<uint8_t>(config.format);
- halConfig->accessMode = static_cast<uint8_t>(config.accessMode);
- halConfig->mask = static_cast<uint8_t>(config.mask);
-}
-
#else // MAJOR_VERSION <= 6
void Effect::effectAuxChannelsConfigFromHal(const channel_config_t& halConfig,
@@ -247,68 +219,8 @@
(void)HidlUtils::audioChannelMaskToHal(config.auxChannels, &halConfig->aux_channels);
}
-void Effect::effectBufferConfigFromHal(const buffer_config_t& halConfig,
- EffectBufferConfig* config) {
- config->buffer.unspecified();
- audio_config_base_t halConfigBase = {halConfig.samplingRate,
- static_cast<audio_channel_mask_t>(halConfig.channels),
- static_cast<audio_format_t>(halConfig.format)};
- (void)HidlUtils::audioConfigBaseOptionalFromHal(
- halConfigBase, mIsInput, halConfig.mask & EFFECT_CONFIG_FORMAT,
- halConfig.mask & EFFECT_CONFIG_SMP_RATE, halConfig.mask & EFFECT_CONFIG_CHANNELS,
- &config->base);
- if (halConfig.mask & EFFECT_CONFIG_ACC_MODE) {
- config->accessMode.value(EffectBufferAccess(halConfig.accessMode));
- }
-}
-
-// static
-void Effect::effectBufferConfigToHal(const EffectBufferConfig& config, buffer_config_t* halConfig) {
- // Note: setting the buffers directly is considered obsolete. They need to be set
- // using 'setProcessBuffers'.
- halConfig->buffer.frameCount = 0;
- halConfig->buffer.raw = nullptr;
- audio_config_base_t halConfigBase = AUDIO_CONFIG_BASE_INITIALIZER;
- bool formatSpecified = false, sRateSpecified = false, channelMaskSpecified = false;
- (void)HidlUtils::audioConfigBaseOptionalToHal(config.base, &halConfigBase, &formatSpecified,
- &sRateSpecified, &channelMaskSpecified);
- halConfig->mask = 0;
- if (sRateSpecified) {
- halConfig->mask |= EFFECT_CONFIG_SMP_RATE;
- halConfig->samplingRate = halConfigBase.sample_rate;
- }
- if (channelMaskSpecified) {
- halConfig->mask |= EFFECT_CONFIG_CHANNELS;
- halConfig->channels = halConfigBase.channel_mask;
- }
- if (formatSpecified) {
- halConfig->mask |= EFFECT_CONFIG_FORMAT;
- halConfig->format = halConfigBase.format;
- }
- // Note: The framework code does not use BP.
- halConfig->bufferProvider.cookie = nullptr;
- halConfig->bufferProvider.getBuffer = nullptr;
- halConfig->bufferProvider.releaseBuffer = nullptr;
- if (config.accessMode.getDiscriminator() ==
- EffectBufferConfig::OptionalAccessMode::hidl_discriminator::value) {
- halConfig->mask |= EFFECT_CONFIG_ACC_MODE;
- halConfig->accessMode = static_cast<uint8_t>(config.accessMode.value());
- }
-}
-
#endif // MAJOR_VERSION <= 6
-void Effect::effectConfigFromHal(const effect_config_t& halConfig, EffectConfig* config) {
- effectBufferConfigFromHal(halConfig.inputCfg, &config->inputCfg);
- effectBufferConfigFromHal(halConfig.outputCfg, &config->outputCfg);
-}
-
-// static
-void Effect::effectConfigToHal(const EffectConfig& config, effect_config_t* halConfig) {
- effectBufferConfigToHal(config.inputCfg, &halConfig->inputCfg);
- effectBufferConfigToHal(config.outputCfg, &halConfig->outputCfg);
-}
-
// static
void Effect::effectOffloadParamToHal(const EffectOffloadParameter& offload,
effect_offload_param_t* halOffload) {
@@ -373,7 +285,7 @@
(*mHandle)->command(mHandle, commandCode, 0, NULL, &halResultSize, &halConfig);
EffectConfig config;
if (status == OK) {
- effectConfigFromHal(halConfig, &config);
+ status = EffectUtils::effectConfigFromHal(halConfig, mIsInput, &config);
}
cb(analyzeCommandStatus(commandName, sContextCallToCommand, status), config);
}
@@ -538,7 +450,7 @@
const sp<IEffectBufferProviderCallback>& inputBufferProvider,
const sp<IEffectBufferProviderCallback>& outputBufferProvider) {
effect_config_t halConfig;
- effectConfigToHal(config, &halConfig);
+ EffectUtils::effectConfigToHal(config, &halConfig);
if (inputBufferProvider != 0) {
LOG_FATAL("Using input buffer provider is not supported");
}
@@ -733,7 +645,7 @@
status_t status = (*mHandle)->get_descriptor(mHandle, &halDescriptor);
EffectDescriptor descriptor;
if (status == OK) {
- effectDescriptorFromHal(halDescriptor, &descriptor);
+ status = EffectUtils::effectDescriptorFromHal(halDescriptor, &descriptor);
}
_hidl_cb(analyzeStatus("get_descriptor", "", sContextCallFunction, status), descriptor);
return Void();
diff --git a/audio/effect/all-versions/default/Effect.h b/audio/effect/all-versions/default/Effect.h
index 9aa47ea..d5218f7 100644
--- a/audio/effect/all-versions/default/Effect.h
+++ b/audio/effect/all-versions/default/Effect.h
@@ -203,11 +203,6 @@
EffectAuxChannelsConfig* config);
static void effectAuxChannelsConfigToHal(const EffectAuxChannelsConfig& config,
channel_config_t* halConfig);
- void effectBufferConfigFromHal(const buffer_config_t& halConfig, EffectBufferConfig* config);
- static void effectBufferConfigToHal(const EffectBufferConfig& config,
- buffer_config_t* halConfig);
- void effectConfigFromHal(const effect_config_t& halConfig, EffectConfig* config);
- static void effectConfigToHal(const EffectConfig& config, effect_config_t* halConfig);
static void effectOffloadParamToHal(const EffectOffloadParameter& offload,
effect_offload_param_t* halOffload);
static std::vector<uint8_t> parameterToHal(uint32_t paramSize, const void* paramData,
diff --git a/audio/effect/all-versions/default/EffectsFactory.cpp b/audio/effect/all-versions/default/EffectsFactory.cpp
index 1ea990b..eb1cb49 100644
--- a/audio/effect/all-versions/default/EffectsFactory.cpp
+++ b/audio/effect/all-versions/default/EffectsFactory.cpp
@@ -19,7 +19,6 @@
#include "AcousticEchoCancelerEffect.h"
#include "AutomaticGainControlEffect.h"
#include "BassBoostEffect.h"
-#include "Conversions.h"
#include "DownmixEffect.h"
#include "Effect.h"
#include "EnvironmentalReverbEffect.h"
@@ -27,11 +26,11 @@
#include "LoudnessEnhancerEffect.h"
#include "NoiseSuppressionEffect.h"
#include "PresetReverbEffect.h"
-#include "UuidUtils.h"
#include "VirtualizerEffect.h"
#include "VisualizerEffect.h"
#include "common/all-versions/default/EffectMap.h"
+#include <UuidUtils.h>
#include <android/log.h>
#include <media/EffectsFactoryApi.h>
#include <system/audio_effects/effect_aec.h>
@@ -45,6 +44,7 @@
#include <system/audio_effects/effect_presetreverb.h>
#include <system/audio_effects/effect_virtualizer.h>
#include <system/audio_effects/effect_visualizer.h>
+#include <util/EffectUtils.h>
namespace android {
namespace hardware {
@@ -107,7 +107,7 @@
effect_descriptor_t halDescriptor;
status = EffectQueryEffect(i, &halDescriptor);
if (status == OK) {
- effectDescriptorFromHal(halDescriptor, &result[i]);
+ EffectUtils::effectDescriptorFromHal(halDescriptor, &result[i]);
} else {
ALOGE("Error querying effect at position %d / %d: %s", i, numEffects,
strerror(-status));
@@ -141,11 +141,11 @@
effect_descriptor_t halDescriptor;
status_t status = EffectGetDescriptor(&halUuid, &halDescriptor);
EffectDescriptor descriptor;
- effectDescriptorFromHal(halDescriptor, &descriptor);
+ EffectUtils::effectDescriptorFromHal(halDescriptor, &descriptor);
Result retval(Result::OK);
if (status != OK) {
- ALOGE("Error querying effect descriptor for %s: %s", uuidToString(halUuid).c_str(),
- strerror(-status));
+ ALOGE("Error querying effect descriptor for %s: %s",
+ UuidUtils::uuidToString(halUuid).c_str(), strerror(-status));
if (status == -ENOENT) {
retval = Result::INVALID_ARGUMENTS;
} else {
@@ -191,13 +191,14 @@
effect = dispatchEffectInstanceCreation(halDescriptor, handle);
effectId = EffectMap::getInstance().add(handle);
} else {
- ALOGE("Error querying effect descriptor for %s: %s", uuidToString(halUuid).c_str(),
- strerror(-status));
+ ALOGE("Error querying effect descriptor for %s: %s",
+ UuidUtils::uuidToString(halUuid).c_str(), strerror(-status));
EffectRelease(handle);
}
}
if (status != OK) {
- ALOGE("Error creating effect %s: %s", uuidToString(halUuid).c_str(), strerror(-status));
+ ALOGE("Error creating effect %s: %s", UuidUtils::uuidToString(halUuid).c_str(),
+ strerror(-status));
if (status == -ENOENT) {
retval = Result::INVALID_ARGUMENTS;
} else {
diff --git a/audio/effect/all-versions/default/TEST_MAPPING b/audio/effect/all-versions/default/TEST_MAPPING
new file mode 100644
index 0000000..b66e4e4
--- /dev/null
+++ b/audio/effect/all-versions/default/TEST_MAPPING
@@ -0,0 +1,7 @@
+{
+ "presubmit": [
+ {
+ "name": "android.hardware.audio.effect@7.0-util_tests"
+ }
+ ]
+}
diff --git a/audio/effect/all-versions/default/util/Android.bp b/audio/effect/all-versions/default/util/Android.bp
new file mode 100644
index 0000000..5ba19d3
--- /dev/null
+++ b/audio/effect/all-versions/default/util/Android.bp
@@ -0,0 +1,136 @@
+cc_defaults {
+ name: "android.hardware.audio.effect-util_default",
+ defaults: ["hidl_defaults"],
+
+ vendor_available: true,
+
+ export_include_dirs: ["include"],
+
+ srcs: [
+ "EffectUtils.cpp",
+ ],
+
+ shared_libs: [
+ "liblog",
+ "libutils",
+ "libhidlbase",
+ "android.hardware.audio.common-util",
+ ],
+ export_shared_lib_headers: [
+ "android.hardware.audio.common-util",
+ ],
+
+ header_libs: [
+ "libaudio_system_headers",
+ "libhardware_headers",
+ ],
+}
+
+cc_library_shared {
+ name: "android.hardware.audio.effect@2.0-util",
+ defaults: ["android.hardware.audio.effect-util_default"],
+ shared_libs: [
+ "android.hardware.audio.common@2.0",
+ "android.hardware.audio.common@2.0-util",
+ "android.hardware.audio.effect@2.0",
+ ],
+ cflags: [
+ "-DMAJOR_VERSION=2",
+ "-DMINOR_VERSION=0",
+ "-include common/all-versions/VersionMacro.h",
+ ],
+}
+
+cc_library_shared {
+ name: "android.hardware.audio.effect@4.0-util",
+ defaults: ["android.hardware.audio.effect-util_default"],
+ shared_libs: [
+ "android.hardware.audio.common@4.0",
+ "android.hardware.audio.common@4.0-util",
+ "android.hardware.audio.effect@4.0",
+ ],
+ cflags: [
+ "-DMAJOR_VERSION=4",
+ "-DMINOR_VERSION=0",
+ "-include common/all-versions/VersionMacro.h",
+ ],
+}
+
+cc_library_shared {
+ name: "android.hardware.audio.effect@5.0-util",
+ defaults: ["android.hardware.audio.effect-util_default"],
+ shared_libs: [
+ "android.hardware.audio.common@5.0",
+ "android.hardware.audio.common@5.0-util",
+ "android.hardware.audio.effect@5.0",
+ ],
+ cflags: [
+ "-DMAJOR_VERSION=5",
+ "-DMINOR_VERSION=0",
+ "-include common/all-versions/VersionMacro.h",
+ ],
+}
+
+cc_library_shared {
+ name: "android.hardware.audio.effect@6.0-util",
+ defaults: ["android.hardware.audio.effect-util_default"],
+ shared_libs: [
+ "android.hardware.audio.common@6.0",
+ "android.hardware.audio.common@6.0-util",
+ "android.hardware.audio.effect@6.0",
+ ],
+ cflags: [
+ "-DMAJOR_VERSION=6",
+ "-DMINOR_VERSION=0",
+ "-include common/all-versions/VersionMacro.h",
+ ],
+}
+
+cc_library {
+ name: "android.hardware.audio.effect@7.0-util",
+ defaults: ["android.hardware.audio.effect-util_default"],
+ shared_libs: [
+ "android.hardware.audio.common@7.0",
+ "android.hardware.audio.common@7.0-util",
+ "android.hardware.audio.effect@7.0",
+ ],
+ cflags: [
+ "-DMAJOR_VERSION=7",
+ "-DMINOR_VERSION=0",
+ "-include common/all-versions/VersionMacro.h",
+ ],
+}
+
+// Note: this isn't a VTS test, but rather a unit test
+// to verify correctness of conversion utilities.
+cc_test {
+ name: "android.hardware.audio.effect@7.0-util_tests",
+ defaults: ["android.hardware.audio.effect-util_default"],
+
+ srcs: ["tests/effectutils_tests.cpp"],
+
+ // Use static linking to allow running in presubmit on
+ // targets that don't have HAL V7.
+ static_libs: [
+ "android.hardware.audio.common@7.0",
+ "android.hardware.audio.common@7.0-enums",
+ "android.hardware.audio.common@7.0-util",
+ "android.hardware.audio.effect@7.0",
+ "android.hardware.audio.effect@7.0-util",
+ ],
+
+ shared_libs: [
+ "libbase",
+ "libxml2",
+ ],
+
+ cflags: [
+ "-Werror",
+ "-Wall",
+ "-DMAJOR_VERSION=7",
+ "-DMINOR_VERSION=0",
+ "-include common/all-versions/VersionMacro.h",
+ ],
+
+ test_suites: ["device-tests"],
+}
diff --git a/audio/effect/all-versions/default/util/EffectUtils.cpp b/audio/effect/all-versions/default/util/EffectUtils.cpp
new file mode 100644
index 0000000..1c0419a
--- /dev/null
+++ b/audio/effect/all-versions/default/util/EffectUtils.cpp
@@ -0,0 +1,183 @@
+/*
+ * Copyright (C) 2021 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 <memory.h>
+
+#include <HidlUtils.h>
+#include <UuidUtils.h>
+#include <common/all-versions/VersionUtils.h>
+
+#include "util/EffectUtils.h"
+
+using ::android::hardware::audio::common::CPP_VERSION::implementation::HidlUtils;
+using ::android::hardware::audio::common::CPP_VERSION::implementation::UuidUtils;
+using ::android::hardware::audio::common::utils::EnumBitfield;
+
+namespace android {
+namespace hardware {
+namespace audio {
+namespace effect {
+namespace CPP_VERSION {
+namespace implementation {
+
+using namespace ::android::hardware::audio::common::CPP_VERSION;
+
+#define CONVERT_CHECKED(expr, result) \
+ if (status_t status = (expr); status != NO_ERROR) { \
+ result = status; \
+ }
+
+#if MAJOR_VERSION <= 6
+
+status_t EffectUtils::effectBufferConfigFromHal(const buffer_config_t& halConfig, bool /*isInput*/,
+ EffectBufferConfig* config) {
+ config->buffer.id = 0;
+ config->buffer.frameCount = 0;
+ config->samplingRateHz = halConfig.samplingRate;
+ config->channels = EnumBitfield<AudioChannelMask>(halConfig.channels);
+ config->format = AudioFormat(halConfig.format);
+ config->accessMode = EffectBufferAccess(halConfig.accessMode);
+ config->mask = EnumBitfield<EffectConfigParameters>(halConfig.mask);
+ return NO_ERROR;
+}
+
+status_t EffectUtils::effectBufferConfigToHal(const EffectBufferConfig& config,
+ buffer_config_t* halConfig) {
+ // Note: setting the buffers directly is considered obsolete. They need to be set
+ // using 'setProcessBuffers'.
+ halConfig->buffer.frameCount = 0;
+ halConfig->buffer.raw = nullptr;
+ halConfig->samplingRate = config.samplingRateHz;
+ halConfig->channels = static_cast<uint32_t>(config.channels);
+ // Note: The framework code does not use BP.
+ halConfig->bufferProvider.cookie = nullptr;
+ halConfig->bufferProvider.getBuffer = nullptr;
+ halConfig->bufferProvider.releaseBuffer = nullptr;
+ halConfig->format = static_cast<uint8_t>(config.format);
+ halConfig->accessMode = static_cast<uint8_t>(config.accessMode);
+ halConfig->mask = static_cast<uint8_t>(config.mask);
+ return NO_ERROR;
+}
+
+#else
+
+status_t EffectUtils::effectBufferConfigFromHal(const buffer_config_t& halConfig, bool isInput,
+ EffectBufferConfig* config) {
+ status_t result = NO_ERROR;
+ config->buffer.unspecified();
+ audio_config_base_t halConfigBase = {halConfig.samplingRate,
+ static_cast<audio_channel_mask_t>(halConfig.channels),
+ static_cast<audio_format_t>(halConfig.format)};
+ CONVERT_CHECKED(HidlUtils::audioConfigBaseOptionalFromHal(
+ halConfigBase, isInput, halConfig.mask & EFFECT_CONFIG_FORMAT,
+ halConfig.mask & EFFECT_CONFIG_SMP_RATE,
+ halConfig.mask & EFFECT_CONFIG_CHANNELS, &config->base),
+ result);
+ if (halConfig.mask & EFFECT_CONFIG_ACC_MODE) {
+ config->accessMode.value(EffectBufferAccess(halConfig.accessMode));
+ }
+ return result;
+}
+
+status_t EffectUtils::effectBufferConfigToHal(const EffectBufferConfig& config,
+ buffer_config_t* halConfig) {
+ status_t result = NO_ERROR;
+ // Note: setting the buffers directly is considered obsolete. They need to be set
+ // using 'setProcessBuffers'.
+ halConfig->buffer.frameCount = 0;
+ halConfig->buffer.raw = nullptr;
+ audio_config_base_t halConfigBase = AUDIO_CONFIG_BASE_INITIALIZER;
+ bool formatSpecified = false, sRateSpecified = false, channelMaskSpecified = false;
+ CONVERT_CHECKED(
+ HidlUtils::audioConfigBaseOptionalToHal(config.base, &halConfigBase, &formatSpecified,
+ &sRateSpecified, &channelMaskSpecified),
+ result);
+ halConfig->mask = 0;
+ if (sRateSpecified) {
+ halConfig->mask |= EFFECT_CONFIG_SMP_RATE;
+ halConfig->samplingRate = halConfigBase.sample_rate;
+ }
+ if (channelMaskSpecified) {
+ halConfig->mask |= EFFECT_CONFIG_CHANNELS;
+ halConfig->channels = halConfigBase.channel_mask;
+ }
+ if (formatSpecified) {
+ halConfig->mask |= EFFECT_CONFIG_FORMAT;
+ halConfig->format = halConfigBase.format;
+ }
+ // Note: The framework code does not use BP.
+ halConfig->bufferProvider.cookie = nullptr;
+ halConfig->bufferProvider.getBuffer = nullptr;
+ halConfig->bufferProvider.releaseBuffer = nullptr;
+ if (config.accessMode.getDiscriminator() ==
+ EffectBufferConfig::OptionalAccessMode::hidl_discriminator::value) {
+ halConfig->mask |= EFFECT_CONFIG_ACC_MODE;
+ halConfig->accessMode = static_cast<uint8_t>(config.accessMode.value());
+ }
+ return result;
+}
+
+#endif // MAJOR_VERSION >= 6
+
+status_t EffectUtils::effectConfigFromHal(const effect_config_t& halConfig, bool isInput,
+ EffectConfig* config) {
+ status_t result = NO_ERROR;
+ CONVERT_CHECKED(effectBufferConfigFromHal(halConfig.inputCfg, isInput, &config->inputCfg),
+ result);
+ CONVERT_CHECKED(effectBufferConfigFromHal(halConfig.outputCfg, isInput, &config->outputCfg),
+ result);
+ return result;
+}
+
+status_t EffectUtils::effectConfigToHal(const EffectConfig& config, effect_config_t* halConfig) {
+ status_t result = NO_ERROR;
+ CONVERT_CHECKED(effectBufferConfigToHal(config.inputCfg, &halConfig->inputCfg), result);
+ CONVERT_CHECKED(effectBufferConfigToHal(config.outputCfg, &halConfig->outputCfg), result);
+ return result;
+}
+
+status_t EffectUtils::effectDescriptorFromHal(const effect_descriptor_t& halDescriptor,
+ EffectDescriptor* descriptor) {
+ UuidUtils::uuidFromHal(halDescriptor.type, &descriptor->type);
+ UuidUtils::uuidFromHal(halDescriptor.uuid, &descriptor->uuid);
+ descriptor->flags = EnumBitfield<EffectFlags>(halDescriptor.flags);
+ descriptor->cpuLoad = halDescriptor.cpuLoad;
+ descriptor->memoryUsage = halDescriptor.memoryUsage;
+ memcpy(descriptor->name.data(), halDescriptor.name, descriptor->name.size());
+ memcpy(descriptor->implementor.data(), halDescriptor.implementor,
+ descriptor->implementor.size());
+ return NO_ERROR;
+}
+
+status_t EffectUtils::effectDescriptorToHal(const EffectDescriptor& descriptor,
+ effect_descriptor_t* halDescriptor) {
+ UuidUtils::uuidToHal(descriptor.type, &halDescriptor->type);
+ UuidUtils::uuidToHal(descriptor.uuid, &halDescriptor->uuid);
+ halDescriptor->flags = static_cast<uint32_t>(descriptor.flags);
+ halDescriptor->cpuLoad = descriptor.cpuLoad;
+ halDescriptor->memoryUsage = descriptor.memoryUsage;
+ memcpy(halDescriptor->name, descriptor.name.data(), descriptor.name.size());
+ memcpy(halDescriptor->implementor, descriptor.implementor.data(),
+ descriptor.implementor.size());
+ return NO_ERROR;
+}
+
+} // namespace implementation
+} // namespace CPP_VERSION
+} // namespace effect
+} // namespace audio
+} // namespace hardware
+} // namespace android
diff --git a/audio/effect/all-versions/default/util/include/util/EffectUtils.h b/audio/effect/all-versions/default/util/include/util/EffectUtils.h
new file mode 100644
index 0000000..23963b4
--- /dev/null
+++ b/audio/effect/all-versions/default/util/include/util/EffectUtils.h
@@ -0,0 +1,53 @@
+/*
+ * Copyright (C) 2021 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.
+ */
+
+#pragma once
+
+// clang-format off
+#include PATH(android/hardware/audio/effect/FILE_VERSION/types.h)
+// clang-format on
+
+#include <system/audio_effect.h>
+
+namespace android {
+namespace hardware {
+namespace audio {
+namespace effect {
+namespace CPP_VERSION {
+namespace implementation {
+
+using namespace ::android::hardware::audio::effect::CPP_VERSION;
+
+struct EffectUtils {
+ static status_t effectBufferConfigFromHal(const buffer_config_t& halConfig, bool isInput,
+ EffectBufferConfig* config);
+ static status_t effectBufferConfigToHal(const EffectBufferConfig& config,
+ buffer_config_t* halConfig);
+ static status_t effectConfigFromHal(const effect_config_t& halConfig, bool isInput,
+ EffectConfig* config);
+ static status_t effectConfigToHal(const EffectConfig& config, effect_config_t* halConfig);
+ static status_t effectDescriptorFromHal(const effect_descriptor_t& halDescriptor,
+ EffectDescriptor* descriptor);
+ static status_t effectDescriptorToHal(const EffectDescriptor& descriptor,
+ effect_descriptor_t* halDescriptor);
+};
+
+} // namespace implementation
+} // namespace CPP_VERSION
+} // namespace effect
+} // namespace audio
+} // namespace hardware
+} // namespace android
diff --git a/audio/effect/all-versions/default/util/tests/effectutils_tests.cpp b/audio/effect/all-versions/default/util/tests/effectutils_tests.cpp
new file mode 100644
index 0000000..7eb8cd2
--- /dev/null
+++ b/audio/effect/all-versions/default/util/tests/effectutils_tests.cpp
@@ -0,0 +1,144 @@
+/*
+ * Copyright (C) 2021 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 <string>
+
+#include <gtest/gtest.h>
+
+#define LOG_TAG "EffectUtils_Test"
+#include <log/log.h>
+
+#include <android_audio_policy_configuration_V7_0-enums.h>
+#include <system/audio_effect.h>
+#include <util/EffectUtils.h>
+#include <xsdc/XsdcSupport.h>
+
+using namespace android;
+using namespace ::android::hardware::audio::common::CPP_VERSION;
+using namespace ::android::hardware::audio::effect::CPP_VERSION;
+using ::android::hardware::audio::effect::CPP_VERSION::implementation::EffectUtils;
+namespace xsd {
+using namespace ::android::audio::policy::configuration::V7_0;
+}
+
+static constexpr audio_channel_mask_t kInvalidHalChannelMask = AUDIO_CHANNEL_INVALID;
+static constexpr audio_format_t kInvalidHalFormat = AUDIO_FORMAT_INVALID;
+
+// Not generated automatically because AudioBuffer contains
+// instances of hidl_memory which can't be compared properly
+// in general case due to presence of handles.
+//
+// However, in this particular case, handles must not present
+// thus comparison is possible.
+//
+// operator== must be defined in the same namespace as the structures.
+namespace android {
+namespace hardware {
+namespace audio {
+namespace effect {
+namespace CPP_VERSION {
+inline bool operator==(const AudioBuffer& lhs, const AudioBuffer& rhs) {
+ return lhs.id == rhs.id && lhs.frameCount == rhs.frameCount && lhs.data.handle() == nullptr &&
+ rhs.data.handle() == nullptr;
+}
+
+inline bool operator==(const EffectBufferConfig& lhs, const EffectBufferConfig& rhs) {
+ return lhs.buffer.getDiscriminator() == rhs.buffer.getDiscriminator() &&
+ (lhs.buffer.getDiscriminator() ==
+ EffectBufferConfig::OptionalBuffer::hidl_discriminator::unspecified ||
+ lhs.buffer.buf() == rhs.buffer.buf()) &&
+ lhs.base == rhs.base && lhs.accessMode == rhs.accessMode;
+}
+
+inline bool operator==(const EffectConfig& lhs, const EffectConfig& rhs) {
+ return lhs.inputCfg == rhs.inputCfg && lhs.outputCfg == rhs.outputCfg;
+}
+} // namespace CPP_VERSION
+} // namespace effect
+} // namespace audio
+} // namespace hardware
+} // namespace android
+
+TEST(EffectUtils, ConvertInvalidBufferConfig) {
+ buffer_config_t halInvalid;
+ EffectBufferConfig invalidChannelMask;
+ invalidChannelMask.base.channelMask.value("random string");
+ EXPECT_EQ(BAD_VALUE, EffectUtils::effectBufferConfigToHal(invalidChannelMask, &halInvalid));
+ EffectBufferConfig invalidFormat;
+ invalidFormat.base.format.value("random string");
+ EXPECT_EQ(BAD_VALUE, EffectUtils::effectBufferConfigToHal(invalidFormat, &halInvalid));
+
+ buffer_config_t halInvalidChannelMask;
+ EffectBufferConfig invalid;
+ halInvalidChannelMask.channels = kInvalidHalChannelMask;
+ halInvalidChannelMask.mask = EFFECT_CONFIG_CHANNELS;
+ EXPECT_EQ(BAD_VALUE, EffectUtils::effectBufferConfigFromHal(halInvalidChannelMask,
+ false /*isInput*/, &invalid));
+ EXPECT_EQ(BAD_VALUE, EffectUtils::effectBufferConfigFromHal(halInvalidChannelMask,
+ true /*isInput*/, &invalid));
+ buffer_config_t halInvalidFormat;
+ halInvalidFormat.format = (uint8_t)kInvalidHalFormat;
+ halInvalidFormat.mask = EFFECT_CONFIG_FORMAT;
+ EXPECT_EQ(BAD_VALUE, EffectUtils::effectBufferConfigFromHal(halInvalidFormat, false /*isInput*/,
+ &invalid));
+ EXPECT_EQ(BAD_VALUE,
+ EffectUtils::effectBufferConfigFromHal(halInvalidFormat, true /*isInput*/, &invalid));
+}
+
+TEST(EffectUtils, ConvertBufferConfig) {
+ EffectBufferConfig empty;
+ buffer_config_t halEmpty;
+ EXPECT_EQ(NO_ERROR, EffectUtils::effectBufferConfigToHal(empty, &halEmpty));
+ EffectBufferConfig emptyBackOut;
+ EXPECT_EQ(NO_ERROR,
+ EffectUtils::effectBufferConfigFromHal(halEmpty, false /*isInput*/, &emptyBackOut));
+ EXPECT_EQ(empty, emptyBackOut);
+ EffectBufferConfig emptyBackIn;
+ EXPECT_EQ(NO_ERROR,
+ EffectUtils::effectBufferConfigFromHal(halEmpty, true /*isInput*/, &emptyBackIn));
+ EXPECT_EQ(empty, emptyBackIn);
+
+ EffectBufferConfig chanMask;
+ chanMask.base.channelMask.value(toString(xsd::AudioChannelMask::AUDIO_CHANNEL_OUT_STEREO));
+ buffer_config_t halChanMask;
+ EXPECT_EQ(NO_ERROR, EffectUtils::effectBufferConfigToHal(chanMask, &halChanMask));
+ EffectBufferConfig chanMaskBack;
+ EXPECT_EQ(NO_ERROR, EffectUtils::effectBufferConfigFromHal(halChanMask, false /*isInput*/,
+ &chanMaskBack));
+ EXPECT_EQ(chanMask, chanMaskBack);
+
+ EffectBufferConfig format;
+ format.base.format.value(toString(xsd::AudioFormat::AUDIO_FORMAT_PCM_16_BIT));
+ buffer_config_t halFormat;
+ EXPECT_EQ(NO_ERROR, EffectUtils::effectBufferConfigToHal(format, &halFormat));
+ EffectBufferConfig formatBackOut;
+ EXPECT_EQ(NO_ERROR,
+ EffectUtils::effectBufferConfigFromHal(halFormat, false /*isInput*/, &formatBackOut));
+ EXPECT_EQ(format, formatBackOut);
+ EffectBufferConfig formatBackIn;
+ EXPECT_EQ(NO_ERROR,
+ EffectUtils::effectBufferConfigFromHal(halFormat, true /*isInput*/, &formatBackIn));
+ EXPECT_EQ(format, formatBackIn);
+}
+
+TEST(EffectUtils, ConvertDescriptor) {
+ EffectDescriptor desc{};
+ effect_descriptor_t halDesc;
+ EXPECT_EQ(NO_ERROR, EffectUtils::effectDescriptorToHal(desc, &halDesc));
+ EffectDescriptor descBack;
+ EXPECT_EQ(NO_ERROR, EffectUtils::effectDescriptorFromHal(halDesc, &descBack));
+ EXPECT_EQ(desc, descBack);
+}
diff --git a/bluetooth/audio/2.0/default/Android.bp b/bluetooth/audio/2.0/default/Android.bp
index 0db0028..8ed631e 100644
--- a/bluetooth/audio/2.0/default/Android.bp
+++ b/bluetooth/audio/2.0/default/Android.bp
@@ -23,24 +23,3 @@
"libutils",
],
}
-
-cc_library_shared {
- name: "libbluetooth_audio_session",
- defaults: ["hidl_defaults"],
- vendor: true,
- srcs: [
- "session/BluetoothAudioSession.cpp",
- "session/BluetoothAudioSupportedCodecsDB.cpp",
- ],
- export_include_dirs: ["session/"],
- header_libs: ["libhardware_headers"],
- shared_libs: [
- "android.hardware.bluetooth.audio@2.0",
- "libbase",
- "libcutils",
- "libfmq",
- "libhidlbase",
- "liblog",
- "libutils",
- ],
-}
diff --git a/bluetooth/audio/2.1/default/A2dpOffloadAudioProvider.cpp b/bluetooth/audio/2.1/default/A2dpOffloadAudioProvider.cpp
index b4a61b6..3fe1a4d 100644
--- a/bluetooth/audio/2.1/default/A2dpOffloadAudioProvider.cpp
+++ b/bluetooth/audio/2.1/default/A2dpOffloadAudioProvider.cpp
@@ -22,8 +22,8 @@
#include <fmq/MessageQueue.h>
#include <hidl/MQDescriptor.h>
-#include "BluetoothAudioSessionReport.h"
-#include "BluetoothAudioSupportedCodecsDB.h"
+#include "BluetoothAudioSessionReport_2_1.h"
+#include "BluetoothAudioSupportedCodecsDB_2_1.h"
namespace android {
namespace hardware {
@@ -32,7 +32,7 @@
namespace V2_1 {
namespace implementation {
-using ::android::bluetooth::audio::BluetoothAudioSessionReport;
+using ::android::bluetooth::audio::BluetoothAudioSessionReport_2_1;
using ::android::hardware::kSynchronizedReadWrite;
using ::android::hardware::MessageQueue;
using ::android::hardware::Void;
@@ -81,8 +81,8 @@
Return<void> A2dpOffloadAudioProvider::onSessionReady(
startSession_cb _hidl_cb) {
- BluetoothAudioSessionReport::OnSessionStarted(session_type_, stack_iface_,
- nullptr, audio_config_);
+ BluetoothAudioSessionReport_2_1::OnSessionStarted(session_type_, stack_iface_,
+ nullptr, audio_config_);
_hidl_cb(BluetoothAudioStatus::SUCCESS, DataMQ::Descriptor());
return Void();
}
diff --git a/bluetooth/audio/2.1/default/A2dpSoftwareAudioProvider.cpp b/bluetooth/audio/2.1/default/A2dpSoftwareAudioProvider.cpp
index a67c341..a37176b 100644
--- a/bluetooth/audio/2.1/default/A2dpSoftwareAudioProvider.cpp
+++ b/bluetooth/audio/2.1/default/A2dpSoftwareAudioProvider.cpp
@@ -20,8 +20,8 @@
#include <android-base/logging.h>
-#include "BluetoothAudioSessionReport.h"
-#include "BluetoothAudioSupportedCodecsDB.h"
+#include "BluetoothAudioSessionReport_2_1.h"
+#include "BluetoothAudioSupportedCodecsDB_2_1.h"
namespace android {
namespace hardware {
@@ -30,7 +30,7 @@
namespace V2_1 {
namespace implementation {
-using ::android::bluetooth::audio::BluetoothAudioSessionReport;
+using ::android::bluetooth::audio::BluetoothAudioSessionReport_2_1;
using ::android::hardware::Void;
using ::android::hardware::bluetooth::audio::V2_0::AudioConfiguration;
@@ -96,7 +96,7 @@
Return<void> A2dpSoftwareAudioProvider::onSessionReady(
startSession_cb _hidl_cb) {
if (mDataMQ && mDataMQ->isValid()) {
- BluetoothAudioSessionReport::OnSessionStarted(
+ BluetoothAudioSessionReport_2_1::OnSessionStarted(
session_type_, stack_iface_, mDataMQ->getDesc(), audio_config_);
_hidl_cb(BluetoothAudioStatus::SUCCESS, *mDataMQ->getDesc());
} else {
diff --git a/bluetooth/audio/2.1/default/Android.bp b/bluetooth/audio/2.1/default/Android.bp
index 5381fec..c05aa3f 100644
--- a/bluetooth/audio/2.1/default/Android.bp
+++ b/bluetooth/audio/2.1/default/Android.bp
@@ -16,29 +16,7 @@
"android.hardware.bluetooth.audio@2.0",
"android.hardware.bluetooth.audio@2.1",
"libbase",
- "libbluetooth_audio_session_2_1",
- "libcutils",
- "libfmq",
- "libhidlbase",
- "liblog",
- "libutils",
- ],
-}
-
-cc_library_shared {
- name: "libbluetooth_audio_session_2_1",
- defaults: ["hidl_defaults"],
- vendor: true,
- srcs: [
- "session/BluetoothAudioSession.cpp",
- "session/BluetoothAudioSupportedCodecsDB.cpp",
- ],
- export_include_dirs: ["session/"],
- header_libs: ["libhardware_headers"],
- shared_libs: [
- "android.hardware.bluetooth.audio@2.0",
- "android.hardware.bluetooth.audio@2.1",
- "libbase",
+ "libbluetooth_audio_session",
"libcutils",
"libfmq",
"libhidlbase",
diff --git a/bluetooth/audio/2.1/default/BluetoothAudioProvider.cpp b/bluetooth/audio/2.1/default/BluetoothAudioProvider.cpp
index 73fe06c..38889ae 100644
--- a/bluetooth/audio/2.1/default/BluetoothAudioProvider.cpp
+++ b/bluetooth/audio/2.1/default/BluetoothAudioProvider.cpp
@@ -20,8 +20,8 @@
#include <android-base/logging.h>
-#include "BluetoothAudioSessionReport.h"
-#include "BluetoothAudioSupportedCodecsDB.h"
+#include "BluetoothAudioSessionReport_2_1.h"
+#include "BluetoothAudioSupportedCodecsDB_2_1.h"
namespace android {
namespace hardware {
@@ -30,7 +30,7 @@
namespace V2_1 {
namespace implementation {
-using ::android::bluetooth::audio::BluetoothAudioSessionReport;
+using ::android::bluetooth::audio::BluetoothAudioSessionReport_2_1;
using ::android::hardware::kSynchronizedReadWrite;
using ::android::hardware::MessageQueue;
using ::android::hardware::Void;
@@ -105,8 +105,8 @@
* HAL server should start the streaming on data path.
*/
if (stack_iface_) {
- BluetoothAudioSessionReport::ReportControlStatus(session_type_, true,
- status);
+ BluetoothAudioSessionReport_2_1::ReportControlStatus(session_type_, true,
+ status);
} else {
LOG(WARNING) << __func__ << " - SessionType=" << toString(session_type_)
<< ", status=" << toString(status) << " has NO session";
@@ -125,8 +125,8 @@
* HAL server should suspend the streaming on data path.
*/
if (stack_iface_) {
- BluetoothAudioSessionReport::ReportControlStatus(session_type_, false,
- status);
+ BluetoothAudioSessionReport_2_1::ReportControlStatus(session_type_, false,
+ status);
} else {
LOG(WARNING) << __func__ << " - SessionType=" << toString(session_type_)
<< ", status=" << toString(status) << " has NO session";
@@ -139,7 +139,7 @@
LOG(INFO) << __func__ << " - SessionType=" << toString(session_type_);
if (stack_iface_) {
- BluetoothAudioSessionReport::OnSessionEnded(session_type_);
+ BluetoothAudioSessionReport_2_1::OnSessionEnded(session_type_);
stack_iface_->unlinkToDeath(death_recipient_);
} else {
LOG(INFO) << __func__ << " - SessionType=" << toString(session_type_)
diff --git a/bluetooth/audio/2.1/default/BluetoothAudioProvidersFactory.cpp b/bluetooth/audio/2.1/default/BluetoothAudioProvidersFactory.cpp
index adf2717..e1b1ac6 100644
--- a/bluetooth/audio/2.1/default/BluetoothAudioProvidersFactory.cpp
+++ b/bluetooth/audio/2.1/default/BluetoothAudioProvidersFactory.cpp
@@ -20,7 +20,7 @@
#include <android-base/logging.h>
-#include "BluetoothAudioSupportedCodecsDB.h"
+#include "BluetoothAudioSupportedCodecsDB_2_1.h"
namespace android {
namespace hardware {
diff --git a/bluetooth/audio/2.1/default/HearingAidAudioProvider.cpp b/bluetooth/audio/2.1/default/HearingAidAudioProvider.cpp
index aded7e1..712bd4f 100644
--- a/bluetooth/audio/2.1/default/HearingAidAudioProvider.cpp
+++ b/bluetooth/audio/2.1/default/HearingAidAudioProvider.cpp
@@ -20,8 +20,8 @@
#include <android-base/logging.h>
-#include "BluetoothAudioSessionReport.h"
-#include "BluetoothAudioSupportedCodecsDB.h"
+#include "BluetoothAudioSessionReport_2_1.h"
+#include "BluetoothAudioSupportedCodecsDB_2_1.h"
namespace android {
namespace hardware {
@@ -30,7 +30,7 @@
namespace V2_1 {
namespace implementation {
-using ::android::bluetooth::audio::BluetoothAudioSessionReport;
+using ::android::bluetooth::audio::BluetoothAudioSessionReport_2_1;
using ::android::hardware::Void;
using ::android::hardware::bluetooth::audio::V2_0::AudioConfiguration;
@@ -95,7 +95,7 @@
Return<void> HearingAidAudioProvider::onSessionReady(startSession_cb _hidl_cb) {
if (mDataMQ && mDataMQ->isValid()) {
- BluetoothAudioSessionReport::OnSessionStarted(
+ BluetoothAudioSessionReport_2_1::OnSessionStarted(
session_type_, stack_iface_, mDataMQ->getDesc(), audio_config_);
_hidl_cb(BluetoothAudioStatus::SUCCESS, *mDataMQ->getDesc());
} else {
diff --git a/bluetooth/audio/2.1/default/LeAudioAudioProvider.cpp b/bluetooth/audio/2.1/default/LeAudioAudioProvider.cpp
index 9c2b4fe..2ebf6c5 100644
--- a/bluetooth/audio/2.1/default/LeAudioAudioProvider.cpp
+++ b/bluetooth/audio/2.1/default/LeAudioAudioProvider.cpp
@@ -21,8 +21,8 @@
#include <android-base/logging.h>
-#include "BluetoothAudioSessionReport.h"
-#include "BluetoothAudioSupportedCodecsDB.h"
+#include "BluetoothAudioSessionReport_2_1.h"
+#include "BluetoothAudioSupportedCodecsDB_2_1.h"
namespace android {
namespace hardware {
@@ -31,7 +31,7 @@
namespace V2_1 {
namespace implementation {
-using ::android::bluetooth::audio::BluetoothAudioSessionReport;
+using ::android::bluetooth::audio::BluetoothAudioSessionReport_2_1;
using ::android::hardware::Void;
using ::android::hardware::bluetooth::audio::V2_0::BitsPerSample;
using ::android::hardware::bluetooth::audio::V2_0::ChannelMode;
@@ -179,7 +179,7 @@
Return<void> LeAudioAudioProvider::onSessionReady(startSession_cb _hidl_cb) {
if (mDataMQ && mDataMQ->isValid()) {
- BluetoothAudioSessionReport::OnSessionStarted(
+ BluetoothAudioSessionReport_2_1::OnSessionStarted(
session_type_, stack_iface_, mDataMQ->getDesc(), audio_config_);
_hidl_cb(BluetoothAudioStatus::SUCCESS, *mDataMQ->getDesc());
} else {
diff --git a/bluetooth/audio/2.1/default/session/BluetoothAudioSession.cpp b/bluetooth/audio/2.1/default/session/BluetoothAudioSession.cpp
deleted file mode 100644
index ea2c54a..0000000
--- a/bluetooth/audio/2.1/default/session/BluetoothAudioSession.cpp
+++ /dev/null
@@ -1,467 +0,0 @@
-/*
- * Copyright 2020 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 "BTAudioProviderSession"
-
-#include "BluetoothAudioSession.h"
-
-#include <android-base/logging.h>
-#include <android-base/stringprintf.h>
-
-namespace android {
-namespace bluetooth {
-namespace audio {
-
-using ::android::hardware::audio::common::V5_0::AudioContentType;
-using ::android::hardware::audio::common::V5_0::AudioUsage;
-using ::android::hardware::audio::common::V5_0::PlaybackTrackMetadata;
-using ::android::hardware::audio::common::V5_0::SourceMetadata;
-using ::android::hardware::bluetooth::audio::V2_0::CodecType;
-using ::android::hardware::bluetooth::audio::V2_0::TimeSpec;
-
-const CodecConfiguration BluetoothAudioSession::kInvalidCodecConfiguration = {
- .codecType = CodecType::UNKNOWN,
- .encodedAudioBitrate = 0x00000000,
- .peerMtu = 0xffff,
- .isScmstEnabled = false,
- .config = {}};
-AudioConfiguration BluetoothAudioSession::invalidSoftwareAudioConfiguration =
- {};
-AudioConfiguration BluetoothAudioSession::invalidOffloadAudioConfiguration = {};
-
-static constexpr int kFmqSendTimeoutMs = 1000; // 1000 ms timeout for sending
-static constexpr int kFmqReceiveTimeoutMs =
- 1000; // 1000 ms timeout for receiving
-static constexpr int kWritePollMs = 1; // polled non-blocking interval
-static constexpr int kReadPollMs = 1; // polled non-blocking interval
-
-static inline timespec timespec_convert_from_hal(const TimeSpec& TS) {
- return {.tv_sec = static_cast<long>(TS.tvSec),
- .tv_nsec = static_cast<long>(TS.tvNSec)};
-}
-
-BluetoothAudioSession::BluetoothAudioSession(const SessionType& session_type)
- : session_type_(session_type), stack_iface_(nullptr), mDataMQ(nullptr) {
- invalidSoftwareAudioConfiguration.pcmConfig(kInvalidPcmParameters);
- invalidOffloadAudioConfiguration.codecConfig(kInvalidCodecConfiguration);
-}
-
-// The report function is used to report that the Bluetooth stack has started
-// this session without any failure, and will invoke session_changed_cb_ to
-// notify those registered bluetooth_audio outputs
-void BluetoothAudioSession::OnSessionStarted(
- const sp<IBluetoothAudioPort> stack_iface, const DataMQ::Descriptor* dataMQ,
- const AudioConfiguration& audio_config) {
- std::lock_guard<std::recursive_mutex> guard(mutex_);
- if (stack_iface == nullptr) {
- LOG(ERROR) << __func__ << " - SessionType=" << toString(session_type_)
- << ", IBluetoothAudioPort Invalid";
- } else if (!UpdateAudioConfig(audio_config)) {
- LOG(ERROR) << __func__ << " - SessionType=" << toString(session_type_)
- << ", AudioConfiguration=" << toString(audio_config)
- << " Invalid";
- } else if (!UpdateDataPath(dataMQ)) {
- LOG(ERROR) << __func__ << " - SessionType=" << toString(session_type_)
- << " DataMQ Invalid";
- audio_config_ =
- (session_type_ == SessionType::A2DP_HARDWARE_OFFLOAD_DATAPATH
- ? kInvalidOffloadAudioConfiguration
- : kInvalidSoftwareAudioConfiguration);
- } else {
- stack_iface_ = stack_iface;
- LOG(INFO) << __func__ << " - SessionType=" << toString(session_type_)
- << ", AudioConfiguration=" << toString(audio_config);
- ReportSessionStatus();
- }
-}
-
-// The report function is used to report that the Bluetooth stack has ended the
-// session, and will invoke session_changed_cb_ to notify registered
-// bluetooth_audio outputs
-void BluetoothAudioSession::OnSessionEnded() {
- std::lock_guard<std::recursive_mutex> guard(mutex_);
- bool toggled = IsSessionReady();
- LOG(INFO) << __func__ << " - SessionType=" << toString(session_type_);
- audio_config_ = (session_type_ == SessionType::A2DP_HARDWARE_OFFLOAD_DATAPATH
- ? kInvalidOffloadAudioConfiguration
- : kInvalidSoftwareAudioConfiguration);
- stack_iface_ = nullptr;
- UpdateDataPath(nullptr);
- if (toggled) {
- ReportSessionStatus();
- }
-}
-
-// invoking the registered session_changed_cb_
-void BluetoothAudioSession::ReportSessionStatus() {
- // This is locked already by OnSessionStarted / OnSessionEnded
- if (observers_.empty()) {
- LOG(INFO) << __func__ << " - SessionType=" << toString(session_type_)
- << " has NO port state observer";
- return;
- }
- for (auto& observer : observers_) {
- uint16_t cookie = observer.first;
- std::shared_ptr<struct PortStatusCallbacks> cb = observer.second;
- LOG(INFO) << __func__ << " - SessionType=" << toString(session_type_)
- << " notify to bluetooth_audio=0x"
- << android::base::StringPrintf("%04x", cookie);
- cb->session_changed_cb_(cookie);
- }
-}
-
-// The report function is used to report that the Bluetooth stack has notified
-// the result of startStream or suspendStream, and will invoke
-// control_result_cb_ to notify registered bluetooth_audio outputs
-void BluetoothAudioSession::ReportControlStatus(
- bool start_resp, const BluetoothAudioStatus& status) {
- std::lock_guard<std::recursive_mutex> guard(mutex_);
- if (observers_.empty()) {
- LOG(WARNING) << __func__ << " - SessionType=" << toString(session_type_)
- << " has NO port state observer";
- return;
- }
- for (auto& observer : observers_) {
- uint16_t cookie = observer.first;
- std::shared_ptr<struct PortStatusCallbacks> cb = observer.second;
- LOG(INFO) << __func__ << " - status=" << toString(status)
- << " for SessionType=" << toString(session_type_)
- << ", bluetooth_audio=0x"
- << android::base::StringPrintf("%04x", cookie)
- << (start_resp ? " started" : " suspended");
- cb->control_result_cb_(cookie, start_resp, status);
- }
-}
-
-// The function helps to check if this session is ready or not
-// @return: true if the Bluetooth stack has started the specified session
-bool BluetoothAudioSession::IsSessionReady() {
- std::lock_guard<std::recursive_mutex> guard(mutex_);
- bool dataMQ_valid =
- (session_type_ == SessionType::A2DP_HARDWARE_OFFLOAD_DATAPATH ||
- (mDataMQ != nullptr && mDataMQ->isValid()));
- return stack_iface_ != nullptr && dataMQ_valid;
-}
-
-bool BluetoothAudioSession::UpdateDataPath(const DataMQ::Descriptor* dataMQ) {
- if (dataMQ == nullptr) {
- // usecase of reset by nullptr
- mDataMQ = nullptr;
- return true;
- }
- std::unique_ptr<DataMQ> tempDataMQ;
- tempDataMQ.reset(new DataMQ(*dataMQ));
- if (!tempDataMQ || !tempDataMQ->isValid()) {
- mDataMQ = nullptr;
- return false;
- }
- mDataMQ = std::move(tempDataMQ);
- return true;
-}
-
-bool BluetoothAudioSession::UpdateAudioConfig(
- const AudioConfiguration& audio_config) {
- bool is_software_session =
- (session_type_ == SessionType::A2DP_SOFTWARE_ENCODING_DATAPATH ||
- session_type_ == SessionType::HEARING_AID_SOFTWARE_ENCODING_DATAPATH ||
- session_type_ == SessionType::LE_AUDIO_SOFTWARE_ENCODING_DATAPATH ||
- session_type_ == SessionType::LE_AUDIO_SOFTWARE_DECODED_DATAPATH);
- bool is_offload_session =
- (session_type_ == SessionType::A2DP_HARDWARE_OFFLOAD_DATAPATH);
- auto audio_config_discriminator = audio_config.getDiscriminator();
- bool is_software_audio_config =
- (is_software_session &&
- audio_config_discriminator ==
- AudioConfiguration::hidl_discriminator::pcmConfig);
- bool is_offload_audio_config =
- (is_offload_session &&
- audio_config_discriminator ==
- AudioConfiguration::hidl_discriminator::codecConfig);
- if (!is_software_audio_config && !is_offload_audio_config) {
- return false;
- }
- audio_config_ = audio_config;
- return true;
-}
-
-// The control function helps the bluetooth_audio module to register
-// PortStatusCallbacks
-// @return: cookie - the assigned number to this bluetooth_audio output
-uint16_t BluetoothAudioSession::RegisterStatusCback(
- const PortStatusCallbacks& cbacks) {
- std::lock_guard<std::recursive_mutex> guard(mutex_);
- uint16_t cookie = ObserversCookieGetInitValue(session_type_);
- uint16_t cookie_upper_bound = ObserversCookieGetUpperBound(session_type_);
-
- while (cookie < cookie_upper_bound) {
- if (observers_.find(cookie) == observers_.end()) {
- break;
- }
- ++cookie;
- }
- if (cookie >= cookie_upper_bound) {
- LOG(ERROR) << __func__ << " - SessionType=" << toString(session_type_)
- << " has " << observers_.size()
- << " observers already (No Resource)";
- return kObserversCookieUndefined;
- }
- std::shared_ptr<struct PortStatusCallbacks> cb =
- std::make_shared<struct PortStatusCallbacks>();
- *cb = cbacks;
- observers_[cookie] = cb;
- return cookie;
-}
-
-// The control function helps the bluetooth_audio module to unregister
-// PortStatusCallbacks
-// @param: cookie - indicates which bluetooth_audio output is
-void BluetoothAudioSession::UnregisterStatusCback(uint16_t cookie) {
- std::lock_guard<std::recursive_mutex> guard(mutex_);
- if (observers_.erase(cookie) != 1) {
- LOG(WARNING) << __func__ << " - SessionType=" << toString(session_type_)
- << " no such provider=0x"
- << android::base::StringPrintf("%04x", cookie);
- }
-}
-
-// The control function is for the bluetooth_audio module to get the current
-// AudioConfiguration
-const AudioConfiguration& BluetoothAudioSession::GetAudioConfig() {
- std::lock_guard<std::recursive_mutex> guard(mutex_);
- if (IsSessionReady()) {
- return audio_config_;
- } else if (session_type_ == SessionType::A2DP_HARDWARE_OFFLOAD_DATAPATH) {
- return kInvalidOffloadAudioConfiguration;
- } else {
- return kInvalidSoftwareAudioConfiguration;
- }
-}
-
-// Those control functions are for the bluetooth_audio module to start, suspend,
-// stop stream, to check position, and to update metadata.
-bool BluetoothAudioSession::StartStream() {
- std::lock_guard<std::recursive_mutex> guard(mutex_);
- if (!IsSessionReady()) {
- LOG(DEBUG) << __func__ << " - SessionType=" << toString(session_type_)
- << " has NO session";
- return false;
- }
- auto hal_retval = stack_iface_->startStream();
- if (!hal_retval.isOk()) {
- LOG(WARNING) << __func__ << " - IBluetoothAudioPort SessionType="
- << toString(session_type_) << " failed";
- return false;
- }
- return true;
-}
-
-bool BluetoothAudioSession::SuspendStream() {
- std::lock_guard<std::recursive_mutex> guard(mutex_);
- if (!IsSessionReady()) {
- LOG(DEBUG) << __func__ << " - SessionType=" << toString(session_type_)
- << " has NO session";
- return false;
- }
- auto hal_retval = stack_iface_->suspendStream();
- if (!hal_retval.isOk()) {
- LOG(WARNING) << __func__ << " - IBluetoothAudioPort SessionType="
- << toString(session_type_) << " failed";
- return false;
- }
- return true;
-}
-
-void BluetoothAudioSession::StopStream() {
- std::lock_guard<std::recursive_mutex> guard(mutex_);
- if (!IsSessionReady()) {
- return;
- }
- auto hal_retval = stack_iface_->stopStream();
- if (!hal_retval.isOk()) {
- LOG(WARNING) << __func__ << " - IBluetoothAudioPort SessionType="
- << toString(session_type_) << " failed";
- }
-}
-
-bool BluetoothAudioSession::GetPresentationPosition(
- uint64_t* remote_delay_report_ns, uint64_t* total_bytes_readed,
- timespec* data_position) {
- std::lock_guard<std::recursive_mutex> guard(mutex_);
- if (!IsSessionReady()) {
- LOG(DEBUG) << __func__ << " - SessionType=" << toString(session_type_)
- << " has NO session";
- return false;
- }
- bool retval = false;
- auto hal_retval = stack_iface_->getPresentationPosition(
- [&retval, &remote_delay_report_ns, &total_bytes_readed, &data_position](
- BluetoothAudioStatus status,
- const uint64_t& remoteDeviceAudioDelayNanos,
- uint64_t transmittedOctets,
- const TimeSpec& transmittedOctetsTimeStamp) {
- if (status == BluetoothAudioStatus::SUCCESS) {
- if (remote_delay_report_ns)
- *remote_delay_report_ns = remoteDeviceAudioDelayNanos;
- if (total_bytes_readed) *total_bytes_readed = transmittedOctets;
- if (data_position)
- *data_position =
- timespec_convert_from_hal(transmittedOctetsTimeStamp);
- retval = true;
- }
- });
- if (!hal_retval.isOk()) {
- LOG(WARNING) << __func__ << " - IBluetoothAudioPort SessionType="
- << toString(session_type_) << " failed";
- return false;
- }
- return retval;
-}
-
-void BluetoothAudioSession::UpdateTracksMetadata(
- const struct source_metadata* source_metadata) {
- std::lock_guard<std::recursive_mutex> guard(mutex_);
- if (!IsSessionReady()) {
- LOG(DEBUG) << __func__ << " - SessionType=" << toString(session_type_)
- << " has NO session";
- return;
- }
-
- ssize_t track_count = source_metadata->track_count;
- LOG(INFO) << __func__ << " - SessionType=" << toString(session_type_) << ", "
- << track_count << " track(s)";
- if (session_type_ == SessionType::A2DP_SOFTWARE_ENCODING_DATAPATH ||
- session_type_ == SessionType::A2DP_HARDWARE_OFFLOAD_DATAPATH) {
- return;
- }
-
- struct playback_track_metadata* track = source_metadata->tracks;
- SourceMetadata sourceMetadata;
- PlaybackTrackMetadata* halMetadata;
-
- sourceMetadata.tracks.resize(track_count);
- halMetadata = sourceMetadata.tracks.data();
- while (track_count && track) {
- halMetadata->usage = static_cast<AudioUsage>(track->usage);
- halMetadata->contentType =
- static_cast<AudioContentType>(track->content_type);
- halMetadata->gain = track->gain;
- LOG(VERBOSE) << __func__ << " - SessionType=" << toString(session_type_)
- << ", usage=" << toString(halMetadata->usage)
- << ", content=" << toString(halMetadata->contentType)
- << ", gain=" << halMetadata->gain;
- --track_count;
- ++track;
- ++halMetadata;
- }
- auto hal_retval = stack_iface_->updateMetadata(sourceMetadata);
- if (!hal_retval.isOk()) {
- LOG(WARNING) << __func__ << " - IBluetoothAudioPort SessionType="
- << toString(session_type_) << " failed";
- }
-}
-
-// The control function writes stream to FMQ
-size_t BluetoothAudioSession::OutWritePcmData(const void* buffer,
- size_t bytes) {
- if (buffer == nullptr || !bytes) return 0;
- size_t totalWritten = 0;
- int ms_timeout = kFmqSendTimeoutMs;
- do {
- std::unique_lock<std::recursive_mutex> lock(mutex_);
- if (!IsSessionReady()) break;
- size_t availableToWrite = mDataMQ->availableToWrite();
- if (availableToWrite) {
- if (availableToWrite > (bytes - totalWritten)) {
- availableToWrite = bytes - totalWritten;
- }
-
- if (!mDataMQ->write(static_cast<const uint8_t*>(buffer) + totalWritten,
- availableToWrite)) {
- ALOGE("FMQ datapath writing %zu/%zu failed", totalWritten, bytes);
- return totalWritten;
- }
- totalWritten += availableToWrite;
- } else if (ms_timeout >= kWritePollMs) {
- lock.unlock();
- usleep(kWritePollMs * 1000);
- ms_timeout -= kWritePollMs;
- } else {
- ALOGD("out data %zu/%zu overflow %d ms", totalWritten, bytes,
- (kFmqSendTimeoutMs - ms_timeout));
- return totalWritten;
- }
- } while (totalWritten < bytes);
- return totalWritten;
-}
-
-// The control function reads stream from FMQ
-size_t BluetoothAudioSession::InReadPcmData(void* buffer, size_t bytes) {
- if (buffer == nullptr || !bytes) return 0;
- size_t totalRead = 0;
- int ms_timeout = kFmqReceiveTimeoutMs;
- do {
- std::unique_lock<std::recursive_mutex> lock(mutex_);
- if (!IsSessionReady()) break;
- size_t availableToRead = mDataMQ->availableToRead();
- if (availableToRead) {
- if (availableToRead > (bytes - totalRead)) {
- availableToRead = bytes - totalRead;
- }
- if (!mDataMQ->read(static_cast<uint8_t*>(buffer) + totalRead,
- availableToRead)) {
- ALOGE("FMQ datapath reading %zu/%zu failed", totalRead, bytes);
- return totalRead;
- }
- totalRead += availableToRead;
- } else if (ms_timeout >= kReadPollMs) {
- lock.unlock();
- usleep(kReadPollMs * 1000);
- ms_timeout -= kReadPollMs;
- continue;
- } else {
- ALOGD("in data %zu/%zu overflow %d ms", totalRead, bytes,
- (kFmqReceiveTimeoutMs - ms_timeout));
- return totalRead;
- }
- } while (totalRead < bytes);
- return totalRead;
-}
-
-std::unique_ptr<BluetoothAudioSessionInstance>
- BluetoothAudioSessionInstance::instance_ptr =
- std::unique_ptr<BluetoothAudioSessionInstance>(
- new BluetoothAudioSessionInstance());
-
-// API to fetch the session
-std::shared_ptr<BluetoothAudioSession>
-BluetoothAudioSessionInstance::GetSessionInstance(
- const SessionType& session_type) {
- std::lock_guard<std::mutex> guard(instance_ptr->mutex_);
- if (!instance_ptr->sessions_map_.empty()) {
- auto entry = instance_ptr->sessions_map_.find(session_type);
- if (entry != instance_ptr->sessions_map_.end()) {
- return entry->second;
- }
- }
- std::shared_ptr<BluetoothAudioSession> session_ptr =
- std::make_shared<BluetoothAudioSession>(session_type);
- instance_ptr->sessions_map_[session_type] = session_ptr;
- return session_ptr;
-}
-
-} // namespace audio
-} // namespace bluetooth
-} // namespace android
diff --git a/bluetooth/audio/2.1/default/session/BluetoothAudioSession.h b/bluetooth/audio/2.1/default/session/BluetoothAudioSession.h
deleted file mode 100644
index 7bc12e6..0000000
--- a/bluetooth/audio/2.1/default/session/BluetoothAudioSession.h
+++ /dev/null
@@ -1,190 +0,0 @@
-/*
- * Copyright 2020 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.
- */
-
-#pragma once
-
-#include <mutex>
-#include <unordered_map>
-
-#include <android/hardware/bluetooth/audio/2.0/IBluetoothAudioPort.h>
-#include <android/hardware/bluetooth/audio/2.1/types.h>
-#include <fmq/MessageQueue.h>
-#include <hardware/audio.h>
-#include <hidl/MQDescriptor.h>
-
-namespace android {
-namespace bluetooth {
-namespace audio {
-
-using ::android::sp;
-using ::android::hardware::kSynchronizedReadWrite;
-using ::android::hardware::MessageQueue;
-using ::android::hardware::bluetooth::audio::V2_0::BitsPerSample;
-using ::android::hardware::bluetooth::audio::V2_0::ChannelMode;
-using ::android::hardware::bluetooth::audio::V2_0::CodecConfiguration;
-using ::android::hardware::bluetooth::audio::V2_0::IBluetoothAudioPort;
-using ::android::hardware::bluetooth::audio::V2_1::AudioConfiguration;
-using ::android::hardware::bluetooth::audio::V2_1::PcmParameters;
-using ::android::hardware::bluetooth::audio::V2_1::SampleRate;
-using ::android::hardware::bluetooth::audio::V2_1::SessionType;
-
-using BluetoothAudioStatus =
- ::android::hardware::bluetooth::audio::V2_0::Status;
-
-using DataMQ = MessageQueue<uint8_t, kSynchronizedReadWrite>;
-
-static constexpr uint16_t kObserversCookieSize = 0x0010; // 0x0000 ~ 0x000f
-constexpr uint16_t kObserversCookieUndefined =
- (static_cast<uint16_t>(SessionType::UNKNOWN) << 8 & 0xff00);
-inline SessionType ObserversCookieGetSessionType(uint16_t cookie) {
- return static_cast<SessionType>(cookie >> 8 & 0x00ff);
-}
-inline uint16_t ObserversCookieGetInitValue(SessionType session_type) {
- return (static_cast<uint16_t>(session_type) << 8 & 0xff00);
-}
-inline uint16_t ObserversCookieGetUpperBound(SessionType session_type) {
- return (static_cast<uint16_t>(session_type) << 8 & 0xff00) +
- kObserversCookieSize;
-}
-
-// This presents the callbacks of started / suspended and session changed,
-// and the bluetooth_audio module uses to receive the status notification
-struct PortStatusCallbacks {
- // control_result_cb_ - when the Bluetooth stack reports results of
- // streamStarted or streamSuspended, the BluetoothAudioProvider will invoke
- // this callback to report to the bluetooth_audio module.
- // @param: cookie - indicates which bluetooth_audio output should handle
- // @param: start_resp - this report is for startStream or not
- // @param: status - the result of startStream
- std::function<void(uint16_t cookie, bool start_resp,
- const BluetoothAudioStatus& status)>
- control_result_cb_;
- // session_changed_cb_ - when the Bluetooth stack start / end session, the
- // BluetoothAudioProvider will invoke this callback to notify to the
- // bluetooth_audio module.
- // @param: cookie - indicates which bluetooth_audio output should handle
- std::function<void(uint16_t cookie)> session_changed_cb_;
-};
-
-class BluetoothAudioSession {
- private:
- // using recursive_mutex to allow hwbinder to re-enter again.
- std::recursive_mutex mutex_;
- SessionType session_type_;
-
- // audio control path to use for both software and offloading
- sp<IBluetoothAudioPort> stack_iface_;
- // Audio path (FMQ) for software encoding/decoded data
- std::unique_ptr<DataMQ> mDataMQ;
- // audio data configuration for both software and offloading
- AudioConfiguration audio_config_;
-
- static AudioConfiguration invalidSoftwareAudioConfiguration;
- static AudioConfiguration invalidOffloadAudioConfiguration;
-
- // saving those registered bluetooth_audio's callbacks
- std::unordered_map<uint16_t, std::shared_ptr<struct PortStatusCallbacks>>
- observers_;
-
- bool UpdateDataPath(const DataMQ::Descriptor* dataMQ);
- bool UpdateAudioConfig(const AudioConfiguration& audio_config);
- // invoking the registered session_changed_cb_
- void ReportSessionStatus();
-
- public:
- BluetoothAudioSession(const SessionType& session_type);
-
- // The function helps to check if this session is ready or not
- // @return: true if the Bluetooth stack has started the specified session
- bool IsSessionReady();
-
- // The report function is used to report that the Bluetooth stack has started
- // this session without any failure, and will invoke session_changed_cb_ to
- // notify those registered bluetooth_audio outputs
- void OnSessionStarted(const sp<IBluetoothAudioPort> stack_iface,
- const DataMQ::Descriptor* dataMQ,
- const AudioConfiguration& audio_config);
-
- // The report function is used to report that the Bluetooth stack has ended
- // the session, and will invoke session_changed_cb_ to notify registered
- // bluetooth_audio outputs
- void OnSessionEnded();
-
- // The report function is used to report that the Bluetooth stack has notified
- // the result of startStream or suspendStream, and will invoke
- // control_result_cb_ to notify registered bluetooth_audio outputs
- void ReportControlStatus(bool start_resp, const BluetoothAudioStatus& status);
-
- // The control function helps the bluetooth_audio module to register
- // PortStatusCallbacks
- // @return: cookie - the assigned number to this bluetooth_audio output
- uint16_t RegisterStatusCback(const PortStatusCallbacks& cbacks);
-
- // The control function helps the bluetooth_audio module to unregister
- // PortStatusCallbacks
- // @param: cookie - indicates which bluetooth_audio output is
- void UnregisterStatusCback(uint16_t cookie);
-
- // The control function is for the bluetooth_audio module to get the current
- // AudioConfiguration
- const AudioConfiguration& GetAudioConfig();
-
- // Those control functions are for the bluetooth_audio module to start,
- // suspend, stop stream, to check position, and to update metadata.
- bool StartStream();
- bool SuspendStream();
- void StopStream();
- bool GetPresentationPosition(uint64_t* remote_delay_report_ns,
- uint64_t* total_bytes_readed,
- timespec* data_position);
- void UpdateTracksMetadata(const struct source_metadata* source_metadata);
-
- // The control function writes stream to FMQ
- size_t OutWritePcmData(const void* buffer, size_t bytes);
- // The control function read stream from FMQ
- size_t InReadPcmData(void* buffer, size_t bytes);
-
- static constexpr PcmParameters kInvalidPcmParameters = {
- .sampleRate = SampleRate::RATE_UNKNOWN,
- .channelMode = ChannelMode::UNKNOWN,
- .bitsPerSample = BitsPerSample::BITS_UNKNOWN,
- .dataIntervalUs = 0,
- };
- // can't be constexpr because of non-literal type
- static const CodecConfiguration kInvalidCodecConfiguration;
-
- static constexpr AudioConfiguration& kInvalidSoftwareAudioConfiguration =
- invalidSoftwareAudioConfiguration;
- static constexpr AudioConfiguration& kInvalidOffloadAudioConfiguration =
- invalidOffloadAudioConfiguration;
-};
-
-class BluetoothAudioSessionInstance {
- public:
- // The API is to fetch the specified session
- static std::shared_ptr<BluetoothAudioSession> GetSessionInstance(
- const SessionType& session_type);
-
- private:
- static std::unique_ptr<BluetoothAudioSessionInstance> instance_ptr;
- std::mutex mutex_;
- std::unordered_map<SessionType, std::shared_ptr<BluetoothAudioSession>>
- sessions_map_;
-};
-
-} // namespace audio
-} // namespace bluetooth
-} // namespace android
diff --git a/bluetooth/audio/2.1/default/session/BluetoothAudioSessionControl.h b/bluetooth/audio/2.1/default/session/BluetoothAudioSessionControl.h
deleted file mode 100644
index 017a611..0000000
--- a/bluetooth/audio/2.1/default/session/BluetoothAudioSessionControl.h
+++ /dev/null
@@ -1,154 +0,0 @@
-/*
- * Copyright 2020 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.
- */
-
-#pragma once
-
-#include "BluetoothAudioSession.h"
-
-namespace android {
-namespace bluetooth {
-namespace audio {
-
-class BluetoothAudioSessionControl {
- public:
- // The control API helps to check if session is ready or not
- // @return: true if the Bluetooth stack has started th specified session
- static bool IsSessionReady(const SessionType& session_type) {
- std::shared_ptr<BluetoothAudioSession> session_ptr =
- BluetoothAudioSessionInstance::GetSessionInstance(session_type);
- if (session_ptr != nullptr) {
- return session_ptr->IsSessionReady();
- }
- return false;
- }
-
- // The control API helps the bluetooth_audio module to register
- // PortStatusCallbacks
- // @return: cookie - the assigned number to this bluetooth_audio output
- static uint16_t RegisterControlResultCback(
- const SessionType& session_type, const PortStatusCallbacks& cbacks) {
- std::shared_ptr<BluetoothAudioSession> session_ptr =
- BluetoothAudioSessionInstance::GetSessionInstance(session_type);
- if (session_ptr != nullptr) {
- return session_ptr->RegisterStatusCback(cbacks);
- }
- return kObserversCookieUndefined;
- }
-
- // The control API helps the bluetooth_audio module to unregister
- // PortStatusCallbacks
- // @param: cookie - indicates which bluetooth_audio output is
- static void UnregisterControlResultCback(const SessionType& session_type,
- uint16_t cookie) {
- std::shared_ptr<BluetoothAudioSession> session_ptr =
- BluetoothAudioSessionInstance::GetSessionInstance(session_type);
- if (session_ptr != nullptr) {
- session_ptr->UnregisterStatusCback(cookie);
- }
- }
-
- // The control API for the bluetooth_audio module to get current
- // AudioConfiguration
- static const AudioConfiguration& GetAudioConfig(
- const SessionType& session_type) {
- std::shared_ptr<BluetoothAudioSession> session_ptr =
- BluetoothAudioSessionInstance::GetSessionInstance(session_type);
- if (session_ptr != nullptr) {
- return session_ptr->GetAudioConfig();
- } else if (session_type == SessionType::A2DP_HARDWARE_OFFLOAD_DATAPATH) {
- return BluetoothAudioSession::kInvalidOffloadAudioConfiguration;
- } else {
- return BluetoothAudioSession::kInvalidSoftwareAudioConfiguration;
- }
- }
-
- // Those control APIs for the bluetooth_audio module to start / suspend / stop
- // stream, to check position, and to update metadata.
- static bool StartStream(const SessionType& session_type) {
- std::shared_ptr<BluetoothAudioSession> session_ptr =
- BluetoothAudioSessionInstance::GetSessionInstance(session_type);
- if (session_ptr != nullptr) {
- return session_ptr->StartStream();
- }
- return false;
- }
-
- static bool SuspendStream(const SessionType& session_type) {
- std::shared_ptr<BluetoothAudioSession> session_ptr =
- BluetoothAudioSessionInstance::GetSessionInstance(session_type);
- if (session_ptr != nullptr) {
- return session_ptr->SuspendStream();
- }
- return false;
- }
-
- static void StopStream(const SessionType& session_type) {
- std::shared_ptr<BluetoothAudioSession> session_ptr =
- BluetoothAudioSessionInstance::GetSessionInstance(session_type);
- if (session_ptr != nullptr) {
- session_ptr->StopStream();
- }
- }
-
- static bool GetPresentationPosition(const SessionType& session_type,
- uint64_t* remote_delay_report_ns,
- uint64_t* total_bytes_readed,
- timespec* data_position) {
- std::shared_ptr<BluetoothAudioSession> session_ptr =
- BluetoothAudioSessionInstance::GetSessionInstance(session_type);
- if (session_ptr != nullptr) {
- return session_ptr->GetPresentationPosition(
- remote_delay_report_ns, total_bytes_readed, data_position);
- }
- return false;
- }
-
- static void UpdateTracksMetadata(
- const SessionType& session_type,
- const struct source_metadata* source_metadata) {
- std::shared_ptr<BluetoothAudioSession> session_ptr =
- BluetoothAudioSessionInstance::GetSessionInstance(session_type);
- if (session_ptr != nullptr) {
- session_ptr->UpdateTracksMetadata(source_metadata);
- }
- }
-
- // The control API writes stream to FMQ
- static size_t OutWritePcmData(const SessionType& session_type,
- const void* buffer, size_t bytes) {
- std::shared_ptr<BluetoothAudioSession> session_ptr =
- BluetoothAudioSessionInstance::GetSessionInstance(session_type);
- if (session_ptr != nullptr) {
- return session_ptr->OutWritePcmData(buffer, bytes);
- }
- return 0;
- }
-
- // The control API reads stream from FMQ
- static size_t InReadPcmData(const SessionType& session_type, void* buffer,
- size_t bytes) {
- std::shared_ptr<BluetoothAudioSession> session_ptr =
- BluetoothAudioSessionInstance::GetSessionInstance(session_type);
- if (session_ptr != nullptr) {
- return session_ptr->InReadPcmData(buffer, bytes);
- }
- return 0;
- }
-};
-
-} // namespace audio
-} // namespace bluetooth
-} // namespace android
diff --git a/bluetooth/audio/2.1/default/session/BluetoothAudioSessionReport.h b/bluetooth/audio/2.1/default/session/BluetoothAudioSessionReport.h
deleted file mode 100644
index 267bf8f..0000000
--- a/bluetooth/audio/2.1/default/session/BluetoothAudioSessionReport.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright 2020 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.
- */
-
-#pragma once
-
-#include "BluetoothAudioSession.h"
-
-namespace android {
-namespace bluetooth {
-namespace audio {
-
-class BluetoothAudioSessionReport {
- public:
- // The API reports the Bluetooth stack has started the session, and will
- // inform registered bluetooth_audio session
- static void OnSessionStarted(const SessionType& session_type,
- const sp<IBluetoothAudioPort> host_iface,
- const DataMQ::Descriptor* dataMQ,
- const AudioConfiguration& audio_config) {
- std::shared_ptr<BluetoothAudioSession> session_ptr =
- BluetoothAudioSessionInstance::GetSessionInstance(session_type);
- if (session_ptr != nullptr) {
- session_ptr->OnSessionStarted(host_iface, dataMQ, audio_config);
- }
- }
- // The API reports the Bluetooth stack has ended the session, and will
- // inform registered bluetooth_audio outputs
- static void OnSessionEnded(const SessionType& session_type) {
- std::shared_ptr<BluetoothAudioSession> session_ptr =
- BluetoothAudioSessionInstance::GetSessionInstance(session_type);
- if (session_ptr != nullptr) {
- session_ptr->OnSessionEnded();
- }
- }
- // The API reports the Bluetooth stack has replied the result of startStream
- // or suspendStream, and will inform registered bluetooth_audio outputs
- static void ReportControlStatus(const SessionType& session_type,
- const bool& start_resp,
- const BluetoothAudioStatus& status) {
- std::shared_ptr<BluetoothAudioSession> session_ptr =
- BluetoothAudioSessionInstance::GetSessionInstance(session_type);
- if (session_ptr != nullptr) {
- session_ptr->ReportControlStatus(start_resp, status);
- }
- }
-};
-
-} // namespace audio
-} // namespace bluetooth
-} // namespace android
diff --git a/bluetooth/audio/2.1/default/session/BluetoothAudioSupportedCodecsDB.cpp b/bluetooth/audio/2.1/default/session/BluetoothAudioSupportedCodecsDB.cpp
deleted file mode 100644
index 0937f44..0000000
--- a/bluetooth/audio/2.1/default/session/BluetoothAudioSupportedCodecsDB.cpp
+++ /dev/null
@@ -1,489 +0,0 @@
-/*
- * Copyright 2020 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 "BTAudioProviderSessionCodecsDB"
-
-#include "BluetoothAudioSupportedCodecsDB.h"
-
-#include <android-base/logging.h>
-
-namespace android {
-namespace bluetooth {
-namespace audio {
-
-using ::android::hardware::bluetooth::audio::V2_0::AacObjectType;
-using ::android::hardware::bluetooth::audio::V2_0::AacParameters;
-using ::android::hardware::bluetooth::audio::V2_0::AacVariableBitRate;
-using ::android::hardware::bluetooth::audio::V2_0::AptxParameters;
-using ::android::hardware::bluetooth::audio::V2_0::BitsPerSample;
-using ::android::hardware::bluetooth::audio::V2_0::ChannelMode;
-using ::android::hardware::bluetooth::audio::V2_0::CodecType;
-using ::android::hardware::bluetooth::audio::V2_0::LdacChannelMode;
-using ::android::hardware::bluetooth::audio::V2_0::LdacParameters;
-using ::android::hardware::bluetooth::audio::V2_0::LdacQualityIndex;
-using ::android::hardware::bluetooth::audio::V2_0::SbcAllocMethod;
-using ::android::hardware::bluetooth::audio::V2_0::SbcBlockLength;
-using ::android::hardware::bluetooth::audio::V2_0::SbcChannelMode;
-using ::android::hardware::bluetooth::audio::V2_0::SbcNumSubbands;
-using ::android::hardware::bluetooth::audio::V2_0::SbcParameters;
-using ::android::hardware::bluetooth::audio::V2_1::SampleRate;
-
-// Default Supported PCM Parameters
-static const ::android::hardware::bluetooth::audio::V2_0::PcmParameters
- kDefaultSoftwarePcmCapabilities = {
- .sampleRate = static_cast<
- android::hardware::bluetooth::audio::V2_0::SampleRate>(
- android::hardware::bluetooth::audio::V2_0::SampleRate::RATE_44100 |
- android::hardware::bluetooth::audio::V2_0::SampleRate::RATE_48000 |
- android::hardware::bluetooth::audio::V2_0::SampleRate::RATE_88200 |
- android::hardware::bluetooth::audio::V2_0::SampleRate::RATE_96000 |
- android::hardware::bluetooth::audio::V2_0::SampleRate::RATE_16000),
- .channelMode =
- static_cast<ChannelMode>(ChannelMode::MONO | ChannelMode::STEREO),
- .bitsPerSample = static_cast<BitsPerSample>(BitsPerSample::BITS_16 |
- BitsPerSample::BITS_24 |
- BitsPerSample::BITS_32)};
-
-static const PcmParameters kDefaultSoftwarePcmCapabilities_2_1 = {
- .sampleRate = static_cast<SampleRate>(
- SampleRate::RATE_48000 | SampleRate::RATE_44100 |
- SampleRate::RATE_32000 | SampleRate::RATE_24000 |
- SampleRate::RATE_16000 | SampleRate::RATE_8000),
- .channelMode =
- static_cast<ChannelMode>(ChannelMode::MONO | ChannelMode::STEREO),
- .bitsPerSample = static_cast<BitsPerSample>(BitsPerSample::BITS_16)};
-
-// Default Supported Codecs
-// SBC: mSampleRate:(44100), mBitsPerSample:(16), mChannelMode:(MONO|STEREO)
-// all blocks | subbands 8 | Loudness
-static const SbcParameters kDefaultOffloadSbcCapability = {
- .sampleRate =
- android::hardware::bluetooth::audio::V2_0::SampleRate::RATE_44100,
- .channelMode = static_cast<SbcChannelMode>(SbcChannelMode::MONO |
- SbcChannelMode::JOINT_STEREO),
- .blockLength = static_cast<SbcBlockLength>(
- SbcBlockLength::BLOCKS_4 | SbcBlockLength::BLOCKS_8 |
- SbcBlockLength::BLOCKS_12 | SbcBlockLength::BLOCKS_16),
- .numSubbands = SbcNumSubbands::SUBBAND_8,
- .allocMethod = SbcAllocMethod::ALLOC_MD_L,
- .bitsPerSample = BitsPerSample::BITS_16,
- .minBitpool = 2,
- .maxBitpool = 53};
-
-// AAC: mSampleRate:(44100), mBitsPerSample:(16), mChannelMode:(STEREO)
-static const AacParameters kDefaultOffloadAacCapability = {
- .objectType = AacObjectType::MPEG2_LC,
- .sampleRate =
- android::hardware::bluetooth::audio::V2_0::SampleRate::RATE_44100,
- .channelMode = ChannelMode::STEREO,
- .variableBitRateEnabled = AacVariableBitRate::ENABLED,
- .bitsPerSample = BitsPerSample::BITS_16};
-
-// LDAC: mSampleRate:(44100|48000|88200|96000), mBitsPerSample:(16|24|32),
-// mChannelMode:(DUAL|STEREO)
-static const LdacParameters kDefaultOffloadLdacCapability = {
- .sampleRate =
- static_cast<android::hardware::bluetooth::audio::V2_0::SampleRate>(
- android::hardware::bluetooth::audio::V2_0::SampleRate::RATE_44100 |
- android::hardware::bluetooth::audio::V2_0::SampleRate::RATE_48000 |
- android::hardware::bluetooth::audio::V2_0::SampleRate::RATE_88200 |
- android::hardware::bluetooth::audio::V2_0::SampleRate::RATE_96000),
- .channelMode = static_cast<LdacChannelMode>(LdacChannelMode::DUAL |
- LdacChannelMode::STEREO),
- .qualityIndex = LdacQualityIndex::QUALITY_HIGH,
- .bitsPerSample = static_cast<BitsPerSample>(BitsPerSample::BITS_16 |
- BitsPerSample::BITS_24 |
- BitsPerSample::BITS_32)};
-
-// aptX: mSampleRate:(44100|48000), mBitsPerSample:(16), mChannelMode:(STEREO)
-static const AptxParameters kDefaultOffloadAptxCapability = {
- .sampleRate =
- static_cast<android::hardware::bluetooth::audio::V2_0::SampleRate>(
- android::hardware::bluetooth::audio::V2_0::SampleRate::RATE_44100 |
- android::hardware::bluetooth::audio::V2_0::SampleRate::RATE_48000),
- .channelMode = ChannelMode::STEREO,
- .bitsPerSample = BitsPerSample::BITS_16,
-};
-
-// aptX HD: mSampleRate:(44100|48000), mBitsPerSample:(24),
-// mChannelMode:(STEREO)
-static const AptxParameters kDefaultOffloadAptxHdCapability = {
- .sampleRate =
- static_cast<android::hardware::bluetooth::audio::V2_0::SampleRate>(
- android::hardware::bluetooth::audio::V2_0::SampleRate::RATE_44100 |
- android::hardware::bluetooth::audio::V2_0::SampleRate::RATE_48000),
- .channelMode = ChannelMode::STEREO,
- .bitsPerSample = BitsPerSample::BITS_24,
-};
-
-const std::vector<CodecCapabilities> kDefaultOffloadA2dpCodecCapabilities = {
- {.codecType = CodecType::SBC, .capabilities = {}},
- {.codecType = CodecType::AAC, .capabilities = {}},
- {.codecType = CodecType::LDAC, .capabilities = {}},
- {.codecType = CodecType::APTX, .capabilities = {}},
- {.codecType = CodecType::APTX_HD, .capabilities = {}}};
-
-static bool IsSingleBit(uint32_t bitmasks, uint32_t bitfield) {
- bool single = false;
- uint32_t test_bit = 0x00000001;
- while (test_bit <= bitmasks && test_bit <= bitfield) {
- if (bitfield & test_bit && bitmasks & test_bit) {
- if (single) return false;
- single = true;
- }
- if (test_bit == 0x80000000) break;
- test_bit <<= 1;
- }
- return single;
-}
-
-static bool IsOffloadSbcConfigurationValid(
- const CodecConfiguration::CodecSpecific& codec_specific);
-static bool IsOffloadAacConfigurationValid(
- const CodecConfiguration::CodecSpecific& codec_specific);
-static bool IsOffloadLdacConfigurationValid(
- const CodecConfiguration::CodecSpecific& codec_specific);
-static bool IsOffloadAptxConfigurationValid(
- const CodecConfiguration::CodecSpecific& codec_specific);
-static bool IsOffloadAptxHdConfigurationValid(
- const CodecConfiguration::CodecSpecific& codec_specific);
-
-static bool IsOffloadSbcConfigurationValid(
- const CodecConfiguration::CodecSpecific& codec_specific) {
- if (codec_specific.getDiscriminator() !=
- CodecConfiguration::CodecSpecific::hidl_discriminator::sbcConfig) {
- LOG(WARNING) << __func__
- << ": Invalid CodecSpecific=" << toString(codec_specific);
- return false;
- }
- const SbcParameters sbc_data = codec_specific.sbcConfig();
- if (!IsSingleBit(static_cast<uint32_t>(sbc_data.sampleRate), 0xff) ||
- !IsSingleBit(static_cast<uint32_t>(sbc_data.channelMode), 0x0f) ||
- !IsSingleBit(static_cast<uint32_t>(sbc_data.blockLength), 0xf0) ||
- !IsSingleBit(static_cast<uint32_t>(sbc_data.numSubbands), 0x0c) ||
- !IsSingleBit(static_cast<uint32_t>(sbc_data.allocMethod), 0x03) ||
- !IsSingleBit(static_cast<uint32_t>(sbc_data.bitsPerSample), 0x07) ||
- sbc_data.minBitpool > sbc_data.maxBitpool) {
- LOG(WARNING) << __func__
- << ": Invalid CodecSpecific=" << toString(codec_specific);
- return false;
- } else if ((sbc_data.sampleRate & kDefaultOffloadSbcCapability.sampleRate) &&
- (sbc_data.channelMode &
- kDefaultOffloadSbcCapability.channelMode) &&
- (sbc_data.blockLength &
- kDefaultOffloadSbcCapability.blockLength) &&
- (sbc_data.numSubbands &
- kDefaultOffloadSbcCapability.numSubbands) &&
- (sbc_data.allocMethod &
- kDefaultOffloadSbcCapability.allocMethod) &&
- (sbc_data.bitsPerSample &
- kDefaultOffloadSbcCapability.bitsPerSample) &&
- (kDefaultOffloadSbcCapability.minBitpool <= sbc_data.minBitpool &&
- sbc_data.maxBitpool <= kDefaultOffloadSbcCapability.maxBitpool)) {
- return true;
- }
- LOG(WARNING) << __func__
- << ": Unsupported CodecSpecific=" << toString(codec_specific);
- return false;
-}
-
-static bool IsOffloadAacConfigurationValid(
- const CodecConfiguration::CodecSpecific& codec_specific) {
- if (codec_specific.getDiscriminator() !=
- CodecConfiguration::CodecSpecific::hidl_discriminator::aacConfig) {
- LOG(WARNING) << __func__
- << ": Invalid CodecSpecific=" << toString(codec_specific);
- return false;
- }
- const AacParameters aac_data = codec_specific.aacConfig();
- if (!IsSingleBit(static_cast<uint32_t>(aac_data.objectType), 0xf0) ||
- !IsSingleBit(static_cast<uint32_t>(aac_data.sampleRate), 0xff) ||
- !IsSingleBit(static_cast<uint32_t>(aac_data.channelMode), 0x03) ||
- !IsSingleBit(static_cast<uint32_t>(aac_data.bitsPerSample), 0x07)) {
- LOG(WARNING) << __func__
- << ": Invalid CodecSpecific=" << toString(codec_specific);
- return false;
- } else if ((aac_data.objectType & kDefaultOffloadAacCapability.objectType) &&
- (aac_data.sampleRate & kDefaultOffloadAacCapability.sampleRate) &&
- (aac_data.channelMode &
- kDefaultOffloadAacCapability.channelMode) &&
- (aac_data.variableBitRateEnabled == AacVariableBitRate::DISABLED ||
- kDefaultOffloadAacCapability.variableBitRateEnabled ==
- AacVariableBitRate::ENABLED) &&
- (aac_data.bitsPerSample &
- kDefaultOffloadAacCapability.bitsPerSample)) {
- return true;
- }
- LOG(WARNING) << __func__
- << ": Unsupported CodecSpecific=" << toString(codec_specific);
- return false;
-}
-
-static bool IsOffloadLdacConfigurationValid(
- const CodecConfiguration::CodecSpecific& codec_specific) {
- if (codec_specific.getDiscriminator() !=
- CodecConfiguration::CodecSpecific::hidl_discriminator::ldacConfig) {
- LOG(WARNING) << __func__
- << ": Invalid CodecSpecific=" << toString(codec_specific);
- return false;
- }
- const LdacParameters ldac_data = codec_specific.ldacConfig();
- if (!IsSingleBit(static_cast<uint32_t>(ldac_data.sampleRate), 0xff) ||
- !IsSingleBit(static_cast<uint32_t>(ldac_data.channelMode), 0x07) ||
- (ldac_data.qualityIndex > LdacQualityIndex::QUALITY_LOW &&
- ldac_data.qualityIndex != LdacQualityIndex::QUALITY_ABR) ||
- !IsSingleBit(static_cast<uint32_t>(ldac_data.bitsPerSample), 0x07)) {
- LOG(WARNING) << __func__
- << ": Invalid CodecSpecific=" << toString(codec_specific);
- return false;
- } else if ((ldac_data.sampleRate &
- kDefaultOffloadLdacCapability.sampleRate) &&
- (ldac_data.channelMode &
- kDefaultOffloadLdacCapability.channelMode) &&
- (ldac_data.bitsPerSample &
- kDefaultOffloadLdacCapability.bitsPerSample)) {
- return true;
- }
- LOG(WARNING) << __func__
- << ": Unsupported CodecSpecific=" << toString(codec_specific);
- return false;
-}
-
-static bool IsOffloadAptxConfigurationValid(
- const CodecConfiguration::CodecSpecific& codec_specific) {
- if (codec_specific.getDiscriminator() !=
- CodecConfiguration::CodecSpecific::hidl_discriminator::aptxConfig) {
- LOG(WARNING) << __func__
- << ": Invalid CodecSpecific=" << toString(codec_specific);
- return false;
- }
- const AptxParameters aptx_data = codec_specific.aptxConfig();
- if (!IsSingleBit(static_cast<uint32_t>(aptx_data.sampleRate), 0xff) ||
- !IsSingleBit(static_cast<uint32_t>(aptx_data.channelMode), 0x03) ||
- !IsSingleBit(static_cast<uint32_t>(aptx_data.bitsPerSample), 0x07)) {
- LOG(WARNING) << __func__
- << ": Invalid CodecSpecific=" << toString(codec_specific);
- return false;
- } else if ((aptx_data.sampleRate &
- kDefaultOffloadAptxCapability.sampleRate) &&
- (aptx_data.channelMode &
- kDefaultOffloadAptxCapability.channelMode) &&
- (aptx_data.bitsPerSample &
- kDefaultOffloadAptxCapability.bitsPerSample)) {
- return true;
- }
- LOG(WARNING) << __func__
- << ": Unsupported CodecSpecific=" << toString(codec_specific);
- return false;
-}
-
-static bool IsOffloadAptxHdConfigurationValid(
- const CodecConfiguration::CodecSpecific& codec_specific) {
- if (codec_specific.getDiscriminator() !=
- CodecConfiguration::CodecSpecific::hidl_discriminator::aptxConfig) {
- LOG(WARNING) << __func__
- << ": Invalid CodecSpecific=" << toString(codec_specific);
- return false;
- }
- const AptxParameters aptx_data = codec_specific.aptxConfig();
- if (!IsSingleBit(static_cast<uint32_t>(aptx_data.sampleRate), 0xff) ||
- !IsSingleBit(static_cast<uint32_t>(aptx_data.channelMode), 0x03) ||
- !IsSingleBit(static_cast<uint32_t>(aptx_data.bitsPerSample), 0x07)) {
- LOG(WARNING) << __func__
- << ": Invalid CodecSpecific=" << toString(codec_specific);
- return false;
- } else if ((aptx_data.sampleRate &
- kDefaultOffloadAptxHdCapability.sampleRate) &&
- (aptx_data.channelMode &
- kDefaultOffloadAptxHdCapability.channelMode) &&
- (aptx_data.bitsPerSample &
- kDefaultOffloadAptxHdCapability.bitsPerSample)) {
- return true;
- }
- LOG(WARNING) << __func__
- << ": Unsupported CodecSpecific=" << toString(codec_specific);
- return false;
-}
-
-std::vector<::android::hardware::bluetooth::audio::V2_0::PcmParameters>
-GetSoftwarePcmCapabilities() {
- return std::vector<
- ::android::hardware::bluetooth::audio::V2_0::PcmParameters>(
- 1, kDefaultSoftwarePcmCapabilities);
-}
-
-std::vector<PcmParameters> GetSoftwarePcmCapabilities_2_1() {
- return std::vector<PcmParameters>(1, kDefaultSoftwarePcmCapabilities_2_1);
-}
-
-std::vector<CodecCapabilities> GetOffloadCodecCapabilities(
- const ::android::hardware::bluetooth::audio::V2_0::SessionType&
- session_type) {
- return GetOffloadCodecCapabilities(static_cast<SessionType>(session_type));
-}
-
-std::vector<CodecCapabilities> GetOffloadCodecCapabilities(
- const SessionType& session_type) {
- if (session_type != SessionType::A2DP_HARDWARE_OFFLOAD_DATAPATH) {
- return std::vector<CodecCapabilities>(0);
- }
- std::vector<CodecCapabilities> offload_a2dp_codec_capabilities =
- kDefaultOffloadA2dpCodecCapabilities;
- for (auto& codec_capability : offload_a2dp_codec_capabilities) {
- switch (codec_capability.codecType) {
- case CodecType::SBC:
- codec_capability.capabilities.sbcCapabilities(
- kDefaultOffloadSbcCapability);
- break;
- case CodecType::AAC:
- codec_capability.capabilities.aacCapabilities(
- kDefaultOffloadAacCapability);
- break;
- case CodecType::LDAC:
- codec_capability.capabilities.ldacCapabilities(
- kDefaultOffloadLdacCapability);
- break;
- case CodecType::APTX:
- codec_capability.capabilities.aptxCapabilities(
- kDefaultOffloadAptxCapability);
- break;
- case CodecType::APTX_HD:
- codec_capability.capabilities.aptxCapabilities(
- kDefaultOffloadAptxHdCapability);
- break;
- case CodecType::UNKNOWN:
- codec_capability = {};
- break;
- }
- }
- return offload_a2dp_codec_capabilities;
-}
-
-bool IsSoftwarePcmConfigurationValid(
- const ::android::hardware::bluetooth::audio::V2_0::PcmParameters&
- pcm_config) {
- if ((pcm_config.sampleRate !=
- android::hardware::bluetooth::audio::V2_0::SampleRate::RATE_44100 &&
- pcm_config.sampleRate !=
- android::hardware::bluetooth::audio::V2_0::SampleRate::RATE_48000 &&
- pcm_config.sampleRate !=
- android::hardware::bluetooth::audio::V2_0::SampleRate::RATE_88200 &&
- pcm_config.sampleRate !=
- android::hardware::bluetooth::audio::V2_0::SampleRate::RATE_96000 &&
- pcm_config.sampleRate !=
- android::hardware::bluetooth::audio::V2_0::SampleRate::RATE_16000 &&
- pcm_config.sampleRate !=
- android::hardware::bluetooth::audio::V2_0::SampleRate::RATE_24000) ||
- (pcm_config.bitsPerSample != BitsPerSample::BITS_16 &&
- pcm_config.bitsPerSample != BitsPerSample::BITS_24 &&
- pcm_config.bitsPerSample != BitsPerSample::BITS_32) ||
- (pcm_config.channelMode != ChannelMode::MONO &&
- pcm_config.channelMode != ChannelMode::STEREO)) {
- LOG(WARNING) << __func__
- << ": Invalid PCM Configuration=" << toString(pcm_config);
- return false;
- } else if (pcm_config.sampleRate &
- kDefaultSoftwarePcmCapabilities.sampleRate &&
- pcm_config.bitsPerSample &
- kDefaultSoftwarePcmCapabilities.bitsPerSample &&
- pcm_config.channelMode &
- kDefaultSoftwarePcmCapabilities.channelMode) {
- return true;
- }
- LOG(WARNING) << __func__
- << ": Unsupported PCM Configuration=" << toString(pcm_config);
- return false;
-}
-
-bool IsSoftwarePcmConfigurationValid_2_1(const PcmParameters& pcm_config) {
- if ((pcm_config.sampleRate != SampleRate::RATE_96000 &&
- pcm_config.sampleRate != SampleRate::RATE_88200 &&
- pcm_config.sampleRate != SampleRate::RATE_48000 &&
- pcm_config.sampleRate != SampleRate::RATE_44100 &&
- pcm_config.sampleRate != SampleRate::RATE_32000 &&
- pcm_config.sampleRate != SampleRate::RATE_24000 &&
- pcm_config.sampleRate != SampleRate::RATE_16000 &&
- pcm_config.sampleRate != SampleRate::RATE_8000) ||
- (pcm_config.bitsPerSample != BitsPerSample::BITS_16 &&
- pcm_config.bitsPerSample != BitsPerSample::BITS_24 &&
- pcm_config.bitsPerSample != BitsPerSample::BITS_32) ||
- (pcm_config.channelMode != ChannelMode::MONO &&
- pcm_config.channelMode != ChannelMode::STEREO)) {
- LOG(WARNING) << __func__
- << ": Invalid PCM Configuration=" << toString(pcm_config);
- return false;
- } else if (pcm_config.sampleRate &
- kDefaultSoftwarePcmCapabilities_2_1.sampleRate &&
- pcm_config.bitsPerSample &
- kDefaultSoftwarePcmCapabilities_2_1.bitsPerSample &&
- pcm_config.channelMode &
- kDefaultSoftwarePcmCapabilities_2_1.channelMode &&
- pcm_config.dataIntervalUs != 0) {
- return true;
- }
- LOG(WARNING) << __func__
- << ": Unsupported PCM Configuration=" << toString(pcm_config);
- return false;
-}
-
-bool IsOffloadCodecConfigurationValid(const SessionType& session_type,
- const CodecConfiguration& codec_config) {
- if (session_type != SessionType::A2DP_HARDWARE_OFFLOAD_DATAPATH) {
- LOG(ERROR) << __func__
- << ": Invalid SessionType=" << toString(session_type);
- return false;
- } else if (codec_config.encodedAudioBitrate < 0x00000001 ||
- 0x00ffffff < codec_config.encodedAudioBitrate) {
- LOG(ERROR) << __func__ << ": Unsupported Codec Configuration="
- << toString(codec_config);
- return false;
- }
- const CodecConfiguration::CodecSpecific& codec_specific = codec_config.config;
- switch (codec_config.codecType) {
- case CodecType::SBC:
- if (IsOffloadSbcConfigurationValid(codec_specific)) {
- return true;
- }
- return false;
- case CodecType::AAC:
- if (IsOffloadAacConfigurationValid(codec_specific)) {
- return true;
- }
- return false;
- case CodecType::LDAC:
- if (IsOffloadLdacConfigurationValid(codec_specific)) {
- return true;
- }
- return false;
- case CodecType::APTX:
- if (IsOffloadAptxConfigurationValid(codec_specific)) {
- return true;
- }
- return false;
- case CodecType::APTX_HD:
- if (IsOffloadAptxHdConfigurationValid(codec_specific)) {
- return true;
- }
- return false;
- case CodecType::UNKNOWN:
- return false;
- }
- return false;
-}
-
-} // namespace audio
-} // namespace bluetooth
-} // namespace android
diff --git a/bluetooth/audio/2.1/default/session/BluetoothAudioSupportedCodecsDB.h b/bluetooth/audio/2.1/default/session/BluetoothAudioSupportedCodecsDB.h
deleted file mode 100644
index 9b2f680..0000000
--- a/bluetooth/audio/2.1/default/session/BluetoothAudioSupportedCodecsDB.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright 2020 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.
- */
-
-#pragma once
-
-#include <android/hardware/bluetooth/audio/2.0/types.h>
-#include <android/hardware/bluetooth/audio/2.1/types.h>
-
-namespace android {
-namespace bluetooth {
-namespace audio {
-
-using ::android::hardware::bluetooth::audio::V2_0::CodecCapabilities;
-using ::android::hardware::bluetooth::audio::V2_0::CodecConfiguration;
-using ::android::hardware::bluetooth::audio::V2_1::PcmParameters;
-using ::android::hardware::bluetooth::audio::V2_1::SessionType;
-
-std::vector<::android::hardware::bluetooth::audio::V2_0::PcmParameters>
-GetSoftwarePcmCapabilities();
-std::vector<PcmParameters> GetSoftwarePcmCapabilities_2_1();
-std::vector<CodecCapabilities> GetOffloadCodecCapabilities(
- const SessionType& session_type);
-std::vector<CodecCapabilities> GetOffloadCodecCapabilities(
- const ::android::hardware::bluetooth::audio::V2_0::SessionType&
- session_type);
-
-bool IsSoftwarePcmConfigurationValid_2_1(const PcmParameters& pcm_config);
-bool IsSoftwarePcmConfigurationValid(
- const ::android::hardware::bluetooth::audio::V2_0::PcmParameters&
- pcm_config);
-bool IsOffloadCodecConfigurationValid(const SessionType& session_type,
- const CodecConfiguration& codec_config);
-
-} // namespace audio
-} // namespace bluetooth
-} // namespace android
diff --git a/bluetooth/audio/utils/Android.bp b/bluetooth/audio/utils/Android.bp
new file mode 100644
index 0000000..35476d2
--- /dev/null
+++ b/bluetooth/audio/utils/Android.bp
@@ -0,0 +1,23 @@
+cc_library_shared {
+ name: "libbluetooth_audio_session",
+ defaults: ["hidl_defaults"],
+ vendor: true,
+ srcs: [
+ "session/BluetoothAudioSession.cpp",
+ "session/BluetoothAudioSession_2_1.cpp",
+ "session/BluetoothAudioSupportedCodecsDB.cpp",
+ "session/BluetoothAudioSupportedCodecsDB_2_1.cpp",
+ ],
+ export_include_dirs: ["session/"],
+ header_libs: ["libhardware_headers"],
+ shared_libs: [
+ "android.hardware.bluetooth.audio@2.0",
+ "android.hardware.bluetooth.audio@2.1",
+ "libbase",
+ "libcutils",
+ "libfmq",
+ "libhidlbase",
+ "liblog",
+ "libutils",
+ ],
+}
diff --git a/bluetooth/audio/2.0/default/session/BluetoothAudioSession.cpp b/bluetooth/audio/utils/session/BluetoothAudioSession.cpp
similarity index 100%
rename from bluetooth/audio/2.0/default/session/BluetoothAudioSession.cpp
rename to bluetooth/audio/utils/session/BluetoothAudioSession.cpp
diff --git a/bluetooth/audio/2.0/default/session/BluetoothAudioSession.h b/bluetooth/audio/utils/session/BluetoothAudioSession.h
similarity index 100%
rename from bluetooth/audio/2.0/default/session/BluetoothAudioSession.h
rename to bluetooth/audio/utils/session/BluetoothAudioSession.h
diff --git a/bluetooth/audio/2.0/default/session/BluetoothAudioSessionControl.h b/bluetooth/audio/utils/session/BluetoothAudioSessionControl.h
similarity index 100%
rename from bluetooth/audio/2.0/default/session/BluetoothAudioSessionControl.h
rename to bluetooth/audio/utils/session/BluetoothAudioSessionControl.h
diff --git a/bluetooth/audio/utils/session/BluetoothAudioSessionControl_2_1.h b/bluetooth/audio/utils/session/BluetoothAudioSessionControl_2_1.h
new file mode 100644
index 0000000..86af468
--- /dev/null
+++ b/bluetooth/audio/utils/session/BluetoothAudioSessionControl_2_1.h
@@ -0,0 +1,148 @@
+/*
+ * Copyright 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.
+ */
+
+#pragma once
+
+#include "BluetoothAudioSession_2_1.h"
+
+namespace android {
+namespace bluetooth {
+namespace audio {
+
+class BluetoothAudioSessionControl_2_1 {
+ using SessionType_2_1 =
+ ::android::hardware::bluetooth::audio::V2_1::SessionType;
+
+ public:
+ // The control API helps to check if session is ready or not
+ // @return: true if the Bluetooth stack has started th specified session
+ static bool IsSessionReady(const SessionType_2_1& session_type) {
+ std::shared_ptr<BluetoothAudioSession_2_1> session_ptr =
+ BluetoothAudioSessionInstance_2_1::GetSessionInstance(session_type);
+ if (session_ptr != nullptr) {
+ return session_ptr->GetAudioSession()->IsSessionReady();
+ }
+ return false;
+ }
+
+ // The control API helps the bluetooth_audio module to register
+ // PortStatusCallbacks
+ // @return: cookie - the assigned number to this bluetooth_audio output
+ static uint16_t RegisterControlResultCback(
+ const SessionType_2_1& session_type, const PortStatusCallbacks& cbacks) {
+ std::shared_ptr<BluetoothAudioSession_2_1> session_ptr =
+ BluetoothAudioSessionInstance_2_1::GetSessionInstance(session_type);
+ if (session_ptr != nullptr) {
+ return session_ptr->GetAudioSession()->RegisterStatusCback(cbacks);
+ }
+ return kObserversCookieUndefined;
+ }
+
+ // The control API helps the bluetooth_audio module to unregister
+ // PortStatusCallbacks
+ // @param: cookie - indicates which bluetooth_audio output is
+ static void UnregisterControlResultCback(const SessionType_2_1& session_type,
+ uint16_t cookie) {
+ std::shared_ptr<BluetoothAudioSession_2_1> session_ptr =
+ BluetoothAudioSessionInstance_2_1::GetSessionInstance(session_type);
+ if (session_ptr != nullptr) {
+ session_ptr->GetAudioSession()->UnregisterStatusCback(cookie);
+ }
+ }
+
+ // The control API for the bluetooth_audio module to get current
+ // AudioConfiguration
+ static const AudioConfiguration& GetAudioConfig(
+ const SessionType_2_1& session_type) {
+ std::shared_ptr<BluetoothAudioSession_2_1> session_ptr =
+ BluetoothAudioSessionInstance_2_1::GetSessionInstance(session_type);
+ if (session_ptr != nullptr) {
+ // TODO: map 2.1 to 2.0 audio config inside GetAudioConfig?
+ return session_ptr->GetAudioSession()->GetAudioConfig();
+ } else if (session_type ==
+ SessionType_2_1::A2DP_HARDWARE_OFFLOAD_DATAPATH) {
+ return BluetoothAudioSession::kInvalidOffloadAudioConfiguration;
+ } else {
+ return BluetoothAudioSession::kInvalidSoftwareAudioConfiguration;
+ }
+ }
+
+ // Those control APIs for the bluetooth_audio module to start / suspend / stop
+ // stream, to check position, and to update metadata.
+ static bool StartStream(const SessionType_2_1& session_type) {
+ std::shared_ptr<BluetoothAudioSession_2_1> session_ptr =
+ BluetoothAudioSessionInstance_2_1::GetSessionInstance(session_type);
+ if (session_ptr != nullptr) {
+ return session_ptr->GetAudioSession()->StartStream();
+ }
+ return false;
+ }
+
+ static bool SuspendStream(const SessionType_2_1& session_type) {
+ std::shared_ptr<BluetoothAudioSession_2_1> session_ptr =
+ BluetoothAudioSessionInstance_2_1::GetSessionInstance(session_type);
+ if (session_ptr != nullptr) {
+ return session_ptr->GetAudioSession()->SuspendStream();
+ }
+ return false;
+ }
+
+ static void StopStream(const SessionType_2_1& session_type) {
+ std::shared_ptr<BluetoothAudioSession_2_1> session_ptr =
+ BluetoothAudioSessionInstance_2_1::GetSessionInstance(session_type);
+ if (session_ptr != nullptr) {
+ session_ptr->GetAudioSession()->StopStream();
+ }
+ }
+
+ static bool GetPresentationPosition(const SessionType_2_1& session_type,
+ uint64_t* remote_delay_report_ns,
+ uint64_t* total_bytes_readed,
+ timespec* data_position) {
+ std::shared_ptr<BluetoothAudioSession_2_1> session_ptr =
+ BluetoothAudioSessionInstance_2_1::GetSessionInstance(session_type);
+ if (session_ptr != nullptr) {
+ return session_ptr->GetAudioSession()->GetPresentationPosition(
+ remote_delay_report_ns, total_bytes_readed, data_position);
+ }
+ return false;
+ }
+
+ static void UpdateTracksMetadata(
+ const SessionType_2_1& session_type,
+ const struct source_metadata* source_metadata) {
+ std::shared_ptr<BluetoothAudioSession_2_1> session_ptr =
+ BluetoothAudioSessionInstance_2_1::GetSessionInstance(session_type);
+ if (session_ptr != nullptr) {
+ session_ptr->GetAudioSession()->UpdateTracksMetadata(source_metadata);
+ }
+ }
+
+ // The control API writes stream to FMQ
+ static size_t OutWritePcmData(const SessionType_2_1& session_type,
+ const void* buffer, size_t bytes) {
+ std::shared_ptr<BluetoothAudioSession_2_1> session_ptr =
+ BluetoothAudioSessionInstance_2_1::GetSessionInstance(session_type);
+ if (session_ptr != nullptr) {
+ return session_ptr->GetAudioSession()->OutWritePcmData(buffer, bytes);
+ }
+ return 0;
+ }
+};
+
+} // namespace audio
+} // namespace bluetooth
+} // namespace android
diff --git a/bluetooth/audio/2.0/default/session/BluetoothAudioSessionReport.h b/bluetooth/audio/utils/session/BluetoothAudioSessionReport.h
similarity index 100%
rename from bluetooth/audio/2.0/default/session/BluetoothAudioSessionReport.h
rename to bluetooth/audio/utils/session/BluetoothAudioSessionReport.h
diff --git a/bluetooth/audio/utils/session/BluetoothAudioSessionReport_2_1.h b/bluetooth/audio/utils/session/BluetoothAudioSessionReport_2_1.h
new file mode 100644
index 0000000..ab30536
--- /dev/null
+++ b/bluetooth/audio/utils/session/BluetoothAudioSessionReport_2_1.h
@@ -0,0 +1,69 @@
+/*
+ * Copyright 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.
+ */
+
+#pragma once
+
+#include "BluetoothAudioSession_2_1.h"
+
+namespace android {
+namespace bluetooth {
+namespace audio {
+
+class BluetoothAudioSessionReport_2_1 {
+ public:
+ // The API reports the Bluetooth stack has started the session, and will
+ // inform registered bluetooth_audio outputs
+ static void OnSessionStarted(
+ const ::android::hardware::bluetooth::audio::V2_1::SessionType&
+ session_type,
+ const sp<IBluetoothAudioPort> host_iface,
+ const DataMQ::Descriptor* dataMQ,
+ const ::android::hardware::bluetooth::audio::V2_1::AudioConfiguration&
+ audio_config) {
+ std::shared_ptr<BluetoothAudioSession_2_1> session_ptr =
+ BluetoothAudioSessionInstance_2_1::GetSessionInstance(session_type);
+ if (session_ptr != nullptr) {
+ session_ptr->OnSessionStarted(host_iface, dataMQ, audio_config);
+ }
+ }
+ // The API reports the Bluetooth stack has ended the session, and will
+ // inform registered bluetooth_audio outputs
+ static void OnSessionEnded(
+ const ::android::hardware::bluetooth::audio::V2_1::SessionType&
+ session_type) {
+ std::shared_ptr<BluetoothAudioSession_2_1> session_ptr =
+ BluetoothAudioSessionInstance_2_1::GetSessionInstance(session_type);
+ if (session_ptr != nullptr) {
+ session_ptr->GetAudioSession()->OnSessionEnded();
+ }
+ }
+ // The API reports the Bluetooth stack has replied the result of startStream
+ // or suspendStream, and will inform registered bluetooth_audio outputs
+ static void ReportControlStatus(
+ const ::android::hardware::bluetooth::audio::V2_1::SessionType&
+ session_type,
+ const bool& start_resp, const BluetoothAudioStatus& status) {
+ std::shared_ptr<BluetoothAudioSession_2_1> session_ptr =
+ BluetoothAudioSessionInstance_2_1::GetSessionInstance(session_type);
+ if (session_ptr != nullptr) {
+ session_ptr->GetAudioSession()->ReportControlStatus(start_resp, status);
+ }
+ }
+};
+
+} // namespace audio
+} // namespace bluetooth
+} // namespace android
diff --git a/bluetooth/audio/utils/session/BluetoothAudioSession_2_1.cpp b/bluetooth/audio/utils/session/BluetoothAudioSession_2_1.cpp
new file mode 100644
index 0000000..9e1baf4
--- /dev/null
+++ b/bluetooth/audio/utils/session/BluetoothAudioSession_2_1.cpp
@@ -0,0 +1,117 @@
+/*
+ * Copyright 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 "BTAudioProviderSession_2_1"
+
+#include "BluetoothAudioSession_2_1.h"
+
+#include <android-base/logging.h>
+#include <android-base/stringprintf.h>
+
+namespace android {
+namespace bluetooth {
+namespace audio {
+using SessionType_2_1 =
+ ::android::hardware::bluetooth::audio::V2_1::SessionType;
+using SessionType_2_0 =
+ ::android::hardware::bluetooth::audio::V2_0::SessionType;
+
+namespace {
+bool is_2_0_session_type(
+ const ::android::hardware::bluetooth::audio::V2_1::SessionType&
+ session_type) {
+ if (session_type == SessionType_2_1::A2DP_SOFTWARE_ENCODING_DATAPATH ||
+ session_type == SessionType_2_1::A2DP_HARDWARE_OFFLOAD_DATAPATH ||
+ session_type == SessionType_2_1::HEARING_AID_SOFTWARE_ENCODING_DATAPATH) {
+ return true;
+ } else {
+ return false;
+ }
+}
+} // namespace
+
+BluetoothAudioSession_2_1::BluetoothAudioSession_2_1(
+ const ::android::hardware::bluetooth::audio::V2_1::SessionType&
+ session_type)
+ : audio_session(BluetoothAudioSessionInstance::GetSessionInstance(
+ static_cast<SessionType_2_0>(session_type))) {
+ if (is_2_0_session_type(session_type)) {
+ session_type_2_1_ = (SessionType_2_1::UNKNOWN);
+ } else {
+ session_type_2_1_ = (session_type);
+ }
+}
+
+std::shared_ptr<BluetoothAudioSession>
+BluetoothAudioSession_2_1::GetAudioSession() {
+ return audio_session;
+}
+
+// The report function is used to report that the Bluetooth stack has started
+// this session without any failure, and will invoke session_changed_cb_ to
+// notify those registered bluetooth_audio outputs
+void BluetoothAudioSession_2_1::OnSessionStarted(
+ const sp<IBluetoothAudioPort> stack_iface, const DataMQ::Descriptor* dataMQ,
+ const ::android::hardware::bluetooth::audio::V2_1::AudioConfiguration&
+ audio_config) {
+ if (session_type_2_1_ == SessionType_2_1::UNKNOWN) {
+ ::android::hardware::bluetooth::audio::V2_0::AudioConfiguration config;
+ if (audio_config.getDiscriminator() ==
+ ::android::hardware::bluetooth::audio::V2_1::AudioConfiguration::
+ hidl_discriminator::codecConfig) {
+ config.codecConfig(audio_config.codecConfig());
+ } else {
+ auto& tmpPcm = audio_config.pcmConfig();
+ config.pcmConfig(
+ ::android::hardware::bluetooth::audio::V2_0::PcmParameters{
+ .sampleRate = static_cast<SampleRate>(tmpPcm.sampleRate),
+ .channelMode = tmpPcm.channelMode,
+ .bitsPerSample = tmpPcm.bitsPerSample
+ /*dataIntervalUs is not passed to 2.0 */
+ });
+ }
+
+ audio_session->OnSessionStarted(stack_iface, dataMQ, config);
+ } else {
+ LOG(FATAL) << " Not implemented yet!!";
+ }
+}
+
+std::unique_ptr<BluetoothAudioSessionInstance_2_1>
+ BluetoothAudioSessionInstance_2_1::instance_ptr =
+ std::unique_ptr<BluetoothAudioSessionInstance_2_1>(
+ new BluetoothAudioSessionInstance_2_1());
+
+// API to fetch the session of A2DP / Hearing Aid
+std::shared_ptr<BluetoothAudioSession_2_1>
+BluetoothAudioSessionInstance_2_1::GetSessionInstance(
+ const SessionType_2_1& session_type) {
+ std::lock_guard<std::mutex> guard(instance_ptr->mutex_);
+ if (!instance_ptr->sessions_map_.empty()) {
+ auto entry = instance_ptr->sessions_map_.find(session_type);
+ if (entry != instance_ptr->sessions_map_.end()) {
+ return entry->second;
+ }
+ }
+ std::shared_ptr<BluetoothAudioSession_2_1> session_ptr =
+ std::make_shared<BluetoothAudioSession_2_1>(session_type);
+ instance_ptr->sessions_map_[session_type] = session_ptr;
+ return session_ptr;
+}
+
+} // namespace audio
+} // namespace bluetooth
+} // namespace android
diff --git a/bluetooth/audio/utils/session/BluetoothAudioSession_2_1.h b/bluetooth/audio/utils/session/BluetoothAudioSession_2_1.h
new file mode 100644
index 0000000..0e9c12b
--- /dev/null
+++ b/bluetooth/audio/utils/session/BluetoothAudioSession_2_1.h
@@ -0,0 +1,82 @@
+/*
+ * Copyright 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.
+ */
+
+#pragma once
+
+#include <android/hardware/bluetooth/audio/2.1/types.h>
+#include "BluetoothAudioSession.h"
+
+#include <mutex>
+#include <unordered_map>
+
+namespace android {
+namespace bluetooth {
+namespace audio {
+
+class BluetoothAudioSession_2_1 {
+ private:
+ std::shared_ptr<BluetoothAudioSession> audio_session;
+
+ ::android::hardware::bluetooth::audio::V2_1::SessionType session_type_2_1_;
+
+ // audio data configuration for both software and offloading
+ ::android::hardware::bluetooth::audio::V2_1::AudioConfiguration
+ audio_config_2_1_;
+
+ bool UpdateAudioConfig(
+ const ::android::hardware::bluetooth::audio::V2_1::AudioConfiguration&
+ audio_config);
+
+ public:
+ BluetoothAudioSession_2_1(
+ const ::android::hardware::bluetooth::audio::V2_1::SessionType&
+ session_type);
+
+ std::shared_ptr<BluetoothAudioSession> GetAudioSession();
+
+ // The report function is used to report that the Bluetooth stack has started
+ // this session without any failure, and will invoke session_changed_cb_ to
+ // notify those registered bluetooth_audio outputs
+ void OnSessionStarted(
+ const sp<IBluetoothAudioPort> stack_iface,
+ const DataMQ::Descriptor* dataMQ,
+ const ::android::hardware::bluetooth::audio::V2_1::AudioConfiguration&
+ audio_config);
+
+ // The control function is for the bluetooth_audio module to get the current
+ // AudioConfiguration
+ const ::android::hardware::bluetooth::audio::V2_1::AudioConfiguration&
+ GetAudioConfig();
+};
+
+class BluetoothAudioSessionInstance_2_1 {
+ public:
+ // The API is to fetch the specified session of A2DP / Hearing Aid
+ static std::shared_ptr<BluetoothAudioSession_2_1> GetSessionInstance(
+ const ::android::hardware::bluetooth::audio::V2_1::SessionType&
+ session_type);
+
+ private:
+ static std::unique_ptr<BluetoothAudioSessionInstance_2_1> instance_ptr;
+ std::mutex mutex_;
+ std::unordered_map<::android::hardware::bluetooth::audio::V2_1::SessionType,
+ std::shared_ptr<BluetoothAudioSession_2_1>>
+ sessions_map_;
+};
+
+} // namespace audio
+} // namespace bluetooth
+} // namespace android
diff --git a/bluetooth/audio/2.0/default/session/BluetoothAudioSupportedCodecsDB.cpp b/bluetooth/audio/utils/session/BluetoothAudioSupportedCodecsDB.cpp
similarity index 100%
rename from bluetooth/audio/2.0/default/session/BluetoothAudioSupportedCodecsDB.cpp
rename to bluetooth/audio/utils/session/BluetoothAudioSupportedCodecsDB.cpp
diff --git a/bluetooth/audio/2.0/default/session/BluetoothAudioSupportedCodecsDB.h b/bluetooth/audio/utils/session/BluetoothAudioSupportedCodecsDB.h
similarity index 100%
rename from bluetooth/audio/2.0/default/session/BluetoothAudioSupportedCodecsDB.h
rename to bluetooth/audio/utils/session/BluetoothAudioSupportedCodecsDB.h
diff --git a/bluetooth/audio/utils/session/BluetoothAudioSupportedCodecsDB_2_1.cpp b/bluetooth/audio/utils/session/BluetoothAudioSupportedCodecsDB_2_1.cpp
new file mode 100644
index 0000000..8b0b0f7
--- /dev/null
+++ b/bluetooth/audio/utils/session/BluetoothAudioSupportedCodecsDB_2_1.cpp
@@ -0,0 +1,127 @@
+/*
+ * Copyright 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 "BTAudioProviderSessionCodecsDB_2_1"
+
+#include "BluetoothAudioSupportedCodecsDB_2_1.h"
+
+#include <android-base/logging.h>
+
+namespace android {
+namespace bluetooth {
+namespace audio {
+
+using ::android::hardware::bluetooth::audio::V2_0::BitsPerSample;
+using ::android::hardware::bluetooth::audio::V2_0::ChannelMode;
+
+using SampleRate_2_0 = ::android::hardware::bluetooth::audio::V2_0::SampleRate;
+using SampleRate_2_1 = ::android::hardware::bluetooth::audio::V2_1::SampleRate;
+
+using SessionType_2_1 =
+ ::android::hardware::bluetooth::audio::V2_1::SessionType;
+using SessionType_2_0 =
+ ::android::hardware::bluetooth::audio::V2_0::SessionType;
+
+namespace {
+bool is_2_0_session_type(
+ const ::android::hardware::bluetooth::audio::V2_1::SessionType&
+ session_type) {
+ if (session_type == SessionType_2_1::A2DP_SOFTWARE_ENCODING_DATAPATH ||
+ session_type == SessionType_2_1::A2DP_HARDWARE_OFFLOAD_DATAPATH ||
+ session_type == SessionType_2_1::HEARING_AID_SOFTWARE_ENCODING_DATAPATH) {
+ return true;
+ } else {
+ return false;
+ }
+}
+} // namespace
+
+static const ::android::hardware::bluetooth::audio::V2_1::PcmParameters
+ kDefaultSoftwarePcmCapabilities_2_1 = {
+ .sampleRate = static_cast<SampleRate_2_1>(
+ SampleRate_2_1::RATE_44100 | SampleRate_2_1::RATE_48000 |
+ SampleRate_2_1::RATE_88200 | SampleRate_2_1::RATE_96000 |
+ SampleRate_2_1::RATE_16000 | SampleRate_2_1::RATE_24000),
+ .channelMode =
+ static_cast<ChannelMode>(ChannelMode::MONO | ChannelMode::STEREO),
+ .bitsPerSample = static_cast<BitsPerSample>(BitsPerSample::BITS_16 |
+ BitsPerSample::BITS_24 |
+ BitsPerSample::BITS_32)};
+
+std::vector<::android::hardware::bluetooth::audio::V2_1::PcmParameters>
+GetSoftwarePcmCapabilities_2_1() {
+ return std::vector<
+ ::android::hardware::bluetooth::audio::V2_1::PcmParameters>(
+ 1, kDefaultSoftwarePcmCapabilities_2_1);
+}
+
+std::vector<CodecCapabilities> GetOffloadCodecCapabilities(
+ const ::android::hardware::bluetooth::audio::V2_1::SessionType&
+ session_type) {
+ if (is_2_0_session_type(session_type)) {
+ return GetOffloadCodecCapabilities(
+ static_cast<SessionType_2_0>(session_type));
+ }
+ return std::vector<CodecCapabilities>(0);
+}
+
+bool IsSoftwarePcmConfigurationValid_2_1(
+ const ::android::hardware::bluetooth::audio::V2_1::PcmParameters&
+ pcm_config) {
+ if ((pcm_config.sampleRate != SampleRate_2_1::RATE_44100 &&
+ pcm_config.sampleRate != SampleRate_2_1::RATE_48000 &&
+ pcm_config.sampleRate != SampleRate_2_1::RATE_88200 &&
+ pcm_config.sampleRate != SampleRate_2_1::RATE_96000 &&
+ pcm_config.sampleRate != SampleRate_2_1::RATE_16000 &&
+ pcm_config.sampleRate != SampleRate_2_1::RATE_24000) ||
+ (pcm_config.bitsPerSample != BitsPerSample::BITS_16 &&
+ pcm_config.bitsPerSample != BitsPerSample::BITS_24 &&
+ pcm_config.bitsPerSample != BitsPerSample::BITS_32) ||
+ (pcm_config.channelMode != ChannelMode::MONO &&
+ pcm_config.channelMode != ChannelMode::STEREO)) {
+ LOG(WARNING) << __func__
+ << ": Invalid PCM Configuration=" << toString(pcm_config);
+ return false;
+ } else if (pcm_config.sampleRate &
+ kDefaultSoftwarePcmCapabilities_2_1.sampleRate &&
+ pcm_config.bitsPerSample &
+ kDefaultSoftwarePcmCapabilities_2_1.bitsPerSample &&
+ pcm_config.channelMode &
+ kDefaultSoftwarePcmCapabilities_2_1.channelMode &&
+ pcm_config.dataIntervalUs != 0) {
+ return true;
+ }
+ LOG(WARNING) << __func__
+ << ": Unsupported PCM Configuration=" << toString(pcm_config);
+ return false;
+}
+
+bool IsOffloadCodecConfigurationValid(
+ const ::android::hardware::bluetooth::audio::V2_1::SessionType&
+ session_type,
+ const ::android::hardware::bluetooth::audio::V2_0::CodecConfiguration&
+ codec_config) {
+ if (is_2_0_session_type(session_type)) {
+ return IsOffloadCodecConfigurationValid(
+ static_cast<SessionType_2_0>(session_type), codec_config);
+ }
+
+ return false;
+}
+
+} // namespace audio
+} // namespace bluetooth
+} // namespace android
diff --git a/bluetooth/audio/utils/session/BluetoothAudioSupportedCodecsDB_2_1.h b/bluetooth/audio/utils/session/BluetoothAudioSupportedCodecsDB_2_1.h
new file mode 100644
index 0000000..746d9c0
--- /dev/null
+++ b/bluetooth/audio/utils/session/BluetoothAudioSupportedCodecsDB_2_1.h
@@ -0,0 +1,46 @@
+/*
+ * Copyright 2020 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.
+ */
+
+#pragma once
+
+#include "BluetoothAudioSupportedCodecsDB.h"
+
+#include <android/hardware/bluetooth/audio/2.1/types.h>
+
+namespace android {
+namespace bluetooth {
+namespace audio {
+
+std::vector<::android::hardware::bluetooth::audio::V2_1::PcmParameters>
+GetSoftwarePcmCapabilities_2_1();
+std::vector<::android::hardware::bluetooth::audio::V2_0::CodecCapabilities>
+GetOffloadCodecCapabilities(
+ const ::android::hardware::bluetooth::audio::V2_1::SessionType&
+ session_type);
+
+bool IsSoftwarePcmConfigurationValid_2_1(
+ const ::android::hardware::bluetooth::audio::V2_1::PcmParameters&
+ pcm_config);
+
+bool IsOffloadCodecConfigurationValid(
+ const ::android::hardware::bluetooth::audio::V2_1::SessionType&
+ session_type,
+ const ::android::hardware::bluetooth::audio::V2_0::CodecConfiguration&
+ codec_config);
+
+} // namespace audio
+} // namespace bluetooth
+} // namespace android
diff --git a/camera/provider/2.4/vts/functional/VtsHalCameraProviderV2_4TargetTest.cpp b/camera/provider/2.4/vts/functional/VtsHalCameraProviderV2_4TargetTest.cpp
index 2099dc0..7e6b5ad 100644
--- a/camera/provider/2.4/vts/functional/VtsHalCameraProviderV2_4TargetTest.cpp
+++ b/camera/provider/2.4/vts/functional/VtsHalCameraProviderV2_4TargetTest.cpp
@@ -888,6 +888,8 @@
static Status getSystemCameraKind(const camera_metadata_t* staticMeta,
SystemCameraKind* systemCameraKind);
+ static V3_2::DataspaceFlags getDataspace(PixelFormat format);
+
void processCaptureRequestInternal(uint64_t bufferusage, RequestTemplate reqTemplate,
bool useSecureOnlyCameras);
@@ -3178,7 +3180,6 @@
}
}
-
// Verify that all supported stream formats and sizes can be configured
// successfully.
TEST_P(CameraHidlTest, configureStreamsAvailableOutputs) {
@@ -3221,17 +3222,7 @@
uint32_t streamConfigCounter = 0;
for (auto& it : outputStreams) {
V3_2::Stream stream3_2;
- V3_2::DataspaceFlags dataspaceFlag = 0;
- switch (static_cast<PixelFormat>(it.format)) {
- case PixelFormat::BLOB:
- dataspaceFlag = static_cast<V3_2::DataspaceFlags>(Dataspace::V0_JFIF);
- break;
- case PixelFormat::Y16:
- dataspaceFlag = static_cast<V3_2::DataspaceFlags>(Dataspace::DEPTH);
- break;
- default:
- dataspaceFlag = static_cast<V3_2::DataspaceFlags>(Dataspace::UNKNOWN);
- }
+ V3_2::DataspaceFlags dataspaceFlag = getDataspace(static_cast<PixelFormat>(it.format));
stream3_2 = {streamId,
StreamType::OUTPUT,
static_cast<uint32_t>(it.width),
@@ -3351,17 +3342,8 @@
size_t j = 0;
for (const auto& it : outputStreams) {
V3_2::Stream stream3_2;
- V3_2::DataspaceFlags dataspaceFlag = 0;
- switch (static_cast<PixelFormat>(it.format)) {
- case PixelFormat::BLOB:
- dataspaceFlag = static_cast<V3_2::DataspaceFlags>(Dataspace::V0_JFIF);
- break;
- case PixelFormat::Y16:
- dataspaceFlag = static_cast<V3_2::DataspaceFlags>(Dataspace::DEPTH);
- break;
- default:
- dataspaceFlag = static_cast<V3_2::DataspaceFlags>(Dataspace::UNKNOWN);
- }
+ V3_2::DataspaceFlags dataspaceFlag = getDataspace(
+ static_cast<PixelFormat>(it.format));
stream3_2 = {streamId++,
StreamType::OUTPUT,
static_cast<uint32_t>(it.width),
@@ -5901,6 +5883,23 @@
return ret;
}
+// Select an appropriate dataspace given a specific pixel format.
+V3_2::DataspaceFlags CameraHidlTest::getDataspace(PixelFormat format) {
+ switch (format) {
+ case PixelFormat::BLOB:
+ return static_cast<V3_2::DataspaceFlags>(Dataspace::V0_JFIF);
+ case PixelFormat::Y16:
+ return static_cast<V3_2::DataspaceFlags>(Dataspace::DEPTH);
+ case PixelFormat::RAW16:
+ case PixelFormat::RAW_OPAQUE:
+ case PixelFormat::RAW10:
+ case PixelFormat::RAW12:
+ return static_cast<V3_2::DataspaceFlags>(Dataspace::ARBITRARY);
+ default:
+ return static_cast<V3_2::DataspaceFlags>(Dataspace::UNKNOWN);
+ }
+}
+
// Check whether this is a monochrome camera using the static camera characteristics.
Status CameraHidlTest::isMonochromeCamera(const camera_metadata_t *staticMeta) {
Status ret = Status::METHOD_NOT_SUPPORTED;
@@ -6275,17 +6274,8 @@
ASSERT_EQ(Status::OK, rc);
ASSERT_FALSE(outputStreams.empty());
- V3_2::DataspaceFlags dataspaceFlag = 0;
- switch (static_cast<PixelFormat>(outputStreams[idx].format)) {
- case PixelFormat::BLOB:
- dataspaceFlag = static_cast<V3_2::DataspaceFlags>(Dataspace::V0_JFIF);
- break;
- case PixelFormat::Y16:
- dataspaceFlag = static_cast<V3_2::DataspaceFlags>(Dataspace::DEPTH);
- break;
- default:
- dataspaceFlag = static_cast<V3_2::DataspaceFlags>(Dataspace::UNKNOWN);
- }
+ V3_2::DataspaceFlags dataspaceFlag = getDataspace(
+ static_cast<PixelFormat>(outputStreams[idx].format));
::android::hardware::hidl_vec<V3_4::Stream> streams3_4(/*size*/1);
V3_4::Stream stream3_4 = {{ 0 /*streamId*/, StreamType::OUTPUT,
diff --git a/compatibility_matrices/compatibility_matrix.3.xml b/compatibility_matrices/compatibility_matrix.3.xml
index 5888ab9..608890b 100644
--- a/compatibility_matrices/compatibility_matrix.3.xml
+++ b/compatibility_matrices/compatibility_matrix.3.xml
@@ -310,6 +310,10 @@
<instance>slot2</instance>
<instance>slot3</instance>
</interface>
+ </hal>
+ <hal format="hidl" optional="true">
+ <name>android.hardware.radio</name>
+ <version>1.0-2</version>
<interface>
<name>ISap</name>
<instance>slot1</instance>
diff --git a/compatibility_matrices/compatibility_matrix.4.xml b/compatibility_matrices/compatibility_matrix.4.xml
index 5e1266e..e5e012c 100644
--- a/compatibility_matrices/compatibility_matrix.4.xml
+++ b/compatibility_matrices/compatibility_matrix.4.xml
@@ -432,7 +432,6 @@
<hal format="hidl" optional="true">
<name>android.hardware.tv.cec</name>
<version>1.0</version>
- <version>2.0</version>
<interface>
<name>IHdmiCec</name>
<instance>default</instance>
diff --git a/compatibility_matrices/compatibility_matrix.5.xml b/compatibility_matrices/compatibility_matrix.5.xml
index 8bc663d..e772b6f 100644
--- a/compatibility_matrices/compatibility_matrix.5.xml
+++ b/compatibility_matrices/compatibility_matrix.5.xml
@@ -467,7 +467,6 @@
<hal format="hidl" optional="true">
<name>android.hardware.tv.cec</name>
<version>1.0</version>
- <version>2.0</version>
<interface>
<name>IHdmiCec</name>
<instance>default</instance>
diff --git a/compatibility_matrices/compatibility_matrix.current.xml b/compatibility_matrices/compatibility_matrix.current.xml
index fa0a70e..f3edb98 100644
--- a/compatibility_matrices/compatibility_matrix.current.xml
+++ b/compatibility_matrices/compatibility_matrix.current.xml
@@ -9,7 +9,6 @@
</hal>
<hal format="hidl" optional="false">
<name>android.hardware.audio</name>
- <!-- TODO(b/142480271): remove 6.0 when implemented on reference device. -->
<version>6.0</version>
<version>7.0</version>
<interface>
@@ -19,7 +18,6 @@
</hal>
<hal format="hidl" optional="false">
<name>android.hardware.audio.effect</name>
- <!-- TODO(b/142480271): remove 6.0 when implemented on reference device. -->
<version>6.0</version>
<version>7.0</version>
<interface>
@@ -75,6 +73,7 @@
</hal>
<hal format="aidl" optional="true">
<name>android.hardware.automotive.occupant_awareness</name>
+ <version>1</version>
<interface>
<name>IOccupantAwareness</name>
<instance>default</instance>
@@ -317,6 +316,7 @@
</hal>
<hal format="aidl" optional="true">
<name>android.hardware.security.keymint</name>
+ <version>1</version>
<interface>
<name>IKeyMintDevice</name>
<instance>default</instance>
@@ -325,6 +325,7 @@
</hal>
<hal format="aidl" optional="true">
<name>android.hardware.light</name>
+ <version>1</version>
<interface>
<name>ILights</name>
<instance>default</instance>
@@ -353,6 +354,7 @@
</hal>
<hal format="aidl" optional="true">
<name>android.hardware.memtrack</name>
+ <version>1</version>
<interface>
<name>IMemtrack</name>
<instance>default</instance>
@@ -374,6 +376,13 @@
<regex-instance>.*</regex-instance>
</interface>
</hal>
+ <hal format="aidl" optional="true">
+ <name>android.hardware.neuralnetworks</name>
+ <interface>
+ <name>IDevice</name>
+ <regex-instance>.*</regex-instance>
+ </interface>
+ </hal>
<hal format="hidl" optional="true">
<name>android.hardware.nfc</name>
<version>1.2</version>
@@ -392,14 +401,14 @@
</hal>
<hal format="aidl" optional="false">
<name>android.hardware.power</name>
+ <version>1</version>
<interface>
<name>IPower</name>
<instance>default</instance>
</interface>
</hal>
- <hal format="hidl" optional="true">
+ <hal format="aidl" optional="true">
<name>android.hardware.power.stats</name>
- <version>1.0</version>
<interface>
<name>IPowerStats</name>
<instance>default</instance>
@@ -452,6 +461,7 @@
</hal>
<hal format="aidl" optional="true">
<name>android.hardware.rebootescrow</name>
+ <version>1</version>
<interface>
<name>IRebootEscrow</name>
<instance>default</instance>
@@ -468,6 +478,7 @@
</hal>
<hal format="aidl" optional="true">
<name>android.hardware.security.secureclock</name>
+ <version>1</version>
<interface>
<name>ISecureClock</name>
<instance>default</instance>
@@ -475,6 +486,7 @@
</hal>
<hal format="aidl" optional="true">
<name>android.hardware.security.sharedsecret</name>
+ <version>1</version>
<interface>
<name>ISharedSecret</name>
<instance>default</instance>
@@ -524,7 +536,6 @@
<hal format="hidl" optional="true">
<name>android.hardware.tv.cec</name>
<version>1.0</version>
- <version>2.0</version>
<interface>
<name>IHdmiCec</name>
<instance>default</instance>
diff --git a/compatibility_matrices/exclude/fcm_exclude.cpp b/compatibility_matrices/exclude/fcm_exclude.cpp
index 849df94..6a4d251 100644
--- a/compatibility_matrices/exclude/fcm_exclude.cpp
+++ b/compatibility_matrices/exclude/fcm_exclude.cpp
@@ -66,7 +66,7 @@
// TODO(b/171260360) Remove when HAL definition is removed
"android.hardware.audio.effect@2.0",
"android.hardware.audio@2.0",
- // TODO(b/171260613) Remove when HAL definition is removed
+ // Health 1.0 HAL is deprecated. The top level interface are deleted.
"android.hardware.health@1.0",
// TODO(b/171260670) Remove when HAL definition is removed
"android.hardware.nfc@1.0",
diff --git a/graphics/allocator/2.0/default/OWNERS b/graphics/allocator/2.0/default/OWNERS
index 2a56b38..c9f24d0 100644
--- a/graphics/allocator/2.0/default/OWNERS
+++ b/graphics/allocator/2.0/default/OWNERS
@@ -1,4 +1,4 @@
# Graphics team
chrisforbes@google.com
-stoza@google.com
-vhau@google.com
+jreck@google.com
+lpy@google.com
diff --git a/graphics/allocator/2.0/utils/OWNERS b/graphics/allocator/2.0/utils/OWNERS
index 2a56b38..c9f24d0 100644
--- a/graphics/allocator/2.0/utils/OWNERS
+++ b/graphics/allocator/2.0/utils/OWNERS
@@ -1,4 +1,4 @@
# Graphics team
chrisforbes@google.com
-stoza@google.com
-vhau@google.com
+jreck@google.com
+lpy@google.com
diff --git a/graphics/composer/2.1/default/OWNERS b/graphics/composer/2.1/default/OWNERS
index db8fb80..709c4d1 100644
--- a/graphics/composer/2.1/default/OWNERS
+++ b/graphics/composer/2.1/default/OWNERS
@@ -1,6 +1,3 @@
# Graphics team
-courtneygo@google.com
-jessehall@google.com
+adyabr@google.com
lpy@google.com
-stoza@google.com
-vhau@google.com
diff --git a/graphics/composer/2.1/utils/OWNERS b/graphics/composer/2.1/utils/OWNERS
index 5acc631..7af69b4 100644
--- a/graphics/composer/2.1/utils/OWNERS
+++ b/graphics/composer/2.1/utils/OWNERS
@@ -1,5 +1,2 @@
-courtneygo@google.com
-jessehall@google.com
+adyabr@google.com
lpy@google.com
-stoza@google.com
-vhau@google.com
diff --git a/graphics/composer/2.2/default/OWNERS b/graphics/composer/2.2/default/OWNERS
index db8fb80..709c4d1 100644
--- a/graphics/composer/2.2/default/OWNERS
+++ b/graphics/composer/2.2/default/OWNERS
@@ -1,6 +1,3 @@
# Graphics team
-courtneygo@google.com
-jessehall@google.com
+adyabr@google.com
lpy@google.com
-stoza@google.com
-vhau@google.com
diff --git a/graphics/composer/2.2/utils/OWNERS b/graphics/composer/2.2/utils/OWNERS
index 3f1e82c..709c4d1 100644
--- a/graphics/composer/2.2/utils/OWNERS
+++ b/graphics/composer/2.2/utils/OWNERS
@@ -1,5 +1,3 @@
# Graphics team
-courtneygo@google.com
+adyabr@google.com
lpy@google.com
-stoza@google.com
-vhau@google.com
diff --git a/graphics/composer/2.2/vts/functional/OWNERS b/graphics/composer/2.2/vts/functional/OWNERS
index a17a50c..ea06752 100644
--- a/graphics/composer/2.2/vts/functional/OWNERS
+++ b/graphics/composer/2.2/vts/functional/OWNERS
@@ -1,8 +1,6 @@
# Graphics team
-courtneygo@google.com
+adyabr@google.com
lpy@google.com
-stoza@google.com
-vhau@google.com
# VTS team
yim@google.com
diff --git a/graphics/composer/2.3/default/OWNERS b/graphics/composer/2.3/default/OWNERS
index 820ebe6..709c4d1 100644
--- a/graphics/composer/2.3/default/OWNERS
+++ b/graphics/composer/2.3/default/OWNERS
@@ -1,5 +1,3 @@
# Graphics team
-jessehall@google.com
+adyabr@google.com
lpy@google.com
-stoza@google.com
-vhau@google.com
diff --git a/graphics/composer/2.3/utils/OWNERS b/graphics/composer/2.3/utils/OWNERS
index cc6d937..709c4d1 100644
--- a/graphics/composer/2.3/utils/OWNERS
+++ b/graphics/composer/2.3/utils/OWNERS
@@ -1,4 +1,3 @@
# Graphics team
+adyabr@google.com
lpy@google.com
-stoza@google.com
-vhau@google.com
diff --git a/graphics/composer/2.3/vts/functional/OWNERS b/graphics/composer/2.3/vts/functional/OWNERS
index b3ea6be..ea06752 100644
--- a/graphics/composer/2.3/vts/functional/OWNERS
+++ b/graphics/composer/2.3/vts/functional/OWNERS
@@ -1,7 +1,6 @@
# Graphics team
+adyabr@google.com
lpy@google.com
-stoza@google.com
-vhau@google.com
# VTS team
yim@google.com
diff --git a/graphics/composer/2.4/default/OWNERS b/graphics/composer/2.4/default/OWNERS
index cc6d937..709c4d1 100644
--- a/graphics/composer/2.4/default/OWNERS
+++ b/graphics/composer/2.4/default/OWNERS
@@ -1,4 +1,3 @@
# Graphics team
+adyabr@google.com
lpy@google.com
-stoza@google.com
-vhau@google.com
diff --git a/graphics/composer/2.4/utils/OWNERS b/graphics/composer/2.4/utils/OWNERS
index cc6d937..709c4d1 100644
--- a/graphics/composer/2.4/utils/OWNERS
+++ b/graphics/composer/2.4/utils/OWNERS
@@ -1,4 +1,3 @@
# Graphics team
+adyabr@google.com
lpy@google.com
-stoza@google.com
-vhau@google.com
diff --git a/graphics/composer/2.4/vts/functional/OWNERS b/graphics/composer/2.4/vts/functional/OWNERS
index b3ea6be..ea06752 100644
--- a/graphics/composer/2.4/vts/functional/OWNERS
+++ b/graphics/composer/2.4/vts/functional/OWNERS
@@ -1,7 +1,6 @@
# Graphics team
+adyabr@google.com
lpy@google.com
-stoza@google.com
-vhau@google.com
# VTS team
yim@google.com
diff --git a/graphics/mapper/2.0/default/OWNERS b/graphics/mapper/2.0/default/OWNERS
index 2a56b38..c9f24d0 100644
--- a/graphics/mapper/2.0/default/OWNERS
+++ b/graphics/mapper/2.0/default/OWNERS
@@ -1,4 +1,4 @@
# Graphics team
chrisforbes@google.com
-stoza@google.com
-vhau@google.com
+jreck@google.com
+lpy@google.com
diff --git a/graphics/mapper/2.0/utils/OWNERS b/graphics/mapper/2.0/utils/OWNERS
index 2a56b38..c9f24d0 100644
--- a/graphics/mapper/2.0/utils/OWNERS
+++ b/graphics/mapper/2.0/utils/OWNERS
@@ -1,4 +1,4 @@
# Graphics team
chrisforbes@google.com
-stoza@google.com
-vhau@google.com
+jreck@google.com
+lpy@google.com
diff --git a/graphics/mapper/2.0/vts/OWNERS b/graphics/mapper/2.0/vts/OWNERS
index 11b7d21..4177296 100644
--- a/graphics/mapper/2.0/vts/OWNERS
+++ b/graphics/mapper/2.0/vts/OWNERS
@@ -1,7 +1,7 @@
# Graphics team
chrisforbes@google.com
-stoza@google.com
-vhau@google.com
+jreck@google.com
+lpy@google.com
# VTS team
yim@google.com
diff --git a/graphics/mapper/2.1/default/OWNERS b/graphics/mapper/2.1/default/OWNERS
index 2a56b38..c9f24d0 100644
--- a/graphics/mapper/2.1/default/OWNERS
+++ b/graphics/mapper/2.1/default/OWNERS
@@ -1,4 +1,4 @@
# Graphics team
chrisforbes@google.com
-stoza@google.com
-vhau@google.com
+jreck@google.com
+lpy@google.com
diff --git a/graphics/mapper/2.1/utils/OWNERS b/graphics/mapper/2.1/utils/OWNERS
index 2a56b38..c9f24d0 100644
--- a/graphics/mapper/2.1/utils/OWNERS
+++ b/graphics/mapper/2.1/utils/OWNERS
@@ -1,4 +1,4 @@
# Graphics team
chrisforbes@google.com
-stoza@google.com
-vhau@google.com
+jreck@google.com
+lpy@google.com
diff --git a/graphics/mapper/2.1/vts/OWNERS b/graphics/mapper/2.1/vts/OWNERS
index 11b7d21..4177296 100644
--- a/graphics/mapper/2.1/vts/OWNERS
+++ b/graphics/mapper/2.1/vts/OWNERS
@@ -1,7 +1,7 @@
# Graphics team
chrisforbes@google.com
-stoza@google.com
-vhau@google.com
+jreck@google.com
+lpy@google.com
# VTS team
yim@google.com
diff --git a/graphics/mapper/3.0/utils/OWNERS b/graphics/mapper/3.0/utils/OWNERS
index 2a56b38..c9f24d0 100644
--- a/graphics/mapper/3.0/utils/OWNERS
+++ b/graphics/mapper/3.0/utils/OWNERS
@@ -1,4 +1,4 @@
# Graphics team
chrisforbes@google.com
-stoza@google.com
-vhau@google.com
+jreck@google.com
+lpy@google.com
diff --git a/graphics/mapper/3.0/vts/OWNERS b/graphics/mapper/3.0/vts/OWNERS
index 2a56b38..c9f24d0 100644
--- a/graphics/mapper/3.0/vts/OWNERS
+++ b/graphics/mapper/3.0/vts/OWNERS
@@ -1,4 +1,4 @@
# Graphics team
chrisforbes@google.com
-stoza@google.com
-vhau@google.com
+jreck@google.com
+lpy@google.com
diff --git a/graphics/mapper/4.0/utils/OWNERS b/graphics/mapper/4.0/utils/OWNERS
index 2a56b38..c9f24d0 100644
--- a/graphics/mapper/4.0/utils/OWNERS
+++ b/graphics/mapper/4.0/utils/OWNERS
@@ -1,4 +1,4 @@
# Graphics team
chrisforbes@google.com
-stoza@google.com
-vhau@google.com
+jreck@google.com
+lpy@google.com
diff --git a/graphics/mapper/4.0/vts/OWNERS b/graphics/mapper/4.0/vts/OWNERS
index 2a56b38..c9f24d0 100644
--- a/graphics/mapper/4.0/vts/OWNERS
+++ b/graphics/mapper/4.0/vts/OWNERS
@@ -1,4 +1,4 @@
# Graphics team
chrisforbes@google.com
-stoza@google.com
-vhau@google.com
+jreck@google.com
+lpy@google.com
diff --git a/health/storage/impl_common/impl_common.cpp b/health/storage/impl_common/impl_common.cpp
index 6e753d4..1a7a7f1 100644
--- a/health/storage/impl_common/impl_common.cpp
+++ b/health/storage/impl_common/impl_common.cpp
@@ -45,32 +45,59 @@
return "";
}
-Result GarbageCollect(uint64_t timeout_seconds) {
- std::string path = GetGarbageCollectPath();
+static std::string GetWriteBoosterPath() {
+ Fstab fstab;
+ ReadDefaultFstab(&fstab);
- if (path.empty()) {
+ for (const auto& entry : fstab) {
+ if (!entry.sysfs_path.empty()) {
+ return entry.sysfs_path + "/attributes/wb_avail_buf";
+ }
+ }
+
+ return "";
+}
+
+Result GarbageCollect(uint64_t timeout_seconds) {
+ std::string gc_path = GetGarbageCollectPath();
+
+ if (gc_path.empty()) {
LOG(WARNING) << "Cannot find Dev GC path";
return Result::UNKNOWN_ERROR;
}
Result result = Result::SUCCESS;
Timer timer;
- LOG(INFO) << "Start Dev GC on " << path;
+ LOG(INFO) << "Start Dev GC on " << gc_path;
while (1) {
- std::string require;
- if (!ReadFileToString(path, &require)) {
- PLOG(WARNING) << "Reading manual_gc failed in " << path;
+ std::string require_gc;
+ if (!ReadFileToString(gc_path, &require_gc)) {
+ PLOG(WARNING) << "Reading manual_gc failed in " << gc_path;
result = Result::IO_ERROR;
break;
}
- require = Trim(require);
- if (require == "" || require == "off" || require == "disabled") {
+ require_gc = Trim(require_gc);
+
+ std::string wb_path = GetWriteBoosterPath();
+ // Let's flush WB till 100% available
+ std::string wb_avail = "0x0000000A";
+ if (!wb_path.empty() && !ReadFileToString(wb_path, &wb_avail)) {
+ PLOG(WARNING) << "Reading wb_avail_buf failed in " << wb_path;
+ }
+ wb_avail = Trim(wb_avail);
+
+ if (require_gc == "disabled") {
+ LOG(DEBUG) << "Disabled Dev GC";
+ break;
+ }
+ if ((require_gc == "" || require_gc == "off") && wb_avail == "0x0000000A") {
LOG(DEBUG) << "No more to do Dev GC";
break;
}
- LOG(DEBUG) << "Trigger Dev GC on " << path;
- if (!WriteStringToFile("1", path)) {
- PLOG(WARNING) << "Start Dev GC failed on " << path;
+ LOG(DEBUG) << "Trigger Dev GC on " << gc_path << " having " << require_gc << ", WB on "
+ << wb_path << " having " << wb_avail;
+ if (!WriteStringToFile("1", gc_path)) {
+ PLOG(WARNING) << "Start Dev GC failed on " << gc_path;
result = Result::IO_ERROR;
break;
}
@@ -81,9 +108,9 @@
}
sleep(2);
}
- LOG(INFO) << "Stop Dev GC on " << path;
- if (!WriteStringToFile("0", path)) {
- PLOG(WARNING) << "Stop Dev GC failed on " << path;
+ LOG(INFO) << "Stop Dev GC on " << gc_path;
+ if (!WriteStringToFile("0", gc_path)) {
+ PLOG(WARNING) << "Stop Dev GC failed on " << gc_path;
result = Result::IO_ERROR;
}
@@ -97,17 +124,26 @@
if (path.empty()) {
output << "Cannot find Dev GC path";
} else {
- std::string require;
+ std::string require_gc;
- if (ReadFileToString(path, &require)) {
- output << path << ":" << require << std::endl;
+ if (ReadFileToString(path, &require_gc)) {
+ output << path << ":" << require_gc << std::endl;
}
if (WriteStringToFile("0", path)) {
output << "stop success" << std::endl;
}
}
+ std::string wb_path = GetWriteBoosterPath();
+ if (wb_path.empty()) {
+ output << "Cannot find Dev WriteBooster path";
+ } else {
+ std::string wb_available;
+ if (ReadFileToString(wb_path, &wb_available)) {
+ output << wb_path << ":" << wb_available << std::endl;
+ }
+ }
if (!WriteStringToFd(output.str(), fd)) {
PLOG(WARNING) << "debug: cannot write to fd";
}
diff --git a/media/omx/1.0/vts/functional/common/media_hidl_test_common.cpp b/media/omx/1.0/vts/functional/common/media_hidl_test_common.cpp
index 9184c56..ea29f03 100644
--- a/media/omx/1.0/vts/functional/common/media_hidl_test_common.cpp
+++ b/media/omx/1.0/vts/functional/common/media_hidl_test_common.cpp
@@ -215,6 +215,7 @@
ASSERT_NE(handle, nullptr);
*nStride = static_cast<int32_t>(stride);
+ buffer->handle = handle;
buffer->omxBuffer.nativeHandle = handle;
buffer->omxBuffer.attr.anwBuffer.width = nFrameWidth;
buffer->omxBuffer.attr.anwBuffer.height = nFrameHeight;
@@ -335,6 +336,18 @@
}
}
+// free buffers needed on a component port
+void freePortBuffers(android::Vector<BufferInfo>* buffArray, PortMode portMode, bool allocGrap) {
+ for (size_t i = 0; i < buffArray->size(); i++) {
+ if (portMode == PortMode::PRESET_ANW_BUFFER ||
+ (allocGrap && portMode == PortMode::DYNAMIC_ANW_BUFFER)) {
+ android::GraphicBufferAllocator& allocator = android::GraphicBufferAllocator::get();
+ android::status_t error = allocator.free((*buffArray)[i].handle);
+ ASSERT_EQ(error, android::NO_ERROR);
+ }
+ }
+}
+
// State Transition : Loaded -> Idle
// Note: This function does not make any background checks for this transition.
// The callee holds the reponsibility to ensure the legality of the transition.
@@ -399,11 +412,15 @@
// The callee holds the reponsibility to ensure the legality of the transition.
void changeStateIdletoLoaded(sp<IOmxNode> omxNode, sp<CodecObserver> observer,
android::Vector<BufferInfo>* iBuffer,
- android::Vector<BufferInfo>* oBuffer,
- OMX_U32 kPortIndexInput,
- OMX_U32 kPortIndexOutput) {
+ android::Vector<BufferInfo>* oBuffer, OMX_U32 kPortIndexInput,
+ OMX_U32 kPortIndexOutput, PortMode* portMode, bool allocGrap) {
android::hardware::media::omx::V1_0::Status status;
Message msg;
+ PortMode defaultPortMode[2], *pm;
+
+ defaultPortMode[0] = PortMode::PRESET_BYTE_BUFFER;
+ defaultPortMode[1] = PortMode::PRESET_BYTE_BUFFER;
+ pm = portMode ? portMode : defaultPortMode;
// set state to Loaded
status = omxNode->sendCommand(toRawCommandType(OMX_CommandStateSet),
@@ -446,6 +463,8 @@
ASSERT_EQ(msg.data.eventData.data1, OMX_CommandStateSet);
ASSERT_EQ(msg.data.eventData.data2, OMX_StateLoaded);
+ ASSERT_NO_FATAL_FAILURE(freePortBuffers(iBuffer, pm[0], allocGrap));
+ ASSERT_NO_FATAL_FAILURE(freePortBuffers(oBuffer, pm[1], allocGrap));
return;
}
diff --git a/media/omx/1.0/vts/functional/common/media_hidl_test_common.h b/media/omx/1.0/vts/functional/common/media_hidl_test_common.h
index b16c772..eddf83f 100644
--- a/media/omx/1.0/vts/functional/common/media_hidl_test_common.h
+++ b/media/omx/1.0/vts/functional/common/media_hidl_test_common.h
@@ -115,6 +115,7 @@
struct BufferInfo {
uint32_t id;
bufferOwner owner;
+ buffer_handle_t handle;
android::hardware::media::omx::V1_0::CodecBuffer omxBuffer;
::android::sp<IMemory> mMemory;
int32_t slot;
@@ -329,6 +330,9 @@
PortMode portMode = PortMode::PRESET_BYTE_BUFFER,
bool allocGrap = false);
+void freePortBuffers(android::Vector<BufferInfo>* buffArray, PortMode portMode,
+ bool allocGrap = false);
+
void changeStateLoadedtoIdle(sp<IOmxNode> omxNode, sp<CodecObserver> observer,
android::Vector<BufferInfo>* iBuffer,
android::Vector<BufferInfo>* oBuffer,
@@ -338,8 +342,9 @@
void changeStateIdletoLoaded(sp<IOmxNode> omxNode, sp<CodecObserver> observer,
android::Vector<BufferInfo>* iBuffer,
- android::Vector<BufferInfo>* oBuffer,
- OMX_U32 kPortIndexInput, OMX_U32 kPortIndexOutput);
+ android::Vector<BufferInfo>* oBuffer, OMX_U32 kPortIndexInput,
+ OMX_U32 kPortIndexOutput, PortMode* portMode = nullptr,
+ bool allocGrap = false);
void changeStateIdletoExecute(sp<IOmxNode> omxNode, sp<CodecObserver> observer);
diff --git a/media/omx/1.0/vts/functional/video/VtsHalMediaOmxV1_0TargetVideoDecTest.cpp b/media/omx/1.0/vts/functional/video/VtsHalMediaOmxV1_0TargetVideoDecTest.cpp
index 67b9895..d35ce65 100644
--- a/media/omx/1.0/vts/functional/video/VtsHalMediaOmxV1_0TargetVideoDecTest.cpp
+++ b/media/omx/1.0/vts/functional/video/VtsHalMediaOmxV1_0TargetVideoDecTest.cpp
@@ -451,6 +451,7 @@
status,
android::hardware::media::omx::V1_0::Status::TIMED_OUT);
+ ASSERT_NO_FATAL_FAILURE(freePortBuffers(oBuffer, oPortMode, true));
ASSERT_NO_FATAL_FAILURE(allocatePortBuffers(
omxNode, oBuffer, kPortIndexOutput, oPortMode, true));
status = observer->dequeueMessage(&msg, DEFAULT_TIMEOUT,
@@ -853,9 +854,9 @@
ASSERT_NO_FATAL_FAILURE(
changeStateExecutetoIdle(omxNode, observer, &iBuffer, &oBuffer));
// set state to executing
- ASSERT_NO_FATAL_FAILURE(changeStateIdletoLoaded(omxNode, observer, &iBuffer,
- &oBuffer, kPortIndexInput,
- kPortIndexOutput));
+ ASSERT_NO_FATAL_FAILURE(changeStateIdletoLoaded(omxNode, observer, &iBuffer, &oBuffer,
+ kPortIndexInput, kPortIndexOutput, portMode,
+ true));
}
// Test for adaptive playback support
@@ -1001,9 +1002,9 @@
ASSERT_NO_FATAL_FAILURE(
changeStateExecutetoIdle(omxNode, observer, &iBuffer, &oBuffer));
// set state to executing
- ASSERT_NO_FATAL_FAILURE(changeStateIdletoLoaded(omxNode, observer, &iBuffer,
- &oBuffer, kPortIndexInput,
- kPortIndexOutput));
+ ASSERT_NO_FATAL_FAILURE(changeStateIdletoLoaded(omxNode, observer, &iBuffer, &oBuffer,
+ kPortIndexInput, kPortIndexOutput, portMode,
+ true));
}
// end of sequence test
@@ -1067,9 +1068,9 @@
ASSERT_NO_FATAL_FAILURE(
changeStateExecutetoIdle(omxNode, observer, &iBuffer, &oBuffer));
// set state to executing
- ASSERT_NO_FATAL_FAILURE(changeStateIdletoLoaded(omxNode, observer, &iBuffer,
- &oBuffer, kPortIndexInput,
- kPortIndexOutput));
+ ASSERT_NO_FATAL_FAILURE(changeStateIdletoLoaded(omxNode, observer, &iBuffer, &oBuffer,
+ kPortIndexInput, kPortIndexOutput, portMode,
+ true));
}
// end of sequence test
@@ -1188,9 +1189,9 @@
ASSERT_NO_FATAL_FAILURE(
changeStateExecutetoIdle(omxNode, observer, &iBuffer, &oBuffer));
// set state to executing
- ASSERT_NO_FATAL_FAILURE(changeStateIdletoLoaded(omxNode, observer, &iBuffer,
- &oBuffer, kPortIndexInput,
- kPortIndexOutput));
+ ASSERT_NO_FATAL_FAILURE(changeStateIdletoLoaded(omxNode, observer, &iBuffer, &oBuffer,
+ kPortIndexInput, kPortIndexOutput, portMode,
+ true));
}
// end of sequence test
@@ -1295,9 +1296,9 @@
ASSERT_NO_FATAL_FAILURE(
changeStateExecutetoIdle(omxNode, observer, &iBuffer, &oBuffer));
// set state to executing
- ASSERT_NO_FATAL_FAILURE(changeStateIdletoLoaded(omxNode, observer, &iBuffer,
- &oBuffer, kPortIndexInput,
- kPortIndexOutput));
+ ASSERT_NO_FATAL_FAILURE(changeStateIdletoLoaded(omxNode, observer, &iBuffer, &oBuffer,
+ kPortIndexInput, kPortIndexOutput, portMode,
+ true));
}
// test input/output port flush
@@ -1414,9 +1415,9 @@
ASSERT_NO_FATAL_FAILURE(
changeStateExecutetoIdle(omxNode, observer, &iBuffer, &oBuffer));
// set state to executing
- ASSERT_NO_FATAL_FAILURE(changeStateIdletoLoaded(omxNode, observer, &iBuffer,
- &oBuffer, kPortIndexInput,
- kPortIndexOutput));
+ ASSERT_NO_FATAL_FAILURE(changeStateIdletoLoaded(omxNode, observer, &iBuffer, &oBuffer,
+ kPortIndexInput, kPortIndexOutput, portMode,
+ true));
}
GTEST_ALLOW_UNINSTANTIATED_PARAMETERIZED_TEST(VideoDecHidlTest);
diff --git a/media/omx/1.0/vts/functional/video/VtsHalMediaOmxV1_0TargetVideoEncTest.cpp b/media/omx/1.0/vts/functional/video/VtsHalMediaOmxV1_0TargetVideoEncTest.cpp
index 3c0734e..f24c6d1 100644
--- a/media/omx/1.0/vts/functional/video/VtsHalMediaOmxV1_0TargetVideoEncTest.cpp
+++ b/media/omx/1.0/vts/functional/video/VtsHalMediaOmxV1_0TargetVideoEncTest.cpp
@@ -1057,9 +1057,9 @@
ASSERT_NO_FATAL_FAILURE(changeStateExecutetoIdle(
omxNode, observer, &buffersource->iBuffer, &buffersource->oBuffer));
// set state to executing
- ASSERT_NO_FATAL_FAILURE(changeStateIdletoLoaded(
- omxNode, observer, &buffersource->iBuffer, &buffersource->oBuffer,
- kPortIndexInput, kPortIndexOutput));
+ ASSERT_NO_FATAL_FAILURE(changeStateIdletoLoaded(omxNode, observer, &buffersource->iBuffer,
+ &buffersource->oBuffer, kPortIndexInput,
+ kPortIndexOutput, portMode));
// test for callbacks
EXPECT_EQ(buffersource->callback, 31);
}
@@ -1174,9 +1174,8 @@
ASSERT_NO_FATAL_FAILURE(
changeStateExecutetoIdle(omxNode, observer, &iBuffer, &oBuffer));
// set state to executing
- ASSERT_NO_FATAL_FAILURE(changeStateIdletoLoaded(omxNode, observer, &iBuffer,
- &oBuffer, kPortIndexInput,
- kPortIndexOutput));
+ ASSERT_NO_FATAL_FAILURE(changeStateIdletoLoaded(omxNode, observer, &iBuffer, &oBuffer,
+ kPortIndexInput, kPortIndexOutput, portMode));
}
// test raw stream encode (input is ANW buffers)
@@ -1337,9 +1336,8 @@
changeStateExecutetoIdle(omxNode, observer, &iBuffer, &oBuffer));
EXPECT_EQ(portDef.nBufferCountActual, listener->freeBuffers);
// set state to executing
- ASSERT_NO_FATAL_FAILURE(changeStateIdletoLoaded(omxNode, observer, &iBuffer,
- &oBuffer, kPortIndexInput,
- kPortIndexOutput));
+ ASSERT_NO_FATAL_FAILURE(changeStateIdletoLoaded(omxNode, observer, &iBuffer, &oBuffer,
+ kPortIndexInput, kPortIndexOutput, portMode));
returnval = producer->disconnect(
NATIVE_WINDOW_API_CPU, IGraphicBufferProducer::DisconnectMode::API);
@@ -1452,9 +1450,8 @@
changeStateExecutetoIdle(omxNode, observer, &iBuffer, &oBuffer));
EXPECT_EQ(portDef.nBufferCountActual, listener->freeBuffers);
// set state to executing
- ASSERT_NO_FATAL_FAILURE(changeStateIdletoLoaded(omxNode, observer, &iBuffer,
- &oBuffer, kPortIndexInput,
- kPortIndexOutput));
+ ASSERT_NO_FATAL_FAILURE(changeStateIdletoLoaded(omxNode, observer, &iBuffer, &oBuffer,
+ kPortIndexInput, kPortIndexOutput, portMode));
returnval = producer->disconnect(
NATIVE_WINDOW_API_CPU, IGraphicBufferProducer::DisconnectMode::API);
diff --git a/neuralnetworks/1.0/utils/include/nnapi/hal/1.0/Utils.h b/neuralnetworks/1.0/utils/include/nnapi/hal/1.0/Utils.h
index 4cec545..b695f48 100644
--- a/neuralnetworks/1.0/utils/include/nnapi/hal/1.0/Utils.h
+++ b/neuralnetworks/1.0/utils/include/nnapi/hal/1.0/Utils.h
@@ -44,6 +44,12 @@
return result.has_value();
}
+template <typename Type>
+auto convertFromNonCanonical(const Type& nonCanonicalObject)
+ -> decltype(convert(nn::convert(nonCanonicalObject).value())) {
+ return convert(NN_TRY(nn::convert(nonCanonicalObject)));
+}
+
} // namespace android::hardware::neuralnetworks::V1_0::utils
#endif // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_0_UTILS_H
diff --git a/neuralnetworks/1.1/utils/include/nnapi/hal/1.1/Utils.h b/neuralnetworks/1.1/utils/include/nnapi/hal/1.1/Utils.h
index 052d88e..09597a3 100644
--- a/neuralnetworks/1.1/utils/include/nnapi/hal/1.1/Utils.h
+++ b/neuralnetworks/1.1/utils/include/nnapi/hal/1.1/Utils.h
@@ -47,6 +47,12 @@
return result.has_value();
}
+template <typename Type>
+auto convertFromNonCanonical(const Type& nonCanonicalObject)
+ -> decltype(convert(nn::convert(nonCanonicalObject).value())) {
+ return convert(NN_TRY(nn::convert(nonCanonicalObject)));
+}
+
} // namespace android::hardware::neuralnetworks::V1_1::utils
#endif // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_1_UTILS_H
diff --git a/neuralnetworks/1.2/utils/include/nnapi/hal/1.2/Utils.h b/neuralnetworks/1.2/utils/include/nnapi/hal/1.2/Utils.h
index c289fc8..3233114 100644
--- a/neuralnetworks/1.2/utils/include/nnapi/hal/1.2/Utils.h
+++ b/neuralnetworks/1.2/utils/include/nnapi/hal/1.2/Utils.h
@@ -54,6 +54,12 @@
return result.has_value();
}
+template <typename Type>
+auto convertFromNonCanonical(const Type& nonCanonicalObject)
+ -> decltype(convert(nn::convert(nonCanonicalObject).value())) {
+ return convert(NN_TRY(nn::convert(nonCanonicalObject)));
+}
+
} // namespace android::hardware::neuralnetworks::V1_2::utils
#endif // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_2_UTILS_H
diff --git a/neuralnetworks/1.2/utils/src/Callbacks.cpp b/neuralnetworks/1.2/utils/src/Callbacks.cpp
index fefa122..9f54bb1 100644
--- a/neuralnetworks/1.2/utils/src/Callbacks.cpp
+++ b/neuralnetworks/1.2/utils/src/Callbacks.cpp
@@ -43,6 +43,15 @@
namespace android::hardware::neuralnetworks::V1_2::utils {
namespace {
+nn::GeneralResult<nn::SharedPreparedModel> prepareModelCallback(
+ V1_0::ErrorStatus status, const sp<V1_0::IPreparedModel>& preparedModel) {
+ if (const auto dynamicPreparedModel =
+ V1_2::IPreparedModel::castFrom(preparedModel).withDefault(nullptr)) {
+ return V1_2::utils::prepareModelCallback(status, dynamicPreparedModel);
+ }
+ return V1_0::utils::prepareModelCallback(status, preparedModel);
+}
+
nn::GeneralResult<std::pair<std::vector<nn::OutputShape>, nn::Timing>>
convertExecutionGeneralResultsHelper(const hidl_vec<OutputShape>& outputShapes,
const Timing& timing) {
@@ -72,7 +81,7 @@
Return<void> PreparedModelCallback::notify(V1_0::ErrorStatus status,
const sp<V1_0::IPreparedModel>& preparedModel) {
- mData.put(V1_0::utils::prepareModelCallback(status, preparedModel));
+ mData.put(prepareModelCallback(status, preparedModel));
return Void();
}
diff --git a/neuralnetworks/1.3/utils/include/nnapi/hal/1.3/Utils.h b/neuralnetworks/1.3/utils/include/nnapi/hal/1.3/Utils.h
index 29b0c80..3ce412c 100644
--- a/neuralnetworks/1.3/utils/include/nnapi/hal/1.3/Utils.h
+++ b/neuralnetworks/1.3/utils/include/nnapi/hal/1.3/Utils.h
@@ -49,6 +49,12 @@
return result.has_value();
}
+template <typename Type>
+auto convertFromNonCanonical(const Type& nonCanonicalObject)
+ -> decltype(convert(nn::convert(nonCanonicalObject).value())) {
+ return convert(NN_TRY(nn::convert(nonCanonicalObject)));
+}
+
} // namespace android::hardware::neuralnetworks::V1_3::utils
#endif // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_3_UTILS_H
diff --git a/neuralnetworks/1.3/utils/src/Callbacks.cpp b/neuralnetworks/1.3/utils/src/Callbacks.cpp
index af76e6a..8e9fb83 100644
--- a/neuralnetworks/1.3/utils/src/Callbacks.cpp
+++ b/neuralnetworks/1.3/utils/src/Callbacks.cpp
@@ -28,6 +28,7 @@
#include <nnapi/IPreparedModel.h>
#include <nnapi/Result.h>
#include <nnapi/Types.h>
+#include <nnapi/hal/1.0/Callbacks.h>
#include <nnapi/hal/1.0/Conversions.h>
#include <nnapi/hal/1.0/PreparedModel.h>
#include <nnapi/hal/1.2/Callbacks.h>
@@ -46,6 +47,20 @@
namespace android::hardware::neuralnetworks::V1_3::utils {
namespace {
+nn::GeneralResult<nn::SharedPreparedModel> prepareModelCallback(
+ V1_0::ErrorStatus status, const sp<V1_0::IPreparedModel>& preparedModel) {
+ if (const auto dynamicPreparedModel =
+ V1_3::IPreparedModel::castFrom(preparedModel).withDefault(nullptr)) {
+ const auto currentVersionStatus = NN_TRY(convertFromNonCanonical(status));
+ return V1_3::utils::prepareModelCallback(currentVersionStatus, dynamicPreparedModel);
+ }
+ if (const auto dynamicPreparedModel =
+ V1_2::IPreparedModel::castFrom(preparedModel).withDefault(nullptr)) {
+ return V1_2::utils::prepareModelCallback(status, dynamicPreparedModel);
+ }
+ return V1_0::utils::prepareModelCallback(status, preparedModel);
+}
+
nn::GeneralResult<std::pair<std::vector<nn::OutputShape>, nn::Timing>>
convertExecutionGeneralResultsHelper(const hidl_vec<V1_2::OutputShape>& outputShapes,
const V1_2::Timing& timing) {
@@ -82,13 +97,13 @@
Return<void> PreparedModelCallback::notify(V1_0::ErrorStatus status,
const sp<V1_0::IPreparedModel>& preparedModel) {
- mData.put(V1_0::utils::prepareModelCallback(status, preparedModel));
+ mData.put(prepareModelCallback(status, preparedModel));
return Void();
}
Return<void> PreparedModelCallback::notify_1_2(V1_0::ErrorStatus status,
const sp<V1_2::IPreparedModel>& preparedModel) {
- mData.put(V1_2::utils::prepareModelCallback(status, preparedModel));
+ mData.put(prepareModelCallback(status, preparedModel));
return Void();
}
diff --git a/neuralnetworks/1.3/vts/functional/Android.bp b/neuralnetworks/1.3/vts/functional/Android.bp
index b17d445..ee753bb 100644
--- a/neuralnetworks/1.3/vts/functional/Android.bp
+++ b/neuralnetworks/1.3/vts/functional/Android.bp
@@ -57,6 +57,7 @@
"VtsHalNeuralNetworksV1_0_utils",
"VtsHalNeuralNetworksV1_2_utils",
"VtsHalNeuralNetworksV1_3_utils",
+ "android.hardware.neuralnetworks-V1-ndk_platform",
"android.hardware.neuralnetworks@1.0",
"android.hardware.neuralnetworks@1.1",
"android.hardware.neuralnetworks@1.2",
diff --git a/neuralnetworks/aidl/Android.bp b/neuralnetworks/aidl/Android.bp
new file mode 100644
index 0000000..0557e43
--- /dev/null
+++ b/neuralnetworks/aidl/Android.bp
@@ -0,0 +1,27 @@
+aidl_interface {
+ name: "android.hardware.neuralnetworks",
+ vendor_available: true,
+ srcs: [
+ "android/hardware/neuralnetworks/*.aidl",
+ ],
+ stability: "vintf",
+ imports: [
+ "android.hardware.common",
+ ],
+ backend: {
+ java: {
+ enabled: false,
+ },
+ cpp: {
+ enabled: false,
+ },
+ ndk: {
+ apex_available: [
+ "//apex_available:platform",
+ "com.android.neuralnetworks",
+ "test_com.android.neuralnetworks",
+ ],
+ min_sdk_version: "30",
+ },
+ },
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/BufferDesc.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/BufferDesc.aidl
new file mode 100644
index 0000000..2074a2a
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/BufferDesc.aidl
@@ -0,0 +1,22 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable BufferDesc {
+ int[] dimensions;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/BufferRole.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/BufferRole.aidl
new file mode 100644
index 0000000..97f748b
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/BufferRole.aidl
@@ -0,0 +1,24 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable BufferRole {
+ int modelIndex;
+ int ioIndex;
+ float frequency;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Capabilities.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Capabilities.aidl
new file mode 100644
index 0000000..31afafc
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Capabilities.aidl
@@ -0,0 +1,26 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable Capabilities {
+ android.hardware.neuralnetworks.PerformanceInfo relaxedFloat32toFloat16PerformanceScalar;
+ android.hardware.neuralnetworks.PerformanceInfo relaxedFloat32toFloat16PerformanceTensor;
+ android.hardware.neuralnetworks.OperandPerformance[] operandPerformance;
+ android.hardware.neuralnetworks.PerformanceInfo ifPerformance;
+ android.hardware.neuralnetworks.PerformanceInfo whilePerformance;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/DataLocation.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/DataLocation.aidl
new file mode 100644
index 0000000..5b03ba0
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/DataLocation.aidl
@@ -0,0 +1,24 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable DataLocation {
+ int poolIndex;
+ long offset;
+ long length;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/DeviceBuffer.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/DeviceBuffer.aidl
new file mode 100644
index 0000000..9cff6db
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/DeviceBuffer.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable DeviceBuffer {
+ android.hardware.neuralnetworks.IBuffer buffer;
+ int token;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/DeviceType.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/DeviceType.aidl
new file mode 100644
index 0000000..dd4dae7
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/DeviceType.aidl
@@ -0,0 +1,25 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@Backing(type="int") @VintfStability
+enum DeviceType {
+ OTHER = 1,
+ CPU = 2,
+ GPU = 3,
+ ACCELERATOR = 4,
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ErrorStatus.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ErrorStatus.aidl
new file mode 100644
index 0000000..ba18c38
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ErrorStatus.aidl
@@ -0,0 +1,30 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@Backing(type="int") @VintfStability
+enum ErrorStatus {
+ NONE = 0,
+ DEVICE_UNAVAILABLE = 1,
+ GENERAL_FAILURE = 2,
+ OUTPUT_INSUFFICIENT_SIZE = 3,
+ INVALID_ARGUMENT = 4,
+ MISSED_DEADLINE_TRANSIENT = 5,
+ MISSED_DEADLINE_PERSISTENT = 6,
+ RESOURCE_EXHAUSTED_TRANSIENT = 7,
+ RESOURCE_EXHAUSTED_PERSISTENT = 8,
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExecutionPreference.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExecutionPreference.aidl
new file mode 100644
index 0000000..cccae54
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExecutionPreference.aidl
@@ -0,0 +1,24 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@Backing(type="int") @VintfStability
+enum ExecutionPreference {
+ LOW_POWER = 0,
+ FAST_SINGLE_ANSWER = 1,
+ SUSTAINED_SPEED = 2,
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExecutionResult.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExecutionResult.aidl
new file mode 100644
index 0000000..c17ddb9
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExecutionResult.aidl
@@ -0,0 +1,24 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable ExecutionResult {
+ boolean outputSufficientSize;
+ android.hardware.neuralnetworks.OutputShape[] outputShapes;
+ android.hardware.neuralnetworks.Timing timing;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Extension.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Extension.aidl
new file mode 100644
index 0000000..9eb8896
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Extension.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable Extension {
+ String name;
+ android.hardware.neuralnetworks.ExtensionOperandTypeInformation[] operandTypes;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExtensionNameAndPrefix.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExtensionNameAndPrefix.aidl
new file mode 100644
index 0000000..a271a63
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExtensionNameAndPrefix.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable ExtensionNameAndPrefix {
+ String name;
+ char prefix;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExtensionOperandTypeInformation.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExtensionOperandTypeInformation.aidl
new file mode 100644
index 0000000..d1c3f09
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExtensionOperandTypeInformation.aidl
@@ -0,0 +1,24 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable ExtensionOperandTypeInformation {
+ char type;
+ boolean isTensor;
+ int byteSize;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/FusedActivationFunc.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/FusedActivationFunc.aidl
new file mode 100644
index 0000000..ddd3c2a
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/FusedActivationFunc.aidl
@@ -0,0 +1,25 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@Backing(type="int") @VintfStability
+enum FusedActivationFunc {
+ NONE = 0,
+ RELU = 1,
+ RELU1 = 2,
+ RELU6 = 3,
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IBuffer.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IBuffer.aidl
new file mode 100644
index 0000000..a297a6b
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IBuffer.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+interface IBuffer {
+ void copyFrom(in android.hardware.neuralnetworks.Memory src, in int[] dimensions);
+ void copyTo(in android.hardware.neuralnetworks.Memory dst);
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IDevice.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IDevice.aidl
new file mode 100644
index 0000000..38fda16
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IDevice.aidl
@@ -0,0 +1,36 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+interface IDevice {
+ android.hardware.neuralnetworks.DeviceBuffer allocate(in android.hardware.neuralnetworks.BufferDesc desc, in android.hardware.neuralnetworks.IPreparedModelParcel[] preparedModels, in android.hardware.neuralnetworks.BufferRole[] inputRoles, in android.hardware.neuralnetworks.BufferRole[] outputRoles);
+ android.hardware.neuralnetworks.Capabilities getCapabilities();
+ android.hardware.neuralnetworks.NumberOfCacheFiles getNumberOfCacheFilesNeeded();
+ android.hardware.neuralnetworks.Extension[] getSupportedExtensions();
+ boolean[] getSupportedOperations(in android.hardware.neuralnetworks.Model model);
+ android.hardware.neuralnetworks.DeviceType getType();
+ String getVersionString();
+ void prepareModel(in android.hardware.neuralnetworks.Model model, in android.hardware.neuralnetworks.ExecutionPreference preference, in android.hardware.neuralnetworks.Priority priority, in long deadline, in ParcelFileDescriptor[] modelCache, in ParcelFileDescriptor[] dataCache, in byte[] token, in android.hardware.neuralnetworks.IPreparedModelCallback callback);
+ void prepareModelFromCache(in long deadline, in ParcelFileDescriptor[] modelCache, in ParcelFileDescriptor[] dataCache, in byte[] token, in android.hardware.neuralnetworks.IPreparedModelCallback callback);
+ const int BYTE_SIZE_OF_CACHE_TOKEN = 32;
+ const int MAX_NUMBER_OF_CACHE_FILES = 32;
+ const int EXTENSION_TYPE_HIGH_BITS_PREFIX = 15;
+ const int EXTENSION_TYPE_LOW_BITS_TYPE = 16;
+ const int OPERAND_TYPE_BASE_MAX = 65535;
+ const int OPERATION_TYPE_BASE_MAX = 65535;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IFencedExecutionCallback.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IFencedExecutionCallback.aidl
new file mode 100644
index 0000000..a7cf906
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IFencedExecutionCallback.aidl
@@ -0,0 +1,22 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+interface IFencedExecutionCallback {
+ android.hardware.neuralnetworks.ErrorStatus getExecutionInfo(out android.hardware.neuralnetworks.Timing timingLaunched, out android.hardware.neuralnetworks.Timing timingFenced);
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IPreparedModel.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IPreparedModel.aidl
new file mode 100644
index 0000000..8767712
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IPreparedModel.aidl
@@ -0,0 +1,25 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+interface IPreparedModel {
+ android.hardware.neuralnetworks.ExecutionResult executeSynchronously(in android.hardware.neuralnetworks.Request request, in boolean measureTiming, in long deadline, in long loopTimeoutDuration);
+ android.hardware.neuralnetworks.IFencedExecutionCallback executeFenced(in android.hardware.neuralnetworks.Request request, in ParcelFileDescriptor[] waitFor, in boolean measureTiming, in long deadline, in long loopTimeoutDuration, in long duration, out @nullable ParcelFileDescriptor syncFence);
+ const long DEFAULT_LOOP_TIMEOUT_DURATION_NS = 2000000000;
+ const long MAXIMUM_LOOP_TIMEOUT_DURATION_NS = 15000000000;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IPreparedModelCallback.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IPreparedModelCallback.aidl
new file mode 100644
index 0000000..d1ae2eb
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IPreparedModelCallback.aidl
@@ -0,0 +1,22 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+interface IPreparedModelCallback {
+ void notify(in android.hardware.neuralnetworks.ErrorStatus status, in android.hardware.neuralnetworks.IPreparedModel preparedModel);
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IPreparedModelParcel.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IPreparedModelParcel.aidl
new file mode 100644
index 0000000..048251a
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IPreparedModelParcel.aidl
@@ -0,0 +1,22 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable IPreparedModelParcel {
+ android.hardware.neuralnetworks.IPreparedModel preparedModel;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Memory.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Memory.aidl
new file mode 100644
index 0000000..aa735c0
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Memory.aidl
@@ -0,0 +1,24 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable Memory {
+ android.hardware.common.NativeHandle handle;
+ long size;
+ String name;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Model.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Model.aidl
new file mode 100644
index 0000000..944bd7f
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Model.aidl
@@ -0,0 +1,27 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable Model {
+ android.hardware.neuralnetworks.Subgraph main;
+ android.hardware.neuralnetworks.Subgraph[] referenced;
+ byte[] operandValues;
+ android.hardware.neuralnetworks.Memory[] pools;
+ boolean relaxComputationFloat32toFloat16;
+ android.hardware.neuralnetworks.ExtensionNameAndPrefix[] extensionNameToPrefix;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/NumberOfCacheFiles.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/NumberOfCacheFiles.aidl
new file mode 100644
index 0000000..ca5f917
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/NumberOfCacheFiles.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable NumberOfCacheFiles {
+ int numModelCache;
+ int numDataCache;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Operand.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Operand.aidl
new file mode 100644
index 0000000..6615b9b
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Operand.aidl
@@ -0,0 +1,28 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable Operand {
+ android.hardware.neuralnetworks.OperandType type;
+ int[] dimensions;
+ float scale;
+ int zeroPoint;
+ android.hardware.neuralnetworks.OperandLifeTime lifetime;
+ android.hardware.neuralnetworks.DataLocation location;
+ @nullable android.hardware.neuralnetworks.OperandExtraParams extraParams;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandExtraParams.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandExtraParams.aidl
new file mode 100644
index 0000000..20317c7
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandExtraParams.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+union OperandExtraParams {
+ android.hardware.neuralnetworks.SymmPerChannelQuantParams channelQuant;
+ byte[] extension;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandLifeTime.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandLifeTime.aidl
new file mode 100644
index 0000000..1082f9e
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandLifeTime.aidl
@@ -0,0 +1,28 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@Backing(type="int") @VintfStability
+enum OperandLifeTime {
+ TEMPORARY_VARIABLE = 0,
+ SUBGRAPH_INPUT = 1,
+ SUBGRAPH_OUTPUT = 2,
+ CONSTANT_COPY = 3,
+ CONSTANT_POOL = 4,
+ NO_VALUE = 5,
+ SUBGRAPH = 6,
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandPerformance.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandPerformance.aidl
new file mode 100644
index 0000000..9232b4c
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandPerformance.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable OperandPerformance {
+ android.hardware.neuralnetworks.OperandType type;
+ android.hardware.neuralnetworks.PerformanceInfo info;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandType.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandType.aidl
new file mode 100644
index 0000000..bd95fab
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandType.aidl
@@ -0,0 +1,37 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@Backing(type="int") @VintfStability
+enum OperandType {
+ FLOAT32 = 0,
+ INT32 = 1,
+ UINT32 = 2,
+ TENSOR_FLOAT32 = 3,
+ TENSOR_INT32 = 4,
+ TENSOR_QUANT8_ASYMM = 5,
+ BOOL = 6,
+ TENSOR_QUANT16_SYMM = 7,
+ TENSOR_FLOAT16 = 8,
+ TENSOR_BOOL8 = 9,
+ FLOAT16 = 10,
+ TENSOR_QUANT8_SYMM_PER_CHANNEL = 11,
+ TENSOR_QUANT16_ASYMM = 12,
+ TENSOR_QUANT8_SYMM = 13,
+ TENSOR_QUANT8_ASYMM_SIGNED = 14,
+ SUBGRAPH = 15,
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Operation.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Operation.aidl
new file mode 100644
index 0000000..383eba4
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Operation.aidl
@@ -0,0 +1,24 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable Operation {
+ android.hardware.neuralnetworks.OperationType type;
+ int[] inputs;
+ int[] outputs;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperationType.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperationType.aidl
new file mode 100644
index 0000000..f786829
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperationType.aidl
@@ -0,0 +1,123 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@Backing(type="int") @VintfStability
+enum OperationType {
+ ADD = 0,
+ AVERAGE_POOL_2D = 1,
+ CONCATENATION = 2,
+ CONV_2D = 3,
+ DEPTHWISE_CONV_2D = 4,
+ DEPTH_TO_SPACE = 5,
+ DEQUANTIZE = 6,
+ EMBEDDING_LOOKUP = 7,
+ FLOOR = 8,
+ FULLY_CONNECTED = 9,
+ HASHTABLE_LOOKUP = 10,
+ L2_NORMALIZATION = 11,
+ L2_POOL_2D = 12,
+ LOCAL_RESPONSE_NORMALIZATION = 13,
+ LOGISTIC = 14,
+ LSH_PROJECTION = 15,
+ LSTM = 16,
+ MAX_POOL_2D = 17,
+ MUL = 18,
+ RELU = 19,
+ RELU1 = 20,
+ RELU6 = 21,
+ RESHAPE = 22,
+ RESIZE_BILINEAR = 23,
+ RNN = 24,
+ SOFTMAX = 25,
+ SPACE_TO_DEPTH = 26,
+ SVDF = 27,
+ TANH = 28,
+ BATCH_TO_SPACE_ND = 29,
+ DIV = 30,
+ MEAN = 31,
+ PAD = 32,
+ SPACE_TO_BATCH_ND = 33,
+ SQUEEZE = 34,
+ STRIDED_SLICE = 35,
+ SUB = 36,
+ TRANSPOSE = 37,
+ ABS = 38,
+ ARGMAX = 39,
+ ARGMIN = 40,
+ AXIS_ALIGNED_BBOX_TRANSFORM = 41,
+ BIDIRECTIONAL_SEQUENCE_LSTM = 42,
+ BIDIRECTIONAL_SEQUENCE_RNN = 43,
+ BOX_WITH_NMS_LIMIT = 44,
+ CAST = 45,
+ CHANNEL_SHUFFLE = 46,
+ DETECTION_POSTPROCESSING = 47,
+ EQUAL = 48,
+ EXP = 49,
+ EXPAND_DIMS = 50,
+ GATHER = 51,
+ GENERATE_PROPOSALS = 52,
+ GREATER = 53,
+ GREATER_EQUAL = 54,
+ GROUPED_CONV_2D = 55,
+ HEATMAP_MAX_KEYPOINT = 56,
+ INSTANCE_NORMALIZATION = 57,
+ LESS = 58,
+ LESS_EQUAL = 59,
+ LOG = 60,
+ LOGICAL_AND = 61,
+ LOGICAL_NOT = 62,
+ LOGICAL_OR = 63,
+ LOG_SOFTMAX = 64,
+ MAXIMUM = 65,
+ MINIMUM = 66,
+ NEG = 67,
+ NOT_EQUAL = 68,
+ PAD_V2 = 69,
+ POW = 70,
+ PRELU = 71,
+ QUANTIZE = 72,
+ QUANTIZED_16BIT_LSTM = 73,
+ RANDOM_MULTINOMIAL = 74,
+ REDUCE_ALL = 75,
+ REDUCE_ANY = 76,
+ REDUCE_MAX = 77,
+ REDUCE_MIN = 78,
+ REDUCE_PROD = 79,
+ REDUCE_SUM = 80,
+ ROI_ALIGN = 81,
+ ROI_POOLING = 82,
+ RSQRT = 83,
+ SELECT = 84,
+ SIN = 85,
+ SLICE = 86,
+ SPLIT = 87,
+ SQRT = 88,
+ TILE = 89,
+ TOPK_V2 = 90,
+ TRANSPOSE_CONV_2D = 91,
+ UNIDIRECTIONAL_SEQUENCE_LSTM = 92,
+ UNIDIRECTIONAL_SEQUENCE_RNN = 93,
+ RESIZE_NEAREST_NEIGHBOR = 94,
+ QUANTIZED_LSTM = 95,
+ IF = 96,
+ WHILE = 97,
+ ELU = 98,
+ HARD_SWISH = 99,
+ FILL = 100,
+ RANK = 101,
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OutputShape.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OutputShape.aidl
new file mode 100644
index 0000000..1300c49
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OutputShape.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable OutputShape {
+ int[] dimensions;
+ boolean isSufficient;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/PerformanceInfo.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/PerformanceInfo.aidl
new file mode 100644
index 0000000..b5dc179
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/PerformanceInfo.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable PerformanceInfo {
+ float execTime;
+ float powerUsage;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Priority.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Priority.aidl
new file mode 100644
index 0000000..980bee3
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Priority.aidl
@@ -0,0 +1,24 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@Backing(type="int") @VintfStability
+enum Priority {
+ LOW = 0,
+ MEDIUM = 1,
+ HIGH = 2,
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Request.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Request.aidl
new file mode 100644
index 0000000..6f77066
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Request.aidl
@@ -0,0 +1,24 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable Request {
+ android.hardware.neuralnetworks.RequestArgument[] inputs;
+ android.hardware.neuralnetworks.RequestArgument[] outputs;
+ android.hardware.neuralnetworks.RequestMemoryPool[] pools;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/RequestArgument.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/RequestArgument.aidl
new file mode 100644
index 0000000..c9560ef
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/RequestArgument.aidl
@@ -0,0 +1,24 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable RequestArgument {
+ boolean hasNoValue;
+ android.hardware.neuralnetworks.DataLocation location;
+ int[] dimensions;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/RequestMemoryPool.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/RequestMemoryPool.aidl
new file mode 100644
index 0000000..123e4b0
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/RequestMemoryPool.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+union RequestMemoryPool {
+ android.hardware.neuralnetworks.Memory pool;
+ int token;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Subgraph.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Subgraph.aidl
new file mode 100644
index 0000000..771d15a
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Subgraph.aidl
@@ -0,0 +1,25 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable Subgraph {
+ android.hardware.neuralnetworks.Operand[] operands;
+ android.hardware.neuralnetworks.Operation[] operations;
+ int[] inputIndexes;
+ int[] outputIndexes;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/SymmPerChannelQuantParams.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/SymmPerChannelQuantParams.aidl
new file mode 100644
index 0000000..2282feb
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/SymmPerChannelQuantParams.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable SymmPerChannelQuantParams {
+ float[] scales;
+ int channelDim;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Timing.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Timing.aidl
new file mode 100644
index 0000000..b08d34a
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Timing.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable Timing {
+ long timeOnDevice;
+ long timeInDriver;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/BufferDesc.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/BufferDesc.aidl
new file mode 100644
index 0000000..1b92ebc
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/BufferDesc.aidl
@@ -0,0 +1,31 @@
+/*
+ * Copyright (C) 2020 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;
+
+/**
+ * A buffer descriptor. Describes the properties of a buffer.
+ */
+@VintfStability
+parcelable BufferDesc {
+ /**
+ * Dimensions of the buffer. May have unknown dimensions or rank. A buffer with some number of
+ * unspecified dimensions is represented by setting each unspecified dimension to 0. A buffer
+ * with unspecified rank is represented by providing an empty dimensions vector.
+ */
+ int[] dimensions;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/BufferRole.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/BufferRole.aidl
new file mode 100644
index 0000000..7877bc0
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/BufferRole.aidl
@@ -0,0 +1,40 @@
+/*
+ * Copyright (C) 2020 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;
+
+/**
+ * Describes a role of an input or output to a prepared model.
+ */
+@VintfStability
+parcelable BufferRole {
+ /**
+ * The index of the IPreparedModel within the "preparedModel" argument passed in
+ * IDevice::allocate.
+ */
+ int modelIndex;
+ /**
+ * The index of the input or output operand.
+ */
+ int ioIndex;
+ /**
+ * A floating-point value within the range (0.0, 1.0]. Describes how likely the buffer is to be
+ * used in the specified role. This is provided as a hint to optimize the case when multiple
+ * roles prefer different buffer locations or data layouts.
+ */
+ float frequency;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/Capabilities.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/Capabilities.aidl
new file mode 100644
index 0000000..5ce78ee
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/Capabilities.aidl
@@ -0,0 +1,63 @@
+/*
+ * Copyright (C) 2020 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;
+
+import android.hardware.neuralnetworks.OperandPerformance;
+import android.hardware.neuralnetworks.PerformanceInfo;
+
+/**
+ * The capabilities of a driver.
+ *
+ * This represents performance of non-extension operations.
+ *
+ * Performance of an operation other than {@link OperationType::IF} and {@link OperationType::WHILE}
+ * comes from the type of its first operand.
+ */
+@VintfStability
+parcelable 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;
+ /**
+ * Performance by operand type. Must be sorted by OperandType.
+ *
+ * If a particular {@link OperandType} is not present in operandPerformance, its performance is
+ * treated as { .execTime = FLT_MAX, .powerUsage = FLT_MAX }.
+ *
+ * Performance does not apply to {@link OperandType::SUBGRAPH}, and a driver must not report
+ * operand performance for {@link OperandType::SUBGRAPH}.
+ */
+ OperandPerformance[] operandPerformance;
+ /**
+ * Performance of an {@link OperationType::IF} operation is the sum of
+ * {@link Capabilities::ifPerformance} and the mean of performance for the two branch subgraphs,
+ * where performance for a subgraph is the sum of the performance of all operations within the
+ * subgraph.
+ */
+ PerformanceInfo ifPerformance;
+ /**
+ * Performance of a {@link OperationType::WHILE} operation is the sum of
+ * {@link Capabilities::whilePerformance}, performance for the condition subgraph and
+ * performance for the body subgraph, where performance for a subgraph is the sum of the
+ * performance of all operations within the subgraph.
+ */
+ PerformanceInfo whilePerformance;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/DataLocation.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/DataLocation.aidl
new file mode 100644
index 0000000..57e3f4a
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/DataLocation.aidl
@@ -0,0 +1,37 @@
+/*
+ * Copyright (C) 2020 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;
+
+/**
+ * Describes the location of a data object.
+ */
+@VintfStability
+parcelable DataLocation {
+ /**
+ * The index of the memory pool where this location is found.
+ */
+ int poolIndex;
+ /**
+ * Offset in bytes from the start of the pool.
+ */
+ long offset;
+ /**
+ * The length of the data in bytes.
+ */
+ long length;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/DeviceBuffer.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/DeviceBuffer.aidl
new file mode 100644
index 0000000..d51e1b2
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/DeviceBuffer.aidl
@@ -0,0 +1,36 @@
+/*
+ * Copyright (C) 2020 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;
+
+import android.hardware.neuralnetworks.IBuffer;
+
+/**
+ * A type that is used to represent a driver allocated buffer and token that corresponds to it.
+ */
+ @VintfStability
+ parcelable DeviceBuffer {
+ /**
+ * An IBuffer object used to interact with the device allocated buffer.
+ */
+ IBuffer buffer;
+ /**
+ * A positive token identifying the allocated buffer. The token is provided when referencing the
+ * buffer as one of the memory pools in the request of an execution. The token must not collide
+ * with the tokens of other IBuffer objects that are currently alive in the same driver service.
+ */
+ int token;
+ }
\ No newline at end of file
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/DeviceType.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/DeviceType.aidl
new file mode 100644
index 0000000..8399d50
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/DeviceType.aidl
@@ -0,0 +1,45 @@
+/*
+ * Copyright (C) 2020 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;
+
+/**
+ * Device types.
+ *
+ * The type of NNAPI device.
+ */
+@VintfStability
+@Backing(type="int")
+enum DeviceType {
+ /**
+ * 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,
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/ErrorStatus.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/ErrorStatus.aidl
new file mode 100644
index 0000000..860f86a
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/ErrorStatus.aidl
@@ -0,0 +1,52 @@
+/*
+ * Copyright (C) 2020 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;
+
+/**
+ * Calls to neural networks AIDL interfaces may return a ServiceSpecificException with the following
+ * error codes.
+ */
+@VintfStability
+@Backing(type="int")
+enum ErrorStatus {
+ NONE,
+ DEVICE_UNAVAILABLE,
+ GENERAL_FAILURE,
+ OUTPUT_INSUFFICIENT_SIZE,
+ INVALID_ARGUMENT,
+ /**
+ * Failure because a deadline could not be met for a task, but future deadlines may still be met
+ * for the same task after a short delay.
+ */
+ MISSED_DEADLINE_TRANSIENT,
+ /**
+ * Failure because a deadline could not be met for a task, and future deadlines will likely also
+ * not be met for the same task even after a short delay.
+ */
+ MISSED_DEADLINE_PERSISTENT,
+ /**
+ * Failure because of a resource limitation within the driver, but future calls for the same
+ * task may still succeed after a short delay.
+ */
+ RESOURCE_EXHAUSTED_TRANSIENT,
+ /**
+ * Failure because of a resource limitation within the driver, and future calls for the same
+ * task will likely also fail even after a short delay.
+ */
+ RESOURCE_EXHAUSTED_PERSISTENT,
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/ExecutionPreference.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/ExecutionPreference.aidl
new file mode 100644
index 0000000..901cb38
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/ExecutionPreference.aidl
@@ -0,0 +1,41 @@
+/*
+ * Copyright (C) 2020 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;
+
+/**
+ * Execution preferences.
+ */
+@VintfStability
+@Backing(type="int")
+enum ExecutionPreference {
+ /**
+ * Prefer executing in a way that minimizes battery drain. This is desirable for compilations
+ * that will be executed often.
+ */
+ LOW_POWER,
+ /**
+ * Prefer returning a single answer as fast as possible, even if this causes more power
+ * consumption.
+ */
+ FAST_SINGLE_ANSWER,
+ /**
+ * Prefer maximizing the throughput of successive frames, for example when processing successive
+ * frames coming from the camera.
+ */
+ SUSTAINED_SPEED,
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/ExecutionResult.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/ExecutionResult.aidl
new file mode 100644
index 0000000..403fe09
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/ExecutionResult.aidl
@@ -0,0 +1,47 @@
+/*
+ * Copyright (C) 2021 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;
+
+import android.hardware.neuralnetworks.ErrorStatus;
+import android.hardware.neuralnetworks.OutputShape;
+import android.hardware.neuralnetworks.Timing;
+
+/**
+ * A result from running a synchronous execution of a prepared model.
+ */
+@VintfStability
+parcelable ExecutionResult {
+ /**
+ * A value of "true" indicates that the execution was successful. A value of "false" indicates
+ * the execution failed because at least one output operand buffer was not large enough to store
+ * the corresponding output.
+ */
+ boolean outputSufficientSize;
+ /**
+ * A list of shape information of model output operands. The index in "outputShapes" corresponds
+ * to the index of the output operand in the Request outputs vector.
+ */
+ OutputShape[] outputShapes;
+ /**
+ * Duration of execution. Unless measure is true and the execution is successful, all times must
+ * be reported as -1. A driver may choose to report any time as -1, indicating that measurement
+ * is not available.
+ */
+ Timing timing;
+}
+
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/Extension.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/Extension.aidl
new file mode 100644
index 0000000..159e3c1
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/Extension.aidl
@@ -0,0 +1,42 @@
+/*
+ * Copyright (C) 2020 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;
+
+import android.hardware.neuralnetworks.ExtensionOperandTypeInformation;
+
+/**
+ * Information about an extension.
+ */
+@VintfStability
+parcelable 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 operand types defined by the extension.
+ */
+ ExtensionOperandTypeInformation[] operandTypes;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/ExtensionNameAndPrefix.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/ExtensionNameAndPrefix.aidl
new file mode 100644
index 0000000..76074bf
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/ExtensionNameAndPrefix.aidl
@@ -0,0 +1,49 @@
+/*
+ * Copyright (C) 2020 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;
+
+/**
+ * The mapping between extension names and prefixes of operand and operation type values.
+ *
+ * An operand or operation whose numeric type value is above {@link IDevice::OPERAND_TYPE_BASE_MAX}
+ * or {@link IDevice::OPERATION_TYPE_BASE_MAX} respectively should be interpreted as an extension
+ * operand/operation. The low {@link IDevice::EXTENSION_TYPE_LOW_BITS_TYPE} bits of the value
+ * correspond to the type ID within the extension and the high
+ * {@link IDevice::EXTENSION_TYPE_HIGH_BITS_PREFIX} bits encode the "prefix", which maps uniquely to
+ * the extension name. The sign bit is always 0.
+ *
+ * For example, if a model contains an operation whose value is 0x7AAABBBB and extensionNameToPrefix
+ * contains an entry with prefix=0x7AAA 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.
+ */
+@VintfStability
+parcelable ExtensionNameAndPrefix {
+ /**
+ * The extension name.
+ *
+ * See {@link Extension::name} for the format specification.
+ */
+ String name;
+ /**
+ * The extension prefix. Only the lowest 15 bits are used, so the value must be less than 32768.
+ */
+ char prefix;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/ExtensionOperandTypeInformation.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/ExtensionOperandTypeInformation.aidl
new file mode 100644
index 0000000..d7f93c1
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/ExtensionOperandTypeInformation.aidl
@@ -0,0 +1,38 @@
+/*
+ * Copyright (C) 2020 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;
+
+/**
+ * Information about an extension operand type.
+ */
+@VintfStability
+parcelable ExtensionOperandTypeInformation {
+ /**
+ * The extension operand type.
+ */
+ char type;
+ /**
+ * Indicates whether the extension operand type represents a tensor or a scalar.
+ */
+ boolean isTensor;
+ /**
+ * The byte size of the operand (if scalar) or of a single element (if tensor).
+ */
+ int byteSize;
+}
+
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/FusedActivationFunc.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/FusedActivationFunc.aidl
new file mode 100644
index 0000000..40f1053
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/FusedActivationFunc.aidl
@@ -0,0 +1,30 @@
+/*
+ * Copyright (C) 2020 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;
+
+/**
+ * Fused activation function types.
+ */
+@VintfStability
+@Backing(type="int")
+enum FusedActivationFunc {
+ NONE,
+ RELU,
+ RELU1,
+ RELU6,
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/IBuffer.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/IBuffer.aidl
new file mode 100644
index 0000000..eb3dec6
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/IBuffer.aidl
@@ -0,0 +1,58 @@
+/*
+ * Copyright (C) 2020 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;
+
+import android.hardware.neuralnetworks.Memory;
+
+/**
+ * This interface represents a device memory buffer.
+ */
+@VintfStability
+interface IBuffer {
+ /**
+ * Sets the content of this buffer from a shared memory region.
+ *
+ * @param src The source shared memory region.
+ * @param dimensions Updated dimensional information. If the dimensions of the IBuffer object
+ * are not fully specified, then the dimensions must be fully specified here.
+ * If the dimensions of the IBuffer object are fully specified, then the
+ * dimensions may be empty here. If dimensions.size() > 0, then all dimensions
+ * must be specified here, and any dimension that was specified in the IBuffer
+ * object must have the same value here.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ * - INVALID_ARGUMENT if provided memory is invalid, or if the dimensions is invalid
+ */
+ void copyFrom(in Memory src, in int[] dimensions);
+
+ /**
+ * Retrieves the content of this buffer to a shared memory region.
+ *
+ * The IBuffer object must have been initialized before the call to IBuffer::copyTo. For more
+ * information on the state of the IBuffer object, refer to IDevice::allocate.
+ *
+ * @param dst The destination shared memory region.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if the IBuffer object is uninitialized, or there is an unspecified
+ * error
+ * - INVALID_ARGUMENT if provided memory is invalid
+ */
+ void copyTo(in Memory dst);
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/IDevice.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/IDevice.aidl
new file mode 100644
index 0000000..0c4954c
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/IDevice.aidl
@@ -0,0 +1,431 @@
+/*
+ * Copyright (C) 2020 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;
+
+import android.hardware.neuralnetworks.BufferDesc;
+import android.hardware.neuralnetworks.BufferRole;
+import android.hardware.neuralnetworks.Capabilities;
+import android.hardware.neuralnetworks.DeviceBuffer;
+import android.hardware.neuralnetworks.DeviceType;
+import android.hardware.neuralnetworks.ExecutionPreference;
+import android.hardware.neuralnetworks.Extension;
+import android.hardware.neuralnetworks.IPreparedModel;
+import android.hardware.neuralnetworks.IPreparedModelCallback;
+import android.hardware.neuralnetworks.IPreparedModelParcel;
+import android.hardware.neuralnetworks.Model;
+import android.hardware.neuralnetworks.NumberOfCacheFiles;
+import android.hardware.neuralnetworks.Priority;
+
+/**
+ * This interface represents a device driver.
+ */
+@VintfStability
+interface IDevice {
+ /**
+ * The byte size of the cache token.
+ */
+ const int BYTE_SIZE_OF_CACHE_TOKEN = 32;
+ /**
+ * The maximum number of files for each type of cache in compilation caching.
+ */
+ const int MAX_NUMBER_OF_CACHE_FILES = 32;
+
+ /**
+ * Numeric values of extension operand and operation types have the following structure:
+ * - The sign bit is always 0.
+ * - 15 high bits represent the "prefix", which corresponds uniquely to the extension name.
+ * - 16 low bits represent the type ID within the extension.
+ */
+ const int EXTENSION_TYPE_HIGH_BITS_PREFIX = 15;
+ const int EXTENSION_TYPE_LOW_BITS_TYPE = 16;
+ /**
+ * OperandType with any value above {@link IDevice::OPERAND_TYPE_BASE_MAX} must be interpreted
+ * as an extension type according to {@link Model::extensionNameToPrefix}.
+ */
+ const int OPERAND_TYPE_BASE_MAX = 0xFFFF;
+ /**
+ * OperationType with any value above {@link IDevice::OPERATION_TYPE_BASE_MAX} must be
+ * interpreted as an extension type according to {@link Model::extensionNameToPrefix}.
+ */
+ const int OPERATION_TYPE_BASE_MAX = 0xFFFF;
+
+ /**
+ * Allocates a driver-managed buffer with the properties specified by the buffer descriptor as
+ * well as the input and output roles.
+ *
+ * The allocate function must verify its inputs are correct. If there is an error, or if a
+ * certain role or property is not supported by the driver, the allocate function must return a
+ * service specific exception with an appropriate ErrorStatus. If the allocation is successful,
+ * this method must return a DeviceBuffer object with the produced IBuffer and a positive token
+ * identifying the allocated buffer. A successful allocation must accommodate all of the
+ * specified roles and buffer properties.
+ *
+ * The buffer is allocated to an uninitialized state. An uninitialized buffer may only be used
+ * in ways that are specified by outputRoles. A buffer is initialized after it is used as an
+ * output in a successful execution, or after a successful invocation of IBuffer::copyFrom on
+ * the buffer. An initialized buffer may be used according to all roles specified in inputRoles
+ * and outputRoles. A buffer will return to the uninitialized state if it is used as an output
+ * in a failed execution, or after a failed invocation of IBuffer::copyFrom on the buffer.
+ *
+ * The dimensions of the buffer can be deduced from the buffer descriptor as well as the
+ * dimensions of the corresponding model operands of the input and output roles. The dimensions
+ * or rank of the buffer may be unknown at this stage. As such, some driver services may only
+ * create a placeholder and defer the actual allocation until execution time. Note that the same
+ * buffer may be used for different shapes of outputs on different executions. When the buffer
+ * is used as an input, the input shape must be the same as the output shape from the last
+ * execution using this buffer as an output.
+ *
+ * The driver must apply proper validatation upon every usage of the buffer, and must fail the
+ * execution immediately if the usage is illegal.
+ *
+ * @param desc A buffer descriptor specifying the properties of the buffer to allocate.
+ * @param preparedModels A vector of IPreparedModel objects. Must only contain IPreparedModel
+ * objects from the same IDevice as this method is being invoked on.
+ * @param inputRoles A vector of roles with each specifying an input to a prepared model.
+ * @param outputRoles A vector of roles with each specifying an output to a prepared model. Each
+ * role specified in inputRoles and outputRoles must be unique. The
+ * corresponding model operands of the roles must have the same OperandType,
+ * scale, zero point, and ExtraParams. The dimensions of the operands and the
+ * dimensions specified in the buffer descriptor must be compatible with each
+ * other. Two dimensions are incompatible if there is at least one axis that
+ * is fully specified in both but has different values.
+ * @return DeviceBuffer object containing the allocated IBuffer object and a positive token that
+ * can be used to reference the buffer as one of the memory pools.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if a certain buffer property or a certain role is not supported,
+ * or if there is an unspecified error
+ * - INVALID_ARGUMENT if one of the input arguments is invalid
+ * - RESOURCE_EXHAUSTED_* if the task was aborted by the driver
+ */
+ DeviceBuffer allocate(in BufferDesc desc, in IPreparedModelParcel[] preparedModels,
+ in BufferRole[] inputRoles, in BufferRole[] outputRoles);
+
+ /**
+ * Gets the capabilities of a driver.
+ *
+ * @return Capabilities of the driver.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ */
+ Capabilities getCapabilities();
+
+ /**
+ * Gets the caching requirements of the driver implementation.
+ *
+ * There are two types of cache file descriptors provided to the driver: model cache and data
+ * cache.
+ *
+ * The data cache is for caching constant data, possibly including preprocessed and transformed
+ * tensor buffers. Any modification to the data cache should have no worse effect than
+ * generating bad output values at execution time.
+ *
+ * The model cache is for caching security-sensitive data such as compiled executable machine
+ * code in the device's native binary format. A modification to the model cache may affect the
+ * driver's execution behavior, and a malicious client could make use of this to execute beyond
+ * the granted permission. Thus, the driver must always check whether the model cache is
+ * corrupted before preparing the model from cache.
+ *
+ * getNumberOfCacheFilesNeeded returns how many of each type of cache files the driver
+ * implementation needs to cache a single prepared model. Returning 0 for both types indicates
+ * compilation caching is not supported by this driver. The driver may still choose not to cache
+ * certain compiled models even if it reports that caching is supported.
+ *
+ * If the device reports that caching is not supported, the user may avoid calling
+ * IDevice::prepareModelFromCache or providing cache file descriptors to
+ * IDevice::prepareModel.
+ *
+ * @return NumberOfCacheFiles structure indicating how many files for model and data cache the
+ * driver needs to cache a single prepared model. It must be less than or equal to
+ * MAX_NUMBER_OF_CACHE_FILES.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ */
+ NumberOfCacheFiles getNumberOfCacheFilesNeeded();
+
+ /**
+ * Gets information about extensions supported by the driver implementation.
+ *
+ * All extension operations and operands must be fully supported for the extension to appear in
+ * the list of supported extensions.
+ *
+ * @return A list of supported extensions.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ */
+ Extension[] getSupportedExtensions();
+
+ /**
+ * Gets the supported operations in a model.
+ *
+ * getSupportedOperations indicates which operations of the top-level subgraph are fully
+ * supported by the vendor driver. If an operation may not be supported for any reason,
+ * getSupportedOperations must return false for that operation.
+ *
+ * The {@link OperationType::IF} and {@link OperationType::WHILE} operations may only be fully
+ * supported if the vendor driver fully supports all operations in the referenced subgraphs.
+ *
+ * @param model A model whose operations -- and their corresponding operands -- are to be
+ * verified by the driver.
+ * @return 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 in the main subgraph.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ * - INVALID_ARGUMENT if provided model is invalid
+ */
+ boolean[] getSupportedOperations(in Model model);
+
+ /**
+ * Get the type of a given device.
+ *
+ * The device type can be used to help application developers to distribute Machine Learning
+ * workloads and other workloads such as graphical rendering. E.g., for an app which renders AR
+ * scenes based on real time object detection results, the developer could choose an ACCELERATOR
+ * type device for ML workloads, and reserve GPU for graphical rendering.
+ *
+ * @return The DeviceType of the device. Please note, this is not a bitfield of DeviceTypes.
+ * Each device must only be of a single DeviceType.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if the query resulted in an unspecified error
+ */
+ DeviceType getType();
+
+ /**
+ * Get the version string of the driver implementation.
+ *
+ * The version string must be a unique token among the set of version strings of drivers of a
+ * specific device. The token identifies the device driver's implementation. The token must not
+ * be confused with the feature level which is solely defined by the interface version. This API
+ * is opaque to the Android framework, but the Android framework may use the information for
+ * debugging or to pass on to NNAPI applications.
+ *
+ * Application developers sometimes have specific requirements to ensure good user experiences,
+ * and they need more information to make intelligent decisions when the Android framework
+ * cannot. For example, combined with the device name and other information, the token can help
+ * NNAPI applications filter devices based on their needs:
+ * - An application demands a certain level of performance, but a specific version of the
+ * driver cannot meet that requirement because of a performance regression.
+ * The application can disallow the driver based on the version provided.
+ * - An application has a minimum precision requirement, but certain versions of
+ * the driver cannot meet that requirement because of bugs or certain optimizations.
+ * The application can filter out versions of these drivers.
+ *
+ * @return The version string of the device implementation. Must have nonzero length.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if the query resulted in an unspecified error
+ */
+ String getVersionString();
+
+ /**
+ * 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 descriptors provided to the driver: model cache
+ * and data cache. For more information on the two types of cache, 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 a status with a service specific exception 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. If the
+ * asynchronous task fails to launch, prepareModel must immediately invoke the callback with
+ * ErrorStatus::GENERAL_FAILURE and nullptr for the IPreparedModel, then return a service
+ * specific exception 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.
+ *
+ * The model is prepared with a priority. This priority is relative to other prepared models
+ * owned by the same client. Higher priority executions may use more compute resources than
+ * lower priority executions, and may preempt or starve lower priority executions.
+ *
+ * prepareModel can be called with an optional deadline. If the model is not able to be prepared
+ * before the provided deadline, the model preparation may be aborted, and either
+ * {@link ErrorStatus::MISSED_DEADLINE_TRANSIENT} or {@link
+ * ErrorStatus::MISSED_DEADLINE_PERSISTENT} may be returned. The error due to an abort must be
+ * sent the same way as other errors, described above.
+ *
+ * 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 priority The priority of the prepared model relative to other prepared models owned by
+ * the client.
+ * @param deadline The time by which the model is expected to be prepared. The time is measured
+ * in nanoseconds since epoch of the steady clock (as from
+ * std::chrono::steady_clock). If the model cannot be prepared by the deadline,
+ * the preparation may be aborted. Passing -1 means the deadline is omitted.
+ * Other negative values are invalid.
+ * @param modelCache A vector of file descriptors 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 file descriptors will be provided in
+ * the same order when retrieving the preparedModel from cache files with
+ * prepareModelFromCache.
+ * @param dataCache A vector of file descriptors 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 file descriptors will be provided in the same order when retrieving
+ * the preparedModel from cache files with prepareModelFromCache.
+ * @param token A caching token of length 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.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - 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
+ * - MISSED_DEADLINE_* if the preparation is aborted because the model cannot be prepared by
+ * the deadline
+ * - RESOURCE_EXHAUSTED_* if the task was aborted by the driver
+ */
+ void prepareModel(in Model model, in ExecutionPreference preference, in Priority priority,
+ in long deadline, in ParcelFileDescriptor[] modelCache, in ParcelFileDescriptor[] dataCache,
+ in byte[] token, in IPreparedModelCallback callback);
+
+ /**
+ * Creates a prepared model from cache files for execution.
+ *
+ * prepareModelFromCache is used to retrieve a prepared model directly from cache files to avoid
+ * slow model compilation time. There are two types of cache file descriptors provided to the
+ * driver: model cache and data cache. For more information on the two types of cache files,
+ * 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 prepareModelFromCache
+ * function must verify the inputs to the prepareModelFromCache function are correct, and that
+ * the security-sensitive cache has not been modified since it was last written by the driver.
+ * If there is an error, or if compilation caching is not supported, or if the
+ * security-sensitive cache has been modified, prepareModelFromCache must immediately invoke the
+ * callback with the appropriate ErrorStatus value and nullptr for the IPreparedModel, then
+ * return a status with a service specific exception with the same ErrorStatus. If the inputs to
+ * the prepareModelFromCache function are valid, the security-sensitive cache is not modified,
+ * and there is no error, prepareModelFromCache must launch an asynchronous task to prepare the
+ * model in the background, and immediately return from prepareModelFromCache. If the
+ * asynchronous task fails to launch, prepareModelFromCache must immediately invoke the callback
+ * with ErrorStatus::GENERAL_FAILURE and nullptr for the IPreparedModel, then return a service
+ * specific exception 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 prepareModelFromCache. 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.
+ *
+ * prepareModelFromCache can be called with an optional deadline. If the model is not able to
+ * prepared before the provided deadline, the model preparation may be aborted, and either
+ * {@link ErrorStatus::MISSED_DEADLINE_TRANSIENT} or
+ * {@link ErrorStatus::MISSED_DEADLINE_PERSISTENT} may be returned. The error due to an abort
+ * must be sent the same way as other errors, described above.
+ *
+ * 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.
+ *
+ * @param deadline The time by which the model is expected to be prepared. The time is measured
+ * in nanoseconds since epoch of the steady clock (as from
+ * std::chrono::steady_clock). If the model cannot be prepared by the deadline,
+ * the preparation may be aborted. Passing -1 means the deadline is omitted.
+ * Other negative values are invalid.
+ * @param modelCache A vector of file descriptors for the security-sensitive cache. The length
+ * of the vector must match the numModelCache returned from
+ * getNumberOfCacheFilesNeeded. The cache file descriptors will be provided in
+ * the same order as with prepareModel.
+ * @param dataCache A vector of file descriptors for the constants' cache. The length of the
+ * vector must match the numDataCache returned from
+ * getNumberOfCacheFilesNeeded. The cache file descriptors will be provided in
+ * the same order as with prepareModel.
+ * @param token A caching token of length BYTE_SIZE_OF_CACHE_TOKEN identifying the prepared
+ * model. It is the same token provided when saving the cache files with
+ * prepareModel. 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.
+ * @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.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if caching is not supported or if there is an unspecified error
+ * - INVALID_ARGUMENT if one of the input arguments is invalid
+ * - MISSED_DEADLINE_* if the preparation is aborted because the model cannot be prepared by
+ * the deadline
+ * - RESOURCE_EXHAUSTED_* if the task was aborted by the driver
+ */
+ void prepareModelFromCache(in long deadline, in ParcelFileDescriptor[] modelCache,
+ in ParcelFileDescriptor[] dataCache, in byte[] token, in IPreparedModelCallback callback);
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/IFencedExecutionCallback.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/IFencedExecutionCallback.aidl
new file mode 100644
index 0000000..47e5916
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/IFencedExecutionCallback.aidl
@@ -0,0 +1,56 @@
+/*
+ * Copyright (C) 2020 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;
+
+import android.hardware.neuralnetworks.ErrorStatus;
+import android.hardware.neuralnetworks.Timing;
+
+/**
+ * IFencedExecutionCallback can be used to query the error status result and duration information
+ * from an IPreparedModel::executeFenced call.
+ */
+@VintfStability
+interface IFencedExecutionCallback {
+ /**
+ * The getExecutionInfo method is used by the clients to query error status result and duration
+ * information. The method must only be called after the actual evaluation has finished or
+ * resulted in an runtime error, as indicated by the status of the sync fence returned by the
+ * IPreparedModel::executeFenced call, otherwise GENERAL_FAILURE must be returned.
+ *
+ * @param out timingLaunched The duration starts when executeFenced is called and ends when
+ * executeFenced signals the returned syncFence. Unless measureTiming
+ * was set to true when launching the execution and status is NONE,
+ * all times must be reported as -1. A driver may choose to report any
+ * time as -1, indicating that particular measurement is not
+ * available.
+ * @param out timingFenced The duration starts when all waitFor sync fences have been signaled
+ * and ends when executeFenced signals the returned syncFence. Unless
+ * measureTiming was set to true when launching the execution and status
+ * is NONE, all times must be reported as -1. A driver may choose to
+ * report any time as -1, indicating that particular measurement is not
+ * available.
+ * @return Error status returned from the asynchronously dispatched execution must be:
+ * - NONE if the asynchronous execution was successful
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if the asynchronous task resulted in an unspecified error
+ * - MISSED_DEADLINE_* if the execution is aborted because it cannot be completed by the
+ * deadline
+ * - RESOURCE_EXHAUSTED_* if the task was aborted by the driver
+ */
+ ErrorStatus getExecutionInfo(out Timing timingLaunched, out Timing timingFenced);
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/IPreparedModel.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/IPreparedModel.aidl
new file mode 100644
index 0000000..c1b2992
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/IPreparedModel.aidl
@@ -0,0 +1,173 @@
+/*
+ * Copyright (C) 2020 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;
+
+import android.hardware.common.NativeHandle;
+import android.hardware.neuralnetworks.ErrorStatus;
+import android.hardware.neuralnetworks.ExecutionResult;
+import android.hardware.neuralnetworks.IFencedExecutionCallback;
+import android.hardware.neuralnetworks.Request;
+
+/**
+ * IPreparedModel describes a model that has been prepared for execution and is used to launch
+ * executions.
+ */
+@VintfStability
+interface IPreparedModel {
+ /**
+ * Each {@link OperationType::WHILE} operation in the model has an implicit execution timeout
+ * duration associated with it ("loop timeout duration"). This duration is configurable on a
+ * per-execution basis and must not exceed 15 seconds. The default value is 2 seconds. The units
+ * are nanoseconds.
+ */
+ const long DEFAULT_LOOP_TIMEOUT_DURATION_NS = 2000000000;
+ const long MAXIMUM_LOOP_TIMEOUT_DURATION_NS = 15000000000;
+
+ /**
+ * Performs a synchronous execution on a prepared model.
+ *
+ * The execution is performed synchronously with respect to the caller. executeSynchronously
+ * must verify the inputs to the function are correct, and the usages of memory pools allocated
+ * by IDevice::allocate are valid. If there is an error, executeSynchronously must immediately
+ * return a service specific exception with the appropriate ErrorStatus value. If the inputs to
+ * the function are valid and there is no error, executeSynchronously must perform the
+ * execution, and must not return until the execution is complete.
+ *
+ * The caller must not change the content of any data object referenced by 'request' (described
+ * by the {@link DataLocation} of a {@link RequestArgument}) until executeSynchronously returns.
+ * executeSynchronously must not change the content of any of the data objects corresponding to
+ * 'request' inputs.
+ *
+ * If the prepared model was prepared from a model wherein all tensor operands have fully
+ * specified dimensions, and the inputs to the function are valid, and at execution time every
+ * operation's input operands have legal values, then the execution should complete
+ * successfully: there must be no failure unless the device itself is in a bad state.
+ *
+ * executeSynchronously may be called with an optional deadline. If the execution is not able to
+ * be completed before the provided deadline, the execution may be aborted, and either
+ * {@link ErrorStatus::MISSED_DEADLINE_TRANSIENT} or {@link
+ * ErrorStatus::MISSED_DEADLINE_PERSISTENT} may be returned. The error due to an abort must be
+ * sent the same way as other errors, described above.
+ *
+ * Any number of calls to the execute* functions, in any combination, may be made concurrently,
+ * even on the same IPreparedModel object.
+ *
+ * @param request The input and output information on which the prepared model is to be
+ * executed.
+ * @param measure Specifies whether or not to measure duration of the execution. The duration
+ * runs from the time the driver sees the call to the executeSynchronously
+ * function to the time the driver returns from the function.
+ * @param deadline The time by which the execution is expected to complete. The time is measured
+ * in nanoseconds since epoch of the steady clock (as from
+ * std::chrono::steady_clock). If the execution cannot be finished by the
+ * deadline, the execution may be aborted. Passing -1 means the deadline is
+ * omitted. Other negative values are invalid.
+ * @param loopTimeoutDuration The maximum amount of time in nanoseconds that should be spent
+ * executing a {@link OperationType::WHILE} operation. If a loop
+ * condition model does not output false within this duration, the
+ * execution must be aborted. If -1 is provided, the maximum amount
+ * of time is {@link DEFAULT_LOOP_TIMEOUT_DURATION_NS}. Other
+ * negative values are invalid. When provided, the duration must not
+ * exceed {@link MAXIMUM_LOOP_TIMEOUT_DURATION_NS}.
+ * @return ExecutionResult parcelable, containing the status of the execution, output shapes and
+ * timing information.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ * - INVALID_ARGUMENT if one of the input arguments is invalid
+ * - MISSED_DEADLINE_* if the execution is aborted because it cannot be completed by the
+ * deadline
+ * - RESOURCE_EXHAUSTED_* if the task was aborted by the driver
+ */
+ ExecutionResult executeSynchronously(in Request request, in boolean measureTiming,
+ in long deadline, in long loopTimeoutDuration);
+
+ /**
+ * Launch a fenced asynchronous execution on a prepared model.
+ *
+ * The execution is performed asynchronously with respect to the caller. executeFenced must
+ * verify the inputs to the function are correct, and the usages of memory pools allocated by
+ * IDevice::allocate are valid. If there is an error, executeFenced must immediately return a
+ * service specific exception with the corresponding ErrorStatus. If the inputs to the function
+ * are valid and there is no error, executeFenced must dispatch an asynchronous task to perform
+ * the execution in the background, assign a sync fence that will be signaled once the execution
+ * is completed and immediately return a callback that can be used by the client to query the
+ * duration and runtime error status. If the task has finished before the call returns,
+ * syncFence file descriptor may be set to -1. The execution must wait for all the sync fences
+ * (if any) in waitFor to be signaled before starting the actual execution.
+ *
+ * When the asynchronous task has finished its execution, it must immediately signal the
+ * syncFence returned from the executeFenced call. After the syncFence is signaled, the task
+ * must not modify the content of any data object referenced by 'request' (described by the
+ * {@link DataLocation} of a {@link RequestArgument}).
+ *
+ * executeFenced may be called with an optional deadline and an optional duration. If the
+ * execution is not able to be completed before the provided deadline or within the timeout
+ * duration (measured from when all sync fences in waitFor are signaled), whichever comes
+ * earlier, the execution may be aborted, and either
+ * {@link ErrorStatus::MISSED_DEADLINE_TRANSIENT} or {@link
+ * ErrorStatus::MISSED_DEADLINE_PERSISTENT} may be returned. The error due to an abort must be
+ * sent the same way as other errors, described above.
+ *
+ * If any of the sync fences in waitFor changes to error status after the executeFenced call
+ * succeeds, or the execution is aborted because it cannot finish before the deadline has been
+ * reached or the duration has elapsed, the driver must immediately set the returned syncFence
+ * to error status.
+ *
+ * Any number of calls to the execute* functions, in any combination, may be made concurrently,
+ * even on the same IPreparedModel object.
+ *
+ * @param request The input and output information on which the prepared model is to be
+ * executed. The outputs in the request must have fully specified dimensions.
+ * @param waitFor A vector of sync fence file descriptors. Execution must not start until all
+ * sync fences have been signaled.
+ * @param measure Specifies whether or not to measure duration of the execution.
+ * @param deadline The time by which the execution is expected to complete. The time is measured
+ * in nanoseconds since epoch of the steady clock (as from
+ * std::chrono::steady_clock).If the execution cannot be finished by the
+ * deadline, the execution may be aborted. Passing -1 means the deadline is
+ * omitted. Other negative values are invalid.
+ * @param loopTimeoutDuration The maximum amount of time in nanoseconds that should be spent
+ * executing a {@link OperationType::WHILE} operation. If a loop
+ * condition model does not output false within this duration, the
+ * execution must be aborted. If -1 is provided, the maximum amount
+ * of time is {@link DEFAULT_LOOP_TIMEOUT_DURATION_NS}. Other
+ * negative values are invalid. When provided, the duration must not
+ * exceed {@link MAXIMUM_LOOP_TIMEOUT_DURATION_NS}.
+ * @param duration The length of time in nanoseconds within which the execution is expected to
+ * complete after all sync fences in waitFor are signaled. If the execution
+ * cannot be finished within the duration, the execution may be aborted. Passing
+ * -1 means the duration is omitted. Other negative values are invalid.
+ * @param out syncFence The sync fence that will be signaled when the task is completed. The
+ * sync fence will be set to error if a critical error, e.g. hardware
+ * failure or kernel panic, occurs when doing execution.
+ * @return The IFencedExecutionCallback can be used to query information like duration and error
+ * status when the execution is completed.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ * - INVALID_ARGUMENT if one of the input arguments is invalid, including fences in error
+ * states.
+ * - MISSED_DEADLINE_* if the execution is aborted because it cannot be completed by the
+ * deadline
+ * - RESOURCE_EXHAUSTED_* if the task was aborted by the driver
+ */
+ IFencedExecutionCallback executeFenced(in Request request, in ParcelFileDescriptor[] waitFor,
+ in boolean measureTiming, in long deadline, in long loopTimeoutDuration, in long duration,
+ out @nullable ParcelFileDescriptor syncFence);
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/IPreparedModelCallback.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/IPreparedModelCallback.aidl
new file mode 100644
index 0000000..adb4218
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/IPreparedModelCallback.aidl
@@ -0,0 +1,51 @@
+/*
+ * Copyright (C) 2020 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;
+
+import android.hardware.neuralnetworks.ErrorStatus;
+import android.hardware.neuralnetworks.IPreparedModel;
+
+/**
+ * IPreparedModelCallback must be used to return a prepared model produced by an asynchronous task
+ * launched from IDevice::prepareModel*.
+ */
+@VintfStability
+interface IPreparedModelCallback {
+ /**
+ * Notify must be invoked immediately after the asynchronous task holding this callback has
+ * finished preparing the model. If the model was successfully prepared, the method must be
+ * invoked with ErrorStatus::NONE and the prepared model. If the model was not able to be
+ * successfully prepared, the method must be invoked with the appropriate ErrorStatus and
+ * nullptr as the IPreparedModel. If the asynchronous task holding this callback fails to launch
+ * or if the model provided to IDevice::prepareModel is invalid, notify method must be invoked
+ * with the appropriate error as well as nullptr for the IPreparedModel.
+ *
+ * @param status Error status returned from the asynchronous model preparation task; must be:
+ * - NONE if the asynchronous task successfully prepared the model
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if the asynchronous task resulted in an unspecified error
+ * - INVALID_ARGUMENT if one of the input arguments to prepareModel is invalid
+ * - MISSED_DEADLINE_* if the preparation is aborted because the model cannot be
+ * prepared by the deadline
+ * - RESOURCE_EXHAUSTED_* if the task was aborted by the driver
+ * @param preparedModel A model that has been asynchronously prepared for execution. If the
+ * model was unable to be prepared due to an error, nullptr must be passed
+ * in place of the IPreparedModel object.
+ */
+ void notify(in ErrorStatus status, in IPreparedModel preparedModel);
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/IPreparedModelParcel.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/IPreparedModelParcel.aidl
new file mode 100644
index 0000000..f198c3f
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/IPreparedModelParcel.aidl
@@ -0,0 +1,28 @@
+/*
+ * Copyright (C) 2020 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;
+
+import android.hardware.neuralnetworks.IPreparedModel;
+
+/**
+ * A parcelable for passing a vector of IPreparedModel objects.
+ */
+@VintfStability
+parcelable IPreparedModelParcel {
+ IPreparedModel preparedModel;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/Memory.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/Memory.aidl
new file mode 100644
index 0000000..8ecb067
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/Memory.aidl
@@ -0,0 +1,31 @@
+/*
+ * Copyright (C) 2020 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;
+import android.hardware.common.NativeHandle;
+
+import android.os.ParcelFileDescriptor;
+
+/**
+ * A type that is used to pass pieces of shared memory between processes.
+ * The type structure mimics hidl_memory type from HIDL.
+ */
+@VintfStability
+parcelable Memory {
+ NativeHandle handle;
+ long size;
+ String name;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/Model.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/Model.aidl
new file mode 100644
index 0000000..3bb7318
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/Model.aidl
@@ -0,0 +1,70 @@
+/*
+ * Copyright (C) 2020 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;
+
+import android.hardware.neuralnetworks.ExtensionNameAndPrefix;
+import android.hardware.neuralnetworks.Subgraph;
+import android.hardware.neuralnetworks.Memory;
+
+/**
+ * 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.
+ */
+@VintfStability
+parcelable Model {
+ /**
+ * The top-level subgraph.
+ */
+ Subgraph main;
+ /**
+ * Referenced subgraphs.
+ *
+ * Each subgraph is referenced by the main subgraph or at least one other referenced subgraph.
+ *
+ * There must be no reference cycles.
+ */
+ Subgraph[] referenced;
+ /**
+ * 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.
+ */
+ byte[] 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_POOL.
+ */
+ 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.
+ */
+ boolean relaxComputationFloat32toFloat16;
+ /**
+ * The mapping between extension names and prefixes of operand and operation type values.
+ */
+ ExtensionNameAndPrefix[] extensionNameToPrefix;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/NumberOfCacheFiles.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/NumberOfCacheFiles.aidl
new file mode 100644
index 0000000..1ca2676
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/NumberOfCacheFiles.aidl
@@ -0,0 +1,27 @@
+/*
+ * Copyright (C) 2020 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;
+
+/**
+ * Structure indicating how many files for model and numDataCache cache the driver needs to cache a
+ * single prepared model.
+ */
+@VintfStability
+parcelable NumberOfCacheFiles {
+ int numModelCache;
+ int numDataCache;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/Operand.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/Operand.aidl
new file mode 100644
index 0000000..243a89d
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/Operand.aidl
@@ -0,0 +1,113 @@
+/*
+ * Copyright (C) 2020 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;
+
+import android.hardware.neuralnetworks.DataLocation;
+import android.hardware.neuralnetworks.OperandExtraParams;
+import android.hardware.neuralnetworks.OperandLifeTime;
+import android.hardware.neuralnetworks.OperandType;
+
+/**
+ * Describes one operand of the model's graph.
+ */
+@VintfStability
+parcelable Operand {
+ /**
+ * The data type.
+ *
+ * Besides the values listed in {@link OperandType}, any value above
+ * {@link IDevice::OPERAND_TYPE_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 or from the corresponding {@link OperationType::IF} or {@link OperationType::WHILE}
+ * operation input operand dimensions in the case of referenced subgraph input operands.
+ *
+ * In the following situations, a tensor operand's dimensions must be fully specified:
+ *
+ * . The operand has lifetime CONSTANT_COPY or CONSTANT_POOL.
+ *
+ * . The operand has lifetime SUBGRAPH_INPUT and belongs to the main subgraph. 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.
+ */
+ int[] dimensions;
+ /**
+ * Quantized scale of the operand.
+ *
+ * Must be 0 when not applicable to an operand type.
+ *
+ * See {@link OperandType}.
+ */
+ float scale;
+ /**
+ * Quantized zero-point offset of the operand.
+ *
+ * Must be 0 when not applicable to an operand type.
+ *
+ * See {@link OperandType}.
+ */
+ int zeroPoint;
+ /**
+ * How the operand is used.
+ */
+ OperandLifeTime lifetime;
+ /**
+ * Where to find the data for this operand.
+ * If the lifetime is TEMPORARY_VARIABLE, SUBGRAPH_INPUT, SUBGRAPH_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_POOL:
+ * - location.poolIndex is set.
+ * - location.offset is the offset in bytes into the specified pool.
+ * - location.length is set.
+ * If the lifetime is SUBGRAPH:
+ * - location.poolIndex is 0.
+ * - location.offset is the index of the referenced subgraph in {@link Model::referenced}.
+ * - location.length is 0.
+ */
+ DataLocation location;
+ /**
+ * Additional parameters specific to a particular operand type.
+ */
+ @nullable OperandExtraParams extraParams;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandExtraParams.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandExtraParams.aidl
new file mode 100644
index 0000000..b0112ae
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandExtraParams.aidl
@@ -0,0 +1,40 @@
+/*
+ * Copyright (C) 2020 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;
+
+import android.hardware.neuralnetworks.SymmPerChannelQuantParams;
+
+/**
+ * Parameters specific to a particular operand type.
+ */
+@VintfStability
+union OperandExtraParams {
+ /**
+ * 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.
+ */
+ byte[] extension;
+}
\ No newline at end of file
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandLifeTime.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandLifeTime.aidl
new file mode 100644
index 0000000..63d1971
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandLifeTime.aidl
@@ -0,0 +1,63 @@
+/*
+ * Copyright (C) 2020 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;
+
+/**
+ * How an operand is used.
+ */
+@VintfStability
+@Backing(type="int")
+enum OperandLifeTime {
+ /**
+ * 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 a subgraph. It must not be an output operand of any operation.
+ *
+ * An operand can't be both input and output of a subgraph.
+ */
+ SUBGRAPH_INPUT,
+ /**
+ * The operand is an output of a subgraph. It must be an output operand of exactly one
+ * operation.
+ *
+ * An operand can't be both input and output of a subgraph.
+ */
+ SUBGRAPH_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_POOL,
+ /**
+ * The operand does not have a value. This is valid only for optional arguments of operations.
+ */
+ NO_VALUE,
+ /**
+ * The operand is a reference to a subgraph. It must be an input to one or more
+ * {@link OperationType::IF} or {@link OperationType::WHILE} operations.
+ */
+ SUBGRAPH,
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandPerformance.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandPerformance.aidl
new file mode 100644
index 0000000..9a8c2cc
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandPerformance.aidl
@@ -0,0 +1,31 @@
+/*
+ * Copyright (C) 2020 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;
+
+import android.hardware.neuralnetworks.OperandType;
+import android.hardware.neuralnetworks.PerformanceInfo;
+
+/**
+ * Driver performance when operating on a particular data type. In the case of float32 data, this is
+ * used when the calculations are not relaxed.
+ */
+@VintfStability
+parcelable OperandPerformance {
+ OperandType type;
+ PerformanceInfo info;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandType.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandType.aidl
new file mode 100644
index 0000000..9274b6f
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandType.aidl
@@ -0,0 +1,154 @@
+/*
+ * Copyright (C) 2020 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;
+
+/**
+ * Operand types.
+ *
+ * The type of an operand in a model.
+ *
+ * Types prefaced with TENSOR_* must be used for tensor data (i.e., tensors
+ * with at least one dimension). Types not prefaced by TENSOR_* represent
+ * scalar values and must have no dimensions.
+ */
+@VintfStability
+@Backing(type="int")
+enum OperandType {
+ /**
+ * A 32 bit floating point scalar value.
+ */
+ FLOAT32 = 0,
+ /**
+ * A signed 32 bit integer scalar value.
+ */
+ INT32 = 1,
+ /**
+ * An unsigned 32 bit integer scalar value.
+ */
+ UINT32 = 2,
+ /**
+ * A tensor of 32 bit floating point values.
+ */
+ TENSOR_FLOAT32 = 3,
+ /**
+ * A tensor of 32 bit integer values.
+ */
+ TENSOR_INT32 = 4,
+ /**
+ * 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:
+ * - scale: a 32 bit floating point value greater than zero.
+ * - zeroPoint: a 32 bit integer, in range [0, 255].
+ *
+ * The formula is:
+ * real_value = (integer_value - zeroPoint) * scale.
+ */
+ TENSOR_QUANT8_ASYMM = 5,
+ /**
+ * An 8 bit boolean scalar value.
+ *
+ * Values of this operand type are either true or false. A zero value represents false; any
+ * other value represents true.
+ */
+ BOOL = 6,
+ /**
+ * A tensor of 16 bit signed integers that represent real numbers.
+ *
+ * Attached to this tensor is a number representing real value scale that is used to convert the
+ * 16 bit number to a real value in the following way:
+ * realValue = integerValue * scale.
+ *
+ * scale is a 32 bit floating point with value greater than zero.
+ */
+ TENSOR_QUANT16_SYMM = 7,
+ /**
+ * A tensor of IEEE 754 16 bit floating point values.
+ */
+ TENSOR_FLOAT16 = 8,
+ /**
+ * A tensor of 8 bit boolean values.
+ *
+ * Values of this operand type are either true or false. A zero value represents false; any
+ * other value represents true.
+ */
+ TENSOR_BOOL8 = 9,
+ /**
+ * An IEEE 754 16 bit floating point scalar value.
+ */
+ FLOAT16 = 10,
+ /**
+ * A tensor of 8 bit signed integers that represent real numbers.
+ *
+ * This tensor is associated with additional fields that can be used to convert the 8 bit signed
+ * integer to the real value and vice versa. These fields are:
+ * - channelDim: a 32 bit unsigned integer indicating channel dimension.
+ * - 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.
+ */
+ TENSOR_QUANT8_SYMM_PER_CHANNEL = 11,
+ /**
+ * A tensor of 16 bit unsigned integers that represent real numbers.
+ *
+ * Attached to this tensor are two numbers that can be used to convert the 16 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 [0, 65535].
+ *
+ * The formula is:
+ * real_value = (integer_value - zeroPoint) * scale.
+ */
+ TENSOR_QUANT16_ASYMM = 12,
+ /**
+ * A tensor of 8 bit signed integers that represent real numbers.
+ *
+ * Attached to this tensor is a number representing real value scale that is used to convert the
+ * 8 bit number to a real value in the following way:
+ * realValue = integerValue * scale.
+ *
+ * scale is a 32 bit floating point with value greater than zero.
+ */
+ TENSOR_QUANT8_SYMM = 13,
+ /**
+ * 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,
+ /**
+ * A reference to a subgraph.
+ *
+ * Must have the lifetime {@link OperandLifeTime::SUBGRAPH}.
+ */
+ SUBGRAPH = 15,
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/Operation.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/Operation.aidl
new file mode 100644
index 0000000..acfb4b7
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/Operation.aidl
@@ -0,0 +1,46 @@
+/*
+ * Copyright (C) 2020 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;
+
+import android.hardware.neuralnetworks.OperationType;
+
+/**
+ * Describes one operation of the model's graph.
+ */
+@VintfStability
+parcelable Operation {
+ /**
+ * The operation type.
+ *
+ * Besides the values listed in {@link OperationType}, any value above
+ * {@link IDevice::OPERATION_TYPE_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.
+ */
+ int[] inputs;
+ /**
+ * Describes the table that contains the indexes of the outputs of the operation. The offset is
+ * the index in the operandIndexes table.
+ */
+ int[] outputs;
+}
+
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/OperationType.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperationType.aidl
new file mode 100644
index 0000000..fd9da67
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperationType.aidl
@@ -0,0 +1,5132 @@
+/*
+ * Copyright (C) 2020 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;
+
+/**
+ * Operation types.
+ *
+ * The type of an operation in a model.
+ */
+@VintfStability
+@Backing(type="int")
+enum OperationType {
+ /**
+ * Adds two tensors, element-wise.
+ *
+ * Takes two input tensors of identical {@link OperandType} and compatible
+ * dimensions. The output is the sum of both input tensors, optionally
+ * modified by an activation function.
+ *
+ * Two dimensions are compatible when:
+ * 1. they are equal, or
+ * 2. one of them is 1
+ *
+ * The size of the output is the maximum size along each dimension of the
+ * input operands. It starts with the trailing dimensions, and works its
+ * way forward.
+ *
+ * Example:
+ *
+ * input1.dimension = {4, 1, 2}
+ * input2.dimension = {5, 4, 3, 1}
+ * output.dimension = {5, 4, 3, 2}
+ *
+ * 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}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ * * {@link OperandType::TENSOR_INT32} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+ * as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} 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.
+ * For a {@link OperandType::TENSOR_INT32} tensor,
+ * the {@link FusedActivationFunc} must be "NONE".
+ *
+ * Outputs:
+ * * 0: The sum, a tensor of the same {@link OperandType} as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
+ */
+ ADD = 0,
+ /**
+ * Performs a 2-D average pooling operation.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, i, j, channel] =
+ * sum_{di, dj}(
+ * input[b, strides[1] * i + di, strides[2] * j + dj, channel]
+ * ) / sum(1)
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * 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 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
+ * the right, in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * 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 HAL version 1.2.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * 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)}.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 6: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * 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 HAL version 1.2.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ AVERAGE_POOL_2D = 1,
+ /**
+ * Concatenates the input tensors along the given dimension.
+ *
+ * The input tensors must have identical {@link OperandType} and the same
+ * dimensions except the dimension along the concatenation axis.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@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 input section)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0 ~ n-1: The list of n input tensors, of shape
+ * [D0, D1, ..., Daxis(i), ..., Dm].
+ * 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.
+ * Input tensors of
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ * are allowed to have different scale and zeroPoint.
+ * 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].
+ * 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.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint values can be different from input tensors.
+ */
+ CONCATENATION = 2,
+ /**
+ * 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
+ * appropriate size.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, i, j, channel] =
+ * sum_{di, dj, k} (
+ * input[b, strides[1] * i + di, strides[2] * j + dj, k] *
+ * filter[channel, di, dj, k]
+ * ) + bias[channel]
+ *
+ * Supported tensor {@link OperandType} configurations:
+ * * 32 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
+ *
+ * * 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).
+ *
+ * Available since HAL version 1.2:
+ * * 16 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
+ *
+ * * Quantized with symmetric per channel quantization for the filter:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
+ * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+ * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+ * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+ *
+ * Available since HAL version 1.3:
+ * * Quantized signed (since HAL version 1.3):
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, filter, and output.
+ * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+ * * * input.scale * filter.scale).
+ *
+ * * Quantized signed with filter symmetric per channel quantization (since HAL version 1.3):
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, and output.
+ * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+ * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+ * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+ *
+ * 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_in],
+ * 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 (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 type.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * 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
+ * bias_scale[i] = input_scale * filter_scale[i].
+ * * 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
+ * the right, in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * 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 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 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 HAL version 1.2.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * 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 (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
+ * type.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * 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
+ * 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
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * 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 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 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 HAL version 1.2.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [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 = 3,
+ /**
+ * Performs a depthwise 2-D convolution operation.
+ *
+ * Given an input tensor of shape [batches, height, width, depth_in] and a
+ * filter tensor of shape [1, filter_height, filter_width, depth_out]
+ * containing depth_out convolutional filters of depth 1, DEPTHWISE_CONV
+ * applies a different filter to each input channel (expanding from 1
+ * channel to channel_multiplier channels for each), then concatenates the
+ * results together.
+ *
+ * The output has depth_out = depth_in * depth_multiplier channels.
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, i, j, k * channel_multiplier + q] =
+ * sum_{di, dj} (
+ * input[b, strides[1] * i + di, strides[2] * j + dj, k] *
+ * filter[1, di, dj, k * channel_multiplier + q]
+ * ) + bias[k * channel_multiplier + q]
+ *
+ * Supported tensor {@link OperandType} configurations:
+ * * 32 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
+ *
+ * * 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).
+ *
+ * Available since HAL version 1.2:
+ * * 16 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
+ *
+ * * Quantized with symmetric per channel quantization for the filter:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
+ * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+ * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+ * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+ *
+ * Available since HAL version 1.3:
+ * * Quantized signed (since HAL version 1.3):
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, filter, and output.
+ * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+ * * * input.scale * filter.scale).
+ *
+ * * Quantized signed with filter symmetric per channel quantization (since HAL version 1.3):
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, and output.
+ * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+ * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+ * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+ *
+ * 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_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 (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 type.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * 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
+ * bias_scale[i] = input_scale * filter_scale[i].
+ * * 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
+ * the right, in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 9: An {@link OperandType::INT32} scalar, specifying the depthwise
+ * multiplier.
+ * * 10: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * 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 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 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 HAL version 1.2.
+ *
+ * Inputs (implicit padding):
+ * * 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.
+ * * 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 type.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * 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
+ * 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
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the depthwise
+ * multiplier.
+ * * 7: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * 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 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 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 HAL version 1.2.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [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
+ */
+ DEPTHWISE_CONV_2D = 4,
+ /**
+ * Rearranges data from depth into blocks of spatial data.
+ *
+ * More specifically, this op outputs a copy of the input tensor where
+ * values from the depth dimension are moved in spatial blocks to the height
+ * and width dimensions. The value block_size indicates the input block size
+ * and how the data is moved.
+ *
+ * Chunks of data of size block_size * block_size from depth are rearranged
+ * into non-overlapping blocks of size block_size x block_size.
+ *
+ * The width of the output tensor is input_depth * block_size, whereas the
+ * height is input_height * block_size. The depth of the input tensor must
+ * be divisible by block_size * block_size
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * 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: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the block_size.
+ * block_size must be >=1 and block_size * block_size must be a divisor
+ * 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 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)].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ DEPTH_TO_SPACE = 5,
+ /**
+ * Dequantizes the input tensor.
+ *
+ * The formula is:
+ *
+ * output = (input - zeroPoint) * scale.
+ *
+ * Supported input tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_SYMM} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported output tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}.
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * Since HAL version 1.2, this tensor may be zero-sized.
+ *
+ * Outputs:
+ * * 0: A tensor with the same shape as input0.
+ */
+ DEQUANTIZE = 6,
+ /**
+ * Looks up sub-tensors in the input tensor.
+ *
+ * This operator takes for input a tensor of values (Values) and
+ * a one-dimensional tensor of selection indices (Lookups).
+ * The output tensor is the concatenation of sub-tensors of Values as
+ * selected by Lookups.
+ *
+ * Think of Values as being sliced along its first dimension:
+ * The entries in Lookups select which slices are concatenated together
+ * to create the output tensor.
+ *
+ * For example, if Values has shape of [40, 200, 300] and
+ * Lookups has shape of [3], all three values found in Lookups are
+ * expected to be between 0 and 39. The resulting tensor must
+ * have shape of [3, 200, 300].
+ *
+ * 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_FLOAT16} (since HAL version 1.3)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * 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.
+ * * 1: Values. An n-D tensor, where n >= 2, from which sub-tensors are
+ * extracted.
+ *
+ * Output:
+ * * 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.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input1.
+ */
+ EMBEDDING_LOOKUP = 7,
+ /**
+ * Computes element-wise floor() on the input tensor.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor, of the same {@link OperandType} and dimensions as
+ * the input tensor.
+ */
+ FLOOR = 8,
+ /**
+ * Denotes a fully (densely) connected layer, which connects all elements
+ * in the input tensor with each element in the output tensor.
+ *
+ * This layer implements the operation:
+ *
+ * outputs = activation(inputs * weights’ + bias)
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor of at least rank 2, specifying the input. If rank is
+ * greater than 2, then it gets flattened to a 2-D Tensor. The
+ * (flattened) 2-D Tensor is reshaped (if necessary) to
+ * [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 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.
+ * * 2: A 1-D tensor, of shape [num_units], 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}
+ * and {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * 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, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ *
+ * Outputs:
+ * * 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 = 9,
+ /**
+ * Looks up sub-tensors in the input tensor using a key-value map.
+ *
+ * This operator takes for input a tensor of values (Values),
+ * a one-dimensional tensor of selection values (Lookups) and
+ * a one-dimensional tensor that maps these values to Values
+ * indexes. The output tensor is the concatenation of sub-tensors of
+ * Values as selected by Lookups via Keys.
+ *
+ * Think of Values as being sliced along its outer-most dimension.
+ * The output is a concatenation of selected slices, with one slice
+ * for each entry of Lookups. The slice selected is the one at the
+ * same index as the Maps entry that matches the value in Lookups.
+ *
+ * For a hit, the corresponding sub-tensor of Values is included
+ * in the Output tensor. For a miss, the corresponding sub-tensor in
+ * Output must have zero values.
+ *
+ * For example, if Values has shape of [40, 200, 300],
+ * Keys should have a shape of [40]. If Lookups tensor has shape
+ * of [3], three slices are being concatenated, so the resulting tensor
+ * must have the shape of [3, 200, 300]. If the first entry in Lookups
+ * has the value 123456, that value must be located in Keys tensor.
+ * If the sixth entry of Keys contains 123456, the sixth slice of Values
+ * 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 ].
+ * * 1: Keys. A 1-D {@link OperandType::TENSOR_INT32} tensor with shape
+ * [ n ]; Keys and Values pair represent a map, i.e., the ith element
+ * in Keys (Keys[i]) is the key to select the ith sub-tensor in Values
+ * (Values[i]), where 0 <= i <= n-1. Keys tensor *MUST* be sorted in
+ * ascending order.
+ * * 2: Values. A tensor with shape of [ n, … ]; i.e., the first dimension
+ * must be n.
+ *
+ * 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.
+ */
+ HASHTABLE_LOOKUP = 10,
+ /**
+ * Applies L2 normalization along the axis dimension.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[batch, row, col, channel] =
+ * input[batch, row, col, channel] /
+ * sqrt(sum_{c} pow(input[batch, row, col, c], 2))
+ *
+ * By default the axis dimension is the last dimension of the input tensor.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ * 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.
+ * * 1: An optional {@link OperandType::INT32} scalar, default to -1,
+ * 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 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.
+ * For {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * the scale must be 1.f / 128 and the zeroPoint must be 0.
+ *
+ * NOTE: Before HAL version 1.3, if the elements along an axis are all zeros,
+ * the result is undefined. Since HAL version 1.3, if the elements along an axis
+ * are all zeros, the result is logical zero.
+ */
+ L2_NORMALIZATION = 11,
+ /**
+ * Performs an 2-D L2 pooling operation.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, i, j, c] =
+ * sqrt(sum_{di, dj} pow(input[b, strides[1] * i + di, strides[2] * j + dj, c], 2) /
+ * sum(1))
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@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 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
+ * the right, in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * 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 HAL version 1.2.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * 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)}.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 6: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * 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 HAL version 1.2.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth].
+ */
+ L2_POOL_2D = 12,
+ /**
+ * Applies Local Response Normalization along the depth dimension.
+ *
+ * The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the
+ * last dimension), and each vector is normalized independently. Within a
+ * given vector, each component is divided by the weighted, squared sum of
+ * inputs within depth_radius.
+ *
+ * The output is calculated using this formula:
+ *
+ * sqr_sum[a, b, c, d] = sum(
+ * pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2))
+ * output = input / pow((bias + alpha * sqr_sum), beta)
+ *
+ * For input tensor with rank less than 4, independently normalizes each
+ * 1-D slice along specified dimension.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@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 HAL version 1.2.
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the radius of
+ * the normalization window.
+ * * 2: A scalar, specifying the bias, must not be zero.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT16}, the bias
+ * value must be of {@link OperandType::FLOAT16}.
+ * 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}.
+ * * 4: A scalar, specifying the exponent, beta.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT16}, the beta
+ * value must be of {@link OperandType::FLOAT16}.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the beta
+ * value must be of {@link OperandType::FLOAT32}.
+ * * 5: An optional {@link OperandType::INT32} scalar, default to -1,
+ * 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 HAL version 1.2.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ LOCAL_RESPONSE_NORMALIZATION = 13,
+ /**
+ * Computes sigmoid activation on the input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = 1 / (1 + exp(-input))
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 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.
+ * For {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * the scale must be 1.f / 256 and the zeroPoint must be -128.
+ */
+ LOGISTIC = 14,
+ /**
+ * Projects an input to a bit vector via locality senstive hashing.
+ *
+ * Supported input tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@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]: 15 of hash functions.
+ * Tensor[0].Dim[1]: 16 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]
+ * * 3: Type:
+ * Sparse:
+ * 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.
+ *
+ * NOTE: To avoid collisions across hash functions, an offset value
+ * of k * (1 << Tensor[0].Dim[1]) will be added to each signature,
+ * where k is the index of the hash function.
+ *
+ * Value LSHProjectionType_SPARSE_DEPRECATED(=1).
+ * Legacy behavior that does not include the offset value.
+ *
+ * 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.
+ * The offset value for sparse projections was added in HAL version 1.2.
+ */
+ LSH_PROJECTION = 15,
+ /**
+ * Performs a single time step in a Long Short-Term Memory (LSTM) layer
+ *
+ * The LSTM operation is described by the following equations.
+ *
+ * \f{eqnarray*}{
+ * i_t =& \sigma(W_{xi}x_t+W_{hi}h_{t-1}+W_{ci}C_{t-1}+b_i) & \\
+ * f_t =& \sigma(W_{xf}x_t+W_{hf}h_{t-1}+W_{cf}C_{t-1}+b_f) & \\
+ * C_t =& clip(f_t \odot C_{t-1} + i_t \odot
+ * g(W_{xc}x_t+W_{hc}h_{t-1}+b_c),\ t_{cell}) & \\
+ * o_t =& \sigma(W_{xo}x_t+W_{ho}h_{t-1}+W_{co}C_t+b_o) & \\
+ * & & \\
+ * & clip(W_{proj}(o_t \odot g(C_t))+b_{proj},\ t_{proj})
+ * & if\ there\ is\ a\ projection; \\
+ * h_t =& & \\
+ * & o_t \odot g(C_t) & otherwise. \\
+ * \f}
+ * Where:
+ * * \f$x_t\f$ is the input,
+ * * \f$i_t\f$ is the input gate,
+ * * \f$f_t\f$ is the forget gate,
+ * * \f$C_t\f$ is the cell state,
+ * * \f$o_t\f$ is the output,
+ * * \f$h_t\f$ is the output state,
+ * * \f$\sigma\f$ is the logistic sigmoid function,
+ * * \f$g\f$ is the cell input and cell output activation function, usually
+ * \f$tahn\f$,
+ * * \f$W_{xi}\f$ is the input-to-input weight matrix,
+ * * \f$W_{hi}\f$ is the recurrent to input weight matrix,
+ * * \f$W_{ci}\f$ is the cell-to-input weight matrix,
+ * * \f$b_i\f$ is the input gate bias,
+ * * \f$W_{xf}\f$ is the input-to-forget weight matrix,
+ * * \f$W_{hf}\f$ is the recurrent-to-forget weight matrix,
+ * * \f$W_{cf}\f$ is the cell-to-forget weight matrix,
+ * * \f$b_f\f$ is the forget gate bias,
+ * * \f$W_{xc}\f$ is the input-to-cell weight matrix,
+ * * \f$W_{hc}\f$ is the recurrent-to-cell weight matrix,
+ * * \f$b_c\f$ is the cell bias,
+ * * \f$W_{xo}\f$ is the input-to-output weight matrix,
+ * * \f$W_{ho}\f$ is the recurrent-to-output weight matrix,
+ * * \f$W_{co}\f$ is the cell-to-output weight matrix,
+ * * \f$b_o\f$ is the output gate bias,
+ * * \f$W_{proj}\f$ is the projection weight matrix,
+ * * \f$b_{proj}\f$ is the projection bias,
+ * * \f$t_{cell}\f$ is the threshold for clipping the cell state, and
+ * * \f$t_{proj}\f$ is the threshold for clipping the projected output.
+ * * \f$\odot\f$ is the
+ * <a href="https://en.wikipedia.org/wiki/Hadamard_product_(matrices)">
+ * Hadamard product</a> that takes two matrices and produces another
+ * matrix, each element of which is the product of the corresponding
+ * elements of the input matrices.
+ *
+ * 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
+ * https://arxiv.org/pdf/1607.06450.pdf
+ *
+ * The operation has the following independently optional inputs:
+ * * The cell-to-input weights (\f$W_{ci}\f$), cell-to-forget weights
+ * (\f$W_{cf}\f$) and cell-to-output weights (\f$W_{co}\f$) either all
+ * have values or neither of them have values (i.e., all set to null). If
+ * they have values, the peephole optimization is used.
+ * * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights
+ * (\f$W_{hi}\f$) and input gate bias (\f$b_i\f$) either all have values,
+ * or none of them have values. If they have no values, coupling of input
+ * and forget gates (CIFG) is used, in which case the input gate
+ * (\f$i_t\f$) is calculated using the following equation instead.
+ * \f{eqnarray*}{
+ * i_t = 1 - f_t
+ * \f}
+ * In case peephole optimization is used and CIFG is not used
+ * cell-to-input (\f$W_{ci}\f$) weights must be present. Otherwise, the
+ * cell-to-input weights must have no value.
+ * * The projection weights (\f$W_{proj}\f$) is required only for the
+ * recurrent projection layer, and should otherwise have no value.
+ * * 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.
+ * * (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
+ * values. Layer normalization is used when the values of all the layer
+ * normalization weights are present.
+ *
+ * References:
+ *
+ * The default non-peephole non-CIFG implementation is based on:
+ * http://www.bioinf.jku.at/publications/older/2604.pdf
+ * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural
+ * Computation, 9(8):1735-1780, 1997.
+ *
+ * The peephole implementation and projection layer is based on:
+ * https://research.google.com/pubs/archive/43905.pdf
+ * Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory
+ * recurrent neural network architectures for large scale acoustic
+ * modeling." INTERSPEECH, 2014.
+ * (However, the concept of peephole optimization was introduced in work
+ * prior to this paper.)
+ *
+ * The coupling of input and forget gate (CIFG) is based on:
+ * http://arxiv.org/pdf/1503.04069.pdf
+ * Greff et al. "LSTM: A Search Space Odyssey"
+ *
+ * The layer normalization is based on:
+ * https://arxiv.org/pdf/1607.06450.pdf
+ * Jimmy Ba et al. "Layer Normalization"
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@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 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 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 shape [num_units, input_size].
+ * * 3: The input-to-cell weights (\f$W_{xc}\f$).
+ * 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 shape [num_units, input_size].
+ * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
+ * 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 shape [num_units, output_size].
+ * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
+ * 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 shape [num_units, output_size].
+ * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 12:The input gate bias (\f$b_i\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 13:The forget gate bias (\f$b_f\f$).
+ * A 1-D tensor of shape [num_units].
+ * * 14:The cell bias (\f$b_c\f$).
+ * A 1-D tensor of shape [num_units].
+ * * 15:The output gate bias (\f$b_o\f$).
+ * A 1-D tensor of shape [num_units].
+ * * 16:The projection weights (\f$W_{proj}\f$). Optional.
+ * A 2-D tensor of shape [output_size, num_units].
+ * * 17:The projection bias (\f$b_{proj}\f$). Optional.
+ * A 1-D tensor of shape [output_size].
+ * * 18:The output state (in) (\f$h_{t-1}\f$).
+ * 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 shape [batch_size, num_units].
+ * * 20:The activation function (\f$g\f$).
+ * A value indicating the activation function:
+ * <ul>
+ * <li>0: None;
+ * <li>1: Relu;
+ * <li>3: Relu6;
+ * <li>4: Tanh;
+ * <li>6: Sigmoid.
+ * </ul>
+ * * 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 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
+ * 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 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
+ * 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.
+ * * 24:The forget layer normalization weights.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at forget gate.
+ * * 25:The cell layer normalization weights.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at cell gate.
+ * * 26:The output layer normalization weights.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at output gate.
+ *
+ * Outputs:
+ * * 0: The scratch buffer.
+ * 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 shape [batch_size, output_size].
+ * * 2: The cell state (out) (\f$C_t\f$).
+ * A 2-D tensor of shape [batch_size, num_units].
+ * * 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.
+ */
+ LSTM = 16,
+ /**
+ * Performs an 2-D max pooling operation.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, i, j, channel] =
+ * max_{di, dj} (
+ * input[b, strides[1] * i + di, strides[2] * j + dj, channel]
+ * )
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * 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 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
+ * the right, in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * 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 HAL version 1.2.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * 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)}.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 6: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * 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 HAL version 1.2.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ MAX_POOL_2D = 17,
+ /**
+ * Multiplies two tensors, element-wise.
+ *
+ * Takes two input tensors of identical {@link OperandType} and compatible
+ * dimensions. The output is the product of both input tensors, optionally
+ * modified by an activation function.
+ *
+ * Two dimensions are compatible when:
+ * 1. they are equal, or
+ * 2. one of them is 1
+ *
+ * The size of the resulting output is the maximum size along each dimension
+ * of the input operands. It starts with the trailing dimensions, and works
+ * its way forward.
+ *
+ * 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}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ * * {@link OperandType::TENSOR_INT32} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+ * as input0.
+ * * 2: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * For a {@link OperandType::TENSOR_INT32} tensor,
+ * the {@link FusedActivationFunc} must be "NONE".
+ *
+ * Outputs:
+ * * 0: The product, a tensor of the same {@link OperandType} as input0.
+ * For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * the following condition must be satisfied:
+ * output_scale > input1_scale * input2_scale.
+ */
+ MUL = 18,
+ /**
+ * Computes rectified linear activation on the input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = max(0, input)
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 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 a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ RELU = 19,
+ /**
+ * Computes rectified linear 1 activation on the input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = min(1.f, max(-1.f, input))
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ * Since HAL version 1.2, this tensor may be zero-sized.
+ *
+ * Outputs:
+ * * 0: The output tensor of the same shape as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ RELU1 = 20,
+ /**
+ * Computes rectified linear 6 activation on the input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = min(6, max(0, input))
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 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 a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ RELU6 = 21,
+ /**
+ * Reshapes a tensor.
+ *
+ * Given tensor, this operation returns a tensor that has the same values as
+ * tensor, but with a newly specified shape.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the tensor to be reshaped.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}, defining the
+ * shape of the output tensor. The number of elements implied by shape
+ * must be the same as the number of elements in the input tensor.
+ *
+ * If one component of shape is the special value -1, the size of that
+ * dimension is computed so that the total size remains constant. In
+ * particular, a shape of [-1] flattens into 1-D. At most one component
+ * of shape can be -1.
+ *
+ * Outputs:
+ * * 0: The output tensor, of shape specified by the input shape.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ RESHAPE = 22,
+ /**
+ * Resizes images to given size using the bilinear interpretation.
+ *
+ * Resized images must be distorted if their output aspect ratio is not the
+ * same as input aspect ratio. The corner pixels of output may not be the
+ * same as corner pixels of input.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * 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 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 HAL version 1.2.
+ * * 4: Align corners. An optional {@link OperandType::BOOL}
+ * scalar, default to false. If True, the centers of the 4 corner
+ * pixels of the input and output tensors are aligned, preserving the
+ * values at the corner pixels.
+ * Available since HAL version 1.3.
+ * * 5: Half pixel centers. An optional {@link OperandType::BOOL}
+ * scalar, default to false. If True, the pixel centers are assumed to
+ * be at (0.5, 0.5). This is the default behavior of image.resize in
+ * TF 2.0. If this parameter is True, then align_corners parameter
+ * must be False.
+ * Available since HAL version 1.3.
+ *
+ * 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
+ * dimension from the input tensor to the output tensor. The output
+ * width is calculated as new_width = floor(width * width_scale).
+ * The scalar must be of {@link OperandType::FLOAT16} if input0 is
+ * of {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} otherwise.
+ * * 2: A scalar, specifying height_scale, the scaling factor of the height
+ * dimension from the input tensor to the output tensor. The output
+ * height is calculated as new_height = floor(height * height_scale).
+ * The scalar must be of {@link OperandType::FLOAT16} if input0 is
+ * of {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} otherwise.
+ * * 3: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * * 4: Align corners. An optional {@link OperandType::BOOL}
+ * scalar, default to false. If True, the centers of the 4 corner
+ * pixels of the input and output tensors are aligned, preserving the
+ * values at the corner pixels.
+ * Available since HAL version 1.3.
+ * * 5: Half pixel centers. An optional {@link OperandType::BOOL}
+ * scalar, default to false. If True, the pixel centers are assumed to
+ * be at (0.5, 0.5). This is the default behavior of image.resize in
+ * TF 2.0. If this parameter is True, then align_corners parameter
+ * must be False.
+ * Available since HAL version 1.3.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, new_height, new_width, depth].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ RESIZE_BILINEAR = 23,
+ /**
+ * A basic recurrent neural network layer.
+ *
+ * This layer implements the operation:
+ * outputs = state = activation(inputs * input_weights +
+ * state * recurrent_weights + bias)
+ *
+ * Where:
+ * * “input_weights” is a weight matrix that multiplies the inputs;
+ * * “recurrent_weights” is a weight matrix that multiplies the current
+ * “state” which itself is the output from the previous time step
+ * computation;
+ * * “bias” is a bias vector (added to each output vector in the batch);
+ * * “activation” is the function passed as the “fused_activation_function”
+ * argument (if not “NONE”).
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * The input tensors must all be the same type.
+ *
+ * Inputs:
+ * * 0: 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 shape [num_units, input_size], where “num_units”
+ * corresponds to the number of units.
+ * * 2: recurrent_weights.
+ * 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 shape [num_units].
+ * * 4: hidden state (in).
+ * 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
+ * linear activation.
+ *
+ * Outputs:
+ * * 0: hidden state (out).
+ * A 2-D tensor of shape [batch_size, num_units].
+ *
+ * * 1: output.
+ * A 2-D tensor of shape [batch_size, num_units]. This is effectively
+ * the same as the current state value.
+ */
+ RNN = 24,
+ /**
+ * Computes the softmax activation on the input tensor element-wise, per
+ * batch, by normalizing the input vector so the maximum coefficient is
+ * zero.
+ *
+ * The output is calculated using this formula:
+ *
+ * output[batch, i] =
+ * 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_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4.
+ * 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 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},
+ * {@link OperandType::TENSOR_QUANT8_ASYMM} or
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}, 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}.
+ * * 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 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.
+ * For {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * the scale must be 1.f / 256 and the zeroPoint must be -128.
+ */
+ SOFTMAX = 25,
+ /**
+ * Rearranges blocks of spatial data, into depth.
+ *
+ * More specifically, this op outputs a copy of the input tensor where
+ * values from the height and width dimensions are moved to the depth
+ * dimension. The value block_size indicates the input block size and how
+ * the data is moved.
+ *
+ * Chunks of data of size block_size * block_size from depth are rearranged
+ * into non-overlapping blocks of size block_size x block_size.
+ *
+ * The depth of the output tensor is input_depth * block_size * block_size.
+ * The input tensor's height and width must be divisible by block_size.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * 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: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the block_size.
+ * block_size must be >=1 and block_size must be a divisor of both the
+ * 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 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].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ SPACE_TO_DEPTH = 26,
+ /**
+ * SVDF op is a kind of stateful layer derived from the notion that a
+ * densely connected layer that's processing a sequence of input frames can
+ * be approximated by using a singular value decomposition of each of its
+ * nodes. The implementation is based on:
+ *
+ * https://research.google.com/pubs/archive/43813.pdf
+ *
+ * P. Nakkiran, R. Alvarez, R. Prabhavalkar, C. Parada.
+ * “Compressing Deep Neural Networks using a Rank-Constrained Topology”.
+ * INTERSPEECH, 2015.
+ *
+ * It processes the incoming input using a 2-stage filtering mechanism:
+ * * stage 1 performs filtering on the "features" dimension, whose outputs
+ * get pushed into a memory of fixed-size memory_size.
+ * * stage 2 performs filtering on the "time" dimension of the memory_size
+ * memoized outputs of stage 1.
+ *
+ * Specifically, for rank 1, this layer implements the operation:
+ *
+ * memory = push(conv1d(inputs, weights_feature, feature_dim,
+ * "PADDING_VALID"));
+ * outputs = activation(memory * weights_time + bias);
+ *
+ * Where:
+ * * “weights_feature” is a weights matrix that processes the inputs (by
+ * convolving the input with every “feature filter”), and whose outputs
+ * get pushed, stacked in order, into the fixed-size “memory” (the oldest
+ * entry gets dropped);
+ * * “weights_time” is a weights matrix that processes the “memory” (by a
+ * batched matrix multiplication on the num_units);
+ * * “bias” is an optional bias vector (added to each output vector in the
+ * batch); and
+ * * “activation” is the function passed as the “fused_activation_function”
+ * argument (if not “NONE”).
+ *
+ * Each rank adds a dimension to the weights matrices by means of stacking
+ * the filters.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * All input tensors must be the same type.
+ *
+ * Inputs:
+ * * 0: 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 shape [num_units, input_size], where “num_units”
+ * corresponds to the number of units.
+ * * 2: weights_time.
+ * 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 shape [num_units].
+ * * 4: state (in).
+ * 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.
+ * An optional {@link FusedActivationFunc} value indicating the
+ * activation function. If “NONE” is specified then it results in a
+ * linear activation.
+ *
+ * Outputs:
+ * * 0: state (out).
+ * 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 the same {@link OperandType} as the inputs, with shape
+ * [batch_size, num_units].
+ */
+ SVDF = 27,
+ /**
+ * Computes hyperbolic tangent of input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = tanh(input)
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 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.
+ * For {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * the scale must be 1.f / 128 and the zeroPoint must be 0.
+ */
+ TANH = 28,
+ /**
+ * BatchToSpace for N-dimensional tensors.
+ *
+ * This operation reshapes the batch dimension (dimension 0) into M + 1
+ * dimensions of shape block_shape + [batch], interleaves these blocks back
+ * into the grid defined by the spatial dimensions [1, ..., M], to obtain a
+ * result with the same rank as the input.
+ *
+ * This is the reverse of SpaceToBatch.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * 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 tensor to be reshaped
+ * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the block
+ * sizes for each spatial dimension of the input tensor. All values
+ * must be >= 1.
+ * * 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.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ BATCH_TO_SPACE_ND = 29,
+ /**
+ * Element-wise division of two tensors.
+ *
+ * Takes two input tensors of identical {@link OperandType} and compatible
+ * dimensions. The output is the result of dividing the first input tensor
+ * by the second, optionally modified by an activation function.
+ *
+ * For inputs of {@link OperandType::TENSOR_INT32}, performs
+ * "floor division" ("//" in Python). For example,
+ * 5 // 2 = 2
+ * -5 // 2 = -3
+ *
+ * Two dimensions are compatible when:
+ * 1. they are equal, or
+ * 2. one of them is 1
+ *
+ * The size of the output is the maximum size along each dimension of the
+ * input operands. It starts with the trailing dimensions, and works its way
+ * forward.
+ *
+ * Example:
+ * input1.dimension = {4, 1, 2}
+ * input2.dimension = {5, 4, 3, 1}
+ * output.dimension = {5, 4, 3, 2}
+ *
+ * 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_INT32} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the first input.
+ * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+ * as input0.
+ * * 2: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * For a {@link OperandType::TENSOR_INT32} tensor,
+ * the {@link FusedActivationFunc} must be "NONE".
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ */
+ DIV = 30,
+ /**
+ * Computes the mean of elements across dimensions of a tensor.
+ *
+ * Reduces the input tensor along the given dimensions to reduce. Unless
+ * keep_dims is true, the rank of the tensor is reduced by 1 for each entry
+ * in axis. If keep_dims is true, the reduced dimensions are retained with
+ * length 1.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. Must be in the range
+ * [-rank(input_tensor), rank(input_tensor)).
+ *
+ * NOTE: When the operation was introduced, the documentation
+ * incorrectly stated that if dimensions were empty, the operation
+ * would reduce across all dimensions. This behavior was never
+ * implemented.
+ *
+ * * 2: An {@link OperandType::INT32} scalar, keep_dims. If positive,
+ * retains reduced dimensions with length 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ * If all dimensions are reduced and keep_dims is false, the output
+ * shape is [1].
+ */
+ MEAN = 31,
+ /**
+ * Pads a tensor.
+ *
+ * This operation pads a tensor according to the specified paddings.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ * (full support since HAL version 1.2, see the output section)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the tensor to be padded.
+ * * 1: A 2-D Tensor of {@link OperandType::TENSOR_INT32}, the paddings
+ * for each spatial dimension of the input tensor. The shape of the
+ * tensor must be {rank(input0), 2}.
+ * padding[i, 0] specifies the number of elements to be padded in the
+ * front of dimension i.
+ * padding[i, 1] specifies the number of elements to be padded after the
+ * end of dimension i.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0. The
+ * output tensor has the same rank as input0, and each
+ * dimension of the output tensor has the same size as the
+ * corresponding dimension of the input tensor plus the size
+ * of the padding:
+ * output0.dimension[i] =
+ * padding[i, 0] + input0.dimension[i] + padding[i, 1]
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ *
+ * 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 = 32,
+ /**
+ * SpaceToBatch for N-Dimensional tensors.
+ *
+ * This operation divides "spatial" dimensions [1, ..., M] of the input into
+ * a grid of blocks of shape block_shape, and interleaves these blocks with
+ * the "batch" dimension (0) such that in the output, the spatial dimensions
+ * [1, ..., M] correspond to the position within the grid, and the batch
+ * dimension combines both the position within a spatial block and the
+ * original batch position. Prior to division into blocks, the spatial
+ * dimensions of the input are optionally zero padded according to paddings.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ * (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.
+ * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the block
+ * sizes for each spatial dimension of the input tensor. All values
+ * must be >= 1.
+ * * 2: A 2-D Tensor of {@link OperandType::TENSOR_INT32}, the paddings
+ * for each spatial dimension of the input tensor. All values must be
+ * >= 0. The shape of the tensor must be {M, 2}, where M is the number
+ * of spatial dimensions.
+ * padding[i, 0] specifies the number of element to be padded in the
+ * front of dimension i.
+ * padding[i, 1] specifies the number of element to be padded after the
+ * 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 HAL version 1.2.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ *
+ * 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 = 33,
+ /**
+ * Removes dimensions of size 1 from the shape of a tensor.
+ *
+ * Given a tensor input, this operation returns a tensor of the same
+ * {@link OperandType} with all dimensions of size 1 removed. If you don't
+ * want to remove all size 1 dimensions, you can remove specific size 1
+ * dimensions by specifying the axes (input1).
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, the tensor to be squeezed.
+ * * 1: An optional 1-D tensor of {@link OperandType::TENSOR_INT32}. The
+ * dimensions to squeeze. If specified only squeezes the dimensions
+ * listed. Otherwise, squeezes all dimensions. The dimension index
+ * starts at 0. An error must be reported if squeezing a dimension that
+ * is not 1.
+ *
+ * Outputs:
+ * * 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.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ * If all input dimensions are equal to 1 and are to be squeezed, the
+ * output shape is [1].
+ */
+ SQUEEZE = 34,
+ /**
+ * Extracts a strided slice of a tensor.
+ *
+ * Roughly speaking, this op extracts a slice of size (end - begin) / stride
+ * from the given input tensor. Starting at the location specified by begin
+ * the slice continues by adding stride to the index until all dimensions
+ * are not less than end. Note that a stride can be negative, which causes a
+ * reverse slice.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the tensor to be sliced.
+ * * 1: begin, a 1-D tensor of {@link OperandType::TENSOR_INT32}. The
+ * starts of the dimensions of the input tensor to be sliced. The
+ * length must be of rank(input0).
+ * * 2: end, a 1-D tensor of {@link OperandType::TENSOR_INT32}. The
+ * ends of the dimensions of the input tensor to be sliced. The length
+ * must be of rank(input0).
+ * * 3: strides, a 1-D tensor of {@link OperandType::TENSOR_INT32}. The
+ * strides of the dimensions of the input tensor to be sliced. The
+ * length must be of rank(input0). The entries must be non-zero.
+ * * 4: begin_mask, an {@link OperandType::INT32} scalar. If the ith bit
+ * of begin_mask is set, begin[i] is ignored and the fullest possible
+ * range in that dimension is used instead.
+ * * 5: end_mask, an {@link OperandType::INT32} scalar. If the ith bit of
+ * end_mask is set, end[i] is ignored and the fullest possible range in
+ * that dimension is used instead.
+ * * 6: shrink_axis_mask, an {@link OperandType::INT32} scalar. If the
+ * ith bit of shrink_axis_mask is set, the ith dimension specification
+ * shrinks the dimensionality by 1, taking on the value at index
+ * begin[i]. In this case, the ith specification must define a
+ * slice of size 1, e.g. begin[i] = x, end[i] = x + 1.
+ *
+ * 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.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ * If shrink_axis_mask is true for all input dimensions, the output
+ * shape is [1].
+ */
+ STRIDED_SLICE = 35,
+ /**
+ * Element-wise subtraction of two tensors.
+ *
+ * Takes two input tensors of identical {@link OperandType} and compatible
+ * dimensions. The output is the result of subtracting the second input
+ * tensor from the first one, optionally modified by an activation function.
+ *
+ * Two dimensions are compatible when:
+ * 1. they are equal, or
+ * 2. one of them is 1
+ *
+ * The size of the output is the maximum size along each dimension of the
+ * input operands. It starts with the trailing dimensions, and works its way
+ * forward.
+ *
+ * Example:
+ * input1.dimension = {4, 1, 2}
+ * input2.dimension = {5, 4, 3, 1}
+ * output.dimension = {5, 4, 3, 2}
+ *
+ * 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} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ * * {@link OperandType::TENSOR_INT32} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the first input.
+ * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+ * as input0.
+ * * 2: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * For a {@link OperandType::TENSOR_INT32} tensor,
+ * the {@link FusedActivationFunc} must be "NONE".
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
+ */
+ SUB = 36,
+ /**
+ * Transposes the input tensor, permuting the dimensions according to the
+ * perm tensor.
+ *
+ * The returned tensor's dimension i corresponds to the input dimension
+ * perm[i]. If perm is not given, it is set to (n-1...0), where n is the
+ * rank of the input tensor. Hence by default, this operation performs a
+ * regular matrix transpose on 2-D input Tensors.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the tensor to be transposed.
+ * 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.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ TRANSPOSE = 37,
+ /**
+ * Computes the absolute value of a tensor, element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ ABS = 38,
+ /**
+ * Returns the index of the largest element along an axis.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: An n-D tensor specifying the input. Must be non-empty.
+ * * 1: An {@link OperandType::INT32} scalar specifying the axis to
+ * reduce across. Negative index is used to specify axis from the
+ * end (e.g. -1 for the last axis). Must be in the range [-n, n).
+ *
+ * Outputs:
+ * * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor.
+ * If input is 1-dimensional, the output shape is [1].
+ */
+ ARGMAX = 39,
+ /**
+ * Returns the index of the smallest element along an axis.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: An n-D tensor specifying the input. Must be non-empty.
+ * * 1: An {@link OperandType::INT32} scalar specifying the axis to
+ * reduce across. Negative index is used to specify axis from the
+ * end (e.g. -1 for the last axis). Must be in the range [-n, n).
+ *
+ * Outputs:
+ * * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor.
+ * If input is 1-dimensional, the output shape is [1].
+ */
+ ARGMIN = 40,
+ /**
+ * Transform axis-aligned bounding box proposals using bounding box deltas.
+ *
+ * Given the positions of bounding box proposals and the corresponding
+ * bounding box deltas for each class, return the refined bounding box
+ * regions. The resulting bounding boxes are cliped against the edges of
+ * the image.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT16_ASYMM}
+ *
+ * Inputs:
+ * * 0: A 2-D Tensor of shape [num_rois, 4], specifying the locations of the
+ * bounding box proposals, each line with format [x1, y1, x2, y2].
+ * For tensor of type {@link OperandType::TENSOR_QUANT16_ASYMM},
+ * the zeroPoint must be 0 and the scale must be 0.125. Zero num_rois
+ * is supported for this tensor.
+ * * 1: A 2-D Tensor of shape [num_rois, num_classes * 4], specifying the
+ * bounding box delta for each region of interest and each class. The
+ * bounding box deltas are organized in the following order
+ * [dx, dy, dw, dh], where dx and dy is the relative correction factor
+ * for the center position of the bounding box with respect to the width
+ * and height, dw and dh is the log-scale relative correction factor
+ * for the width and height. For input0 of type
+ * {@link OperandType::TENSOR_QUANT16_ASYMM}, this tensor should be
+ * of {@link OperandType::TENSOR_QUANT8_ASYMM} or
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}. Zero num_rois is
+ * supported for this tensor.
+ * * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+ * [num_rois], specifying the batch index of each box. Boxes with
+ * the same batch index are grouped together. Zero num_rois is
+ * supported for this tensor.
+ * * 3: A 2-D Tensor of shape [batches, 2], specifying the information of
+ * each image in the batch, each line with format
+ * [image_height, image_width].
+ *
+ * Outputs:
+ * * 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].
+ * 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,
+ /**
+ * A recurrent neural network layer that applies an LSTM cell to a
+ * sequence of inputs in forward and backward directions.
+ *
+ * The op supports cross-linking via an auxiliary input. Regular cell feeds
+ * one input into the two RNN cells in the following way:
+ *
+ * INPUT (INPUT_REVERSED)
+ * | |
+ * ---------------------
+ * | FW_LSTM BW_LSTM |
+ * ---------------------
+ * | |
+ * FW_OUT BW_OUT
+ *
+ * An op with cross-linking takes two inputs and feeds them into the RNN
+ * cells in the following way:
+ *
+ * AUX_INPUT (AUX_INPUT_REVERSED)
+ * | |
+ * INPUT | (INPUT_R'D.)|
+ * | | | |
+ * -----------------------
+ * | \ / \ / |
+ * | FW_LSTM BW_LSTM |
+ * -----------------------
+ * | |
+ * FW_OUT BW_OUT
+ *
+ * The cross-linking mode is enabled iff auxiliary input and auxiliary
+ * weights are present. While stacking this op on top of itself, this
+ * allows to connect both forward and backward outputs from previous cell
+ * to the next cell's input.
+ *
+ * Since HAL version 1.3 parallel linking mode is supported. The mode is
+ * enabled if auxiliary input is present but auxiliary weights are omitted.
+ * In this case, the cell feeds inputs into the RNN in the following way:
+ *
+ * INPUT (AUX_INPUT_REVERSED)
+ * | |
+ * ---------------------
+ * | FW_LSTM BW_LSTM |
+ * ---------------------
+ * | |
+ * FW_OUT BW_OUT
+ *
+ * While stacking this op on top of itself, this allows to connect both
+ * forward and backward outputs from previous cell to the next cell's
+ * corresponding inputs.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: 3, either time-major or batch-major.
+ *
+ * All input and output tensors must be of the same type.
+ *
+ * Inputs:
+ * * 0: The input.
+ * A 3-D tensor of shape:
+ * If time-major: [max_time, batch_size, input_size]
+ * If batch-major: [batch_size, max_time, input_size]
+ * where "max_time" is the number of timesteps (sequence length),
+ * "batch_size" corresponds to the batching dimension, and
+ * "input_size" is the size of the input.
+ * * 1: The forward input-to-input weights. Optional.
+ * A 2-D tensor of shape [fw_num_units, input_size], where “fw_num_units”
+ * corresponds to the number of forward cell units.
+ * * 2: The forward input-to-forget weights.
+ * A 2-D tensor of shape [fw_num_units, input_size].
+ * * 3: The forward input-to-cell weights.
+ * A 2-D tensor of shape [fw_num_units, input_size].
+ * * 4: The forward input-to-output weights.
+ * A 2-D tensor of shape [fw_num_units, input_size].
+ * * 5: The forward recurrent-to-input weights. Optional.
+ * A 2-D tensor of shape [fw_num_units, fw_output_size], where “fw_output_size”
+ * corresponds to either the number of cell units (i.e., fw_num_units),
+ * or the second dimension of the “fw_projection_weights”, if defined.
+ * * 6: The forward recurrent-to-forget weights.
+ * A 2-D tensor of shape [fw_num_units, fw_output_size].
+ * * 7: The forward recurrent-to-cell weights.
+ * A 2-D tensor of shape [fw_num_units, fw_output_size].
+ * * 8: The forward recurrent-to-output weights.
+ * A 2-D tensor of shape [fw_num_units, fw_output_size].
+ * * 9: The forward cell-to-input weights. Optional.
+ * A 1-D tensor of shape [fw_num_units].
+ * * 10: The forward cell-to-forget weights. Optional.
+ * A 1-D tensor of shape [fw_num_units].
+ * * 11: The forward cell-to-output weights. Optional.
+ * A 1-D tensor of shape [fw_num_units].
+ * * 12: The forward input gate bias. Optional.
+ * A 1-D tensor of shape [fw_num_units].
+ * * 13: The forward forget gate bias.
+ * A 1-D tensor of shape [fw_num_units].
+ * * 14: The forward cell gate bias.
+ * A 1-D tensor of shape [fw_num_units].
+ * * 15: The forward output gate bias.
+ * A 1-D tensor of shape [fw_num_units].
+ * * 16: The forward projection weights. Optional.
+ * A 2-D tensor of shape [fw_output_size, fw_num_units].
+ * * 17: The forward projection bias. Optional.
+ * A 1-D tensor of shape [fw_output_size].
+ * * 18: The backward input-to-input weights. Optional.
+ * A 2-D tensor of shape [bw_num_units, input_size], where “bw_num_units”
+ * corresponds to the number of backward cell units.
+ * * 19: The backward input-to-forget weights.
+ * A 2-D tensor of shape [bw_num_units, input_size].
+ * * 20: The backward input-to-cell weights.
+ * A 2-D tensor of shape [bw_num_units, input_size].
+ * * 21: The backward input-to-output weights.
+ * A 2-D tensor of shape [bw_num_units, input_size].
+ * * 22: The backward recurrent-to-input weights. Optional.
+ * A 2-D tensor of shape [bw_num_units, bw_output_size], where “bw_output_size”
+ * corresponds to either the number of cell units (i.e., “bw_num_units”),
+ * or the second dimension of the “bw_projection_weights”, if defined.
+ * * 23: The backward recurrent-to-forget weights.
+ * A 2-D tensor of shape [bw_num_units, bw_output_size].
+ * * 24: The backward recurrent-to-cell weights.
+ * A 2-D tensor of shape [bw_num_units, bw_output_size].
+ * * 25: The backward recurrent-to-output weights.
+ * A 2-D tensor of shape [bw_num_units, bw_output_size].
+ * * 26: The backward cell-to-input weights. Optional.
+ * A 1-D tensor of shape [bw_num_units].
+ * * 27: The backward cell-to-forget weights. Optional.
+ * A 1-D tensor of shape [bw_num_units].
+ * * 28: The backward cell-to-output weights. Optional.
+ * A 1-D tensor of shape [bw_num_units].
+ * * 29: The backward input gate bias. Optional.
+ * A 1-D tensor of shape [bw_num_units].
+ * * 30: The backward forget gate bias.
+ * A 1-D tensor of shape [bw_num_units].
+ * * 31: The backward cell gate bias.
+ * A 1-D tensor of shape [bw_num_units].
+ * * 32: The backward output gate bias.
+ * A 1-D tensor of shape [bw_num_units].
+ * * 33: The backward projection weights. Optional.
+ * A 2-D tensor of shape [bw_output_size, bw_num_units].
+ * * 34: The backward projection bias. Optional.
+ * A 1-D tensor of shape [bw_output_size].
+ * * 35: The forward input activation state.
+ * A 2-D tensor of shape [batch_size, bw_output_size].
+ * * 36: The forward input cell state.
+ * A 2-D tensor of shape [batch_size, bw_num_units].
+ * * 37: The backward input activation state.
+ * A 2-D tensor of shape [batch_size, bw_output_size].
+ * * 38: The backward input cell state.
+ * A 2-D tensor of shape [batch_size, bw_num_units].
+ * * 39: The auxiliary input. Optional.
+ * A 3-D tensor of shape [max_time, batch_size, aux_input_size],
+ * where “batch_size” corresponds to the batching dimension, and
+ * “aux_input_size” is the size of the auxiliary input. Optional. See
+ * the docs above for the usage modes explanation.
+ * * 40: The forward auxiliary input-to-input weights.
+ * Optional. See the docs above for the usage modes explanation.
+ * A 2-D tensor of shape [fw_num_units, aux_input_size].
+ * * 41: The forward auxiliary input-to-forget weights.
+ * Optional. See the docs above for the usage modes explanation.
+ * A 2-D tensor of shape [fw_num_units, aux_input_size].
+ * * 42: The forward auxiliary input-to-cell weights.
+ * Optional. See the docs above for the usage modes explanation.
+ * A 2-D tensor of shape [fw_num_units, aux_input_size].
+ * * 43: The forward auxiliary input-to-output weights.
+ * Optional. See the docs above for the usage modes explanation.
+ * A 2-D tensor of shape [fw_num_units, aux_input_size].
+ * * 44: The backward auxiliary input-to-input weights.
+ * Optional. See the docs above for the usage modes explanation.
+ * A 2-D tensor of shape [bw_num_units, aux_input_size].
+ * * 45: The backward auxiliary input-to-forget weights.
+ * Optional. See the docs above for the usage modes explanation.
+ * A 2-D tensor of shape [bw_num_units, aux_input_size].
+ * * 46: The backward auxiliary input-to-cell weights.
+ * Optional. See the docs above for the usage modes explanation.
+ * A 2-D tensor of shape [bw_num_units, aux_input_size].
+ * * 47: The backward auxiliary input-to-output weights.
+ * Optional. See the docs above for the usage modes explanation.
+ * A 2-D tensor of shape [bw_num_units, aux_input_size].
+ * * 48: The activation function.
+ * A value indicating the activation function:
+ * <ul>
+ * <li>0: None;
+ * <li>1: Relu;
+ * <li>3: Relu6;
+ * <li>4: Tanh;
+ * <li>6: Sigmoid.
+ * </ul>
+ * * 49: The clipping threshold for the cell state, such
+ * that values are bound within [-cell_clip, cell_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 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 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.
+ * * 52: time_major
+ * An {@link OperandType::BOOL} scalar specifying the shape format
+ * of input and output tensors.
+ * * 53: The forward input layer normalization weights. Optional.
+ * A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
+ * to activation at input gate.
+ * * 54: The forward forget layer normalization weights. Optional.
+ * A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
+ * to activation at forget gate.
+ * * 55: The forward cell layer normalization weights. Optional.
+ * A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
+ * to activation at cell gate.
+ * * 56: The forward output layer normalization weights. Optional.
+ * A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
+ * to activation at output gate.
+ * * 57: The backward input layer normalization weights. Optional.
+ * A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
+ * to activation at input gate.
+ * * 58: The backward forget layer normalization weights. Optional.
+ * A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
+ * to activation at forget gate.
+ * * 59: The backward cell layer normalization weights. Optional.
+ * A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
+ * to activation at cell gate.
+ * * 60: The backward output layer normalization weights. Optional.
+ * A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
+ * to activation at output gate.
+ *
+ * Outputs:
+ * * 0: The forward output.
+ * A 3-D tensor of shape:
+ * If time-major and not merge_outputs:
+ * [max_time, batch_size, fw_output_size]
+ * If time-major and merge_outputs:
+ * [max_time, batch_size, fw_output_size + bw_output_size]
+ * If batch-major and not merge_outputs:
+ * [batch_size, max_time, fw_output_size]
+ * If batch-major and merge_outputs:
+ * [batch_size, max_time, fw_output_size + bw_output_size]
+ * * 1: The backward output. Unused if merge_outputs is true.
+ * 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]
+ * * 2: The forward activation state output.
+ * A 2-D tensor of shape [batch_size, fw_output_size] containing an
+ * activation state from the last time step in the sequence. This
+ * output is optional and can be omitted. If this output is present
+ * then outputs 3-5 must be present as well.
+ * Available since HAL version 1.3.
+ * * 3: The forward cell state output.
+ * A tensor of shape [batch_size, fw_cell_size] containing a cell state
+ * from the last time step in the sequence. This output is optional
+ * and can be omitted. If this output is present
+ * then outputs 2, 4, 5 must be present as well.
+ * Available since HAL version 1.3.
+ * * 4: The backward activation state output.
+ * A 2-D tensor of shape [batch_size, bw_output_size] containing an
+ * activation state from the last time step in the sequence. This
+ * output is optional and can be omitted. If this output is present
+ * then outputs 2, 3, 5 must be present as well.
+ * Available since HAL version 1.3.
+ * * 5: The backward cell state output.
+ * A tensor of shape [batch_size, bw_cell_size] containing a cell state
+ * from the last time step in the sequence. This output is optional
+ * and can be omitted. If this output is present
+ * then outputs 2-4 must be present as well.
+ * Available since HAL version 1.3.
+ */
+ BIDIRECTIONAL_SEQUENCE_LSTM = 42,
+ /**
+ * A recurrent neural network layer that applies a basic RNN cell to a
+ * sequence of inputs in forward and backward directions.
+ *
+ * This Op unrolls the input along the sequence dimension, and implements
+ * the following operation for each element in the sequence s =
+ * 1...sequence_length:
+ * fw_outputs[s] = fw_state = activation(inputs[s] * fw_input_weights’ +
+ * fw_state * fw_recurrent_weights’ + fw_bias)
+ *
+ * And for each element in sequence t = sequence_length : 1
+ * bw_outputs[t] = bw_state = activation(inputs[t] * bw_input_weights’ +
+ * bw_state * bw_recurrent_weights’ + bw_bias)
+ *
+ * Where:
+ * * “{fw,bw}_input_weights” is a weight matrix that multiplies the inputs;
+ * * “{fw,bw}_recurrent_weights” is a weight matrix that multiplies the
+ * current “state” which itself is the output from the previous time step
+ * computation;
+ * * “{fw,bw}_bias” is a bias vector (added to each output vector in the
+ * batch);
+ * * “activation” is the function passed as the “fused_activation_function”
+ * argument (if not “NONE”).
+ *
+ * The op supports cross-linking via an auxiliary input. Regular cell feeds
+ * one input into the two RNN cells in the following way:
+ *
+ * INPUT (INPUT_REVERSED)
+ * | |
+ * ---------------------
+ * | FW_RNN BW_RNN |
+ * ---------------------
+ * | |
+ * FW_OUT BW_OUT
+ *
+ * An op with cross-linking takes two inputs and feeds them into the RNN
+ * cells in the following way:
+ *
+ * AUX_INPUT (AUX_INPUT_REVERSED)
+ * | |
+ * INPUT | (INPUT_R'D.)|
+ * | | | |
+ * -----------------------
+ * | \ / \ / |
+ * | FW_RNN BW_RNN |
+ * -----------------------
+ * | |
+ * FW_OUT BW_OUT
+ *
+ * The cross-linking mode is enabled iff auxiliary input and auxiliary
+ * weights are present. While stacking this op on top of itself, this
+ * allows to connect both forward and backward outputs from previous cell
+ * to the next cell's input.
+ *
+ * Since HAL version 1.3 parallel linking mode is supported. The mode is
+ * enabled if auxiliary input is present but auxiliary weights are omitted.
+ * In this case, the cell feeds inputs into the RNN in the following way:
+ *
+ * INPUT (AUX_INPUT_REVERSED)
+ * | |
+ * ---------------------
+ * | FW_RNN BW_RNN |
+ * ---------------------
+ * | |
+ * FW_OUT BW_OUT
+ *
+ * While stacking this op on top of itself, this allows to connect both
+ * forward and backward outputs from previous cell to the next cell's
+ * corresponding inputs.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * The input tensors must all be the same type.
+ *
+ * Inputs:
+ * * 0: input.
+ * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
+ * it is set to true, then the input has a shape [maxTime, batchSize,
+ * inputSize], otherwise the input has a shape [batchSize, maxTime,
+ * inputSize].
+ * * 1: fwWeights.
+ * A 2-D tensor of shape [fwNumUnits, inputSize].
+ * * 2: fwRecurrentWeights.
+ * A 2-D tensor of shape [fwNumUnits, fwNumUnits].
+ * * 3: fwBias.
+ * A 1-D tensor of shape [fwNumUnits].
+ * * 4: fwHiddenState.
+ * A 2-D tensor of shape [batchSize, fwNumUnits]. Specifies a hidden
+ * state input for the first time step of the computation.
+ * * 5: bwWeights.
+ * A 2-D tensor of shape [bwNumUnits, inputSize].
+ * * 6: bwRecurrentWeights.
+ * A 2-D tensor of shape [bwNumUnits, bwNumUnits].
+ * * 7: bwBias.
+ * A 1-D tensor of shape [bwNumUnits].
+ * * 8: bwHiddenState
+ * A 2-D tensor of shape [batchSize, bwNumUnits]. Specifies a hidden
+ * state input for the first time step of the computation.
+ * * 9: auxInput.
+ * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
+ * it is set to true, then the input has a shape [maxTime, batchSize,
+ * auxInputSize], otherwise the input has a shape [batchSize, maxTime,
+ * auxInputSize]. Can be omitted. See the docs above for the usage
+ * modes explanation.
+ * * 10:fwAuxWeights.
+ * A 2-D tensor of shape [fwNumUnits, auxInputSize]. Can be omitted.
+ * See the docs above for the usage modes explanation.
+ * * 11:bwAuxWeights.
+ * A 2-D tensor of shape [bwNumUnits, auxInputSize]. Can be omitted.
+ * See the docs above for the usage modes explanation.
+ * * 12:fusedActivationFunction.
+ * A {@link FusedActivationFunc} value indicating the activation function. If
+ * “NONE” is specified then it results in a linear activation.
+ * * 13:timeMajor
+ * An {@link OperandType::BOOL} scalar specifying the shape format
+ * of input and output tensors.
+ * * 14:mergeOutputs
+ * An {@link OperandType::BOOL} scalar specifying if the outputs
+ * from forward and backward cells are separate (if set to false) or
+ * concatenated (if set to true).
+ * Outputs:
+ * * 0: fwOutput.
+ * A 3-D tensor. The first two dimensions of the shape are defined by
+ * the input 6 (timeMajor) and the third dimension is defined by the
+ * input 14 (mergeOutputs). If timeMajor is set to true, then the first
+ * two dimensions are [maxTime, batchSize], otherwise they are set to
+ * [batchSize, maxTime]. If mergeOutputs is set to true, then the third
+ * dimension is equal to (fwNumUnits + bwNumUnits), otherwise it is set
+ * to fwNumUnits.
+ * * 1: bwOutput.
+ * A 3-D tensor. If the input 14 (mergeOutputs) is set to true, then
+ * this tensor is not produced. The shape is defined by the input 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].
+ * * 2: The forward hidden state output.
+ * A 2-D tensor of shape [batchSize, fwNumUnits] containing a hidden
+ * state from the last time step in the sequence. This output is
+ * optional and can be omitted. If this output is present then output
+ * 3 must be present as well.
+ * Available since HAL version 1.3.
+ * * 3: The backward hidden state output.
+ * A 2-D tensor of shape [batchSize, bwNumUnits] containing a hidden
+ * state from the last time step in the sequence. This output is
+ * optional and can be omitted. If this output is present then output
+ * 2 must be present as well.
+ * Available since HAL version 1.3.
+ */
+ BIDIRECTIONAL_SEQUENCE_RNN = 43,
+ /**
+ * Greedily selects a subset of bounding boxes in descending order of score.
+ *
+ * This op applies NMS algorithm to each class. In each loop of execution,
+ * the box with maximum score gets selected and removed from the pending set.
+ * The scores of the rest of boxes are lowered according to the
+ * intersection-over-union (IOU) overlapping with the previously selected
+ * boxes and a specified NMS kernel method. Any boxes with score less
+ * than a threshold are removed from the pending set.
+ *
+ * Three NMS kernels are supported:
+ * * Hard: score_new = score_old * (1 if IoU < threshold else 0)
+ * * Linear: score_new = score_old * (1 if IoU < threshold else 1 - IoU)
+ * * Gaussian: score_new = score_old * exp(- IoU^2 / sigma)
+ *
+ * Axis-aligned bounding boxes are represented by its upper-left corner
+ * coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid
+ * bounding box should satisfy x1 <= x2 and y1 <= y2.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Inputs:
+ * * 0: A 2-D Tensor of shape [num_rois, num_classes], specifying the score
+ * of each bounding box proposal. The boxes are grouped by batches in the
+ * first dimension. Zero num_rois is supported for this tensor.
+ * * 1: A 2-D Tensor specifying the bounding boxes of shape
+ * [num_rois, num_classes * 4], organized in the order [x1, y1, x2, y2].
+ * The boxes are grouped by batches in the first dimension. The sequential
+ * order of the boxes corresponds with input0. For input0 of type
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}, this tensor should be of
+ * {@link OperandType::TENSOR_QUANT16_ASYMM}, with zeroPoint of 0 and
+ * scale of 0.125.
+ * For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * this tensor should be of {@link OperandType::TENSOR_QUANT16_ASYMM},
+ * with zeroPoint of -128 and scale of 0.125.
+ * Zero num_rois is supported for this tensor.
+ * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+ * [num_rois], specifying the batch index of each box. Boxes with
+ * the same batch index are grouped together.
+ * * 3: An {@link OperandType::FLOAT32} scalar, score_threshold. Boxes
+ * with scores lower than the threshold are filtered before sending
+ * to the NMS algorithm.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the maximum
+ * number of selected bounding boxes for each image. Set to a negative
+ * value for unlimited number of output bounding boxes.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the NMS
+ * kernel method, options are 0:hard, 1:linear, 2:gaussian.
+ * * 6: An {@link OperandType::FLOAT32} scalar, specifying the IoU
+ * threshold in hard and linear NMS kernel. This field is ignored if
+ * gaussian kernel is selected.
+ * * 7: An {@link OperandType::FLOAT32} scalar, specifying the sigma in
+ * gaussian NMS kernel. This field is ignored if gaussian kernel is
+ * not selected.
+ * * 8: An {@link OperandType::FLOAT32} scalar, nms_score_threshold.
+ * Boxes with scores lower than the threshold are dropped during the
+ * score updating phase in soft NMS.
+ *
+ * Outputs:
+ * * 0: A 1-D Tensor of the same {@link OperandType} as input0, with shape
+ * [num_output_rois], specifying the score of each output box. The boxes
+ * are grouped by batches, but the sequential order in each batch is not
+ * guaranteed. For type of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * guaranteed. For type of {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * or {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * the scale and zero point must be the same as input0.
+ * * 1: A 2-D Tensor of the same {@link OperandType} as input1, with shape
+ * [num_output_rois, 4], specifying the coordinates of each
+ * output bounding box with the same format as input1. The sequential
+ * order of the boxes corresponds with output0. For type of
+ * {@link OperandType::TENSOR_QUANT16_ASYMM}, the scale must be
+ * 0.125 and the zero point must be 0.
+ * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+ * [num_output_rois], specifying the class of each output box. The
+ * sequential order of the boxes corresponds with output0.
+ * * 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.
+ */
+ BOX_WITH_NMS_LIMIT = 44,
+ /**
+ * Casts a tensor to a type.
+ *
+ * This operation ignores the scale and zeroPoint of quanized tensors,
+ * e.g. it treats a {@link OperandType::TENSOR_QUANT8_ASYMM} input
+ * as a tensor of uint8 values.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Since HAL version 1.3, casting tensors of the following
+ * {@link OperandType} to the same {@link OperandType} is supported:
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT16_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT16_SYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ * * {@link OperandType::TENSOR_QUANT8_SYMM}
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: A tensor with the same shape as input0.
+ */
+ CAST = 45,
+ /**
+ * Shuffle the channels of the input tensor.
+ *
+ * Given an input tensor and a integer value of num_groups, CHANNEL_SHUFFLE
+ * divide the channel dimension into num_groups groups, and reorganize the
+ * channels by grouping channels with the same index in each group.
+ *
+ * Along the channel dimension, the output is calculated using this formula:
+ *
+ * output_channel[k * num_groups + g] = input_channel[g * group_size + k]
+ *
+ * where group_size = num_channels / num_groups
+ *
+ * The number of channels must be divisible by num_groups.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the tensor to be shuffled.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the number of
+ * groups.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the dimension
+ * channel shuffle 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).
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} and same shape as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ CHANNEL_SHUFFLE = 46,
+ /**
+ * Apply postprocessing steps to bounding box detections.
+ *
+ * Bounding box detections are generated by applying transformation on a set
+ * of predefined anchors with the bounding box deltas from bounding box
+ * regression. A final step of hard NMS is applied to limit the number of
+ * returned boxes.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Inputs:
+ * * 0: A 3-D Tensor of shape [batches, num_anchors, num_classes], specifying
+ * the score of each anchor with each class. Class 0 for each
+ * [batches, num_anchors, 0] is background and will be ignored.
+ * * 1: A 3-D Tensor of shape [batches, num_anchors, length_box_encoding], with
+ * the first four values in length_box_encoding specifying the bounding
+ * box deltas. The box deltas are encoded in the order of [dy, dx, dh, dw],
+ * where dy and dx is the linear-scale relative correction factor for the
+ * center position of the bounding box with respect to the width and height,
+ * dh and dw is the log-scale relative correction factor for the width and
+ * height. All the entries in length_box_encoding beyond the first four
+ * values are ignored in this operation.
+ * * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each
+ * predefined anchor, with format [ctr_y, ctr_x, h, w], where ctr_y and
+ * ctr_x are the center position of the box, and h and w are the height
+ * and the width.
+ * * 3: An {@link OperandType::FLOAT32} scalar, specifying the scaling
+ * factor for dy in bounding box deltas.
+ * * 4: An {@link OperandType::FLOAT32} scalar, specifying the scaling
+ * factor for dx in bounding box deltas.
+ * * 5: An {@link OperandType::FLOAT32} scalar, specifying the scaling
+ * factor for dh in bounding box deltas.
+ * * 6: An {@link OperandType::FLOAT32} scalar, specifying the scaling
+ * factor for dw in bounding box deltas.
+ * * 7: An {@link OperandType::BOOL} scalar, set to true to use regular
+ * multi-class NMS algorithm that do NMS separately for each class,
+ * set to false for a faster algorithm that only do one single NMS
+ * using the highest class score..
+ * * 8: An {@link OperandType::INT32} scalar, max_num_detections, specifying
+ * the maximum number of boxes for the output. Boxes with the lowest
+ * scores are discarded to meet the limit.
+ * * 9: An {@link OperandType::INT32} scalar, only used when input7 is
+ * set to false, specifying the maximum number of classes per detection.
+ * * 10: An {@link OperandType::INT32} scalar, only used when input7 is
+ * set to true, specifying the maximum number of detections when
+ * applying NMS algorithm for each single class.
+ * * 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}.
+ * * 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}.
+ * * 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
+ * class is included, it has label 0 and the output classes start
+ * at 1 in the label map, otherwise, the output classes start at 0.
+ *
+ * Outputs:
+ * * 0: A 2-D tensor of the same {@link OperandType} as input0, with shape
+ * [batches, max_num_detections], specifying the score of each output
+ * detections.
+ * * 1: A 3-D tensor of shape [batches, max_num_detections, 4], specifying the
+ * coordinates of each output bounding box, with format
+ * [y1, x1, y2, x2].
+ * * 2: A 2-D {@link OperandType::TENSOR_INT32} tensor, of shape
+ * [batches, max_num_detections], specifying the class label for each
+ * output detection.
+ * * 3: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape [batches],
+ * specifying the number of valid output detections for each batch.
+ */
+ DETECTION_POSTPROCESSING = 47,
+ /**
+ * For input tensors x and y, computes x == y elementwise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+ * with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ */
+ EQUAL = 48,
+ /**
+ * Computes exponential of x element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ EXP = 49,
+ /**
+ * Inserts a dimension of 1 into a tensor's shape.
+ *
+ * Given a tensor input, this operation inserts a dimension of 1 at the
+ * given dimension index of input's shape. The dimension index starts at
+ * zero; if you specify a negative dimension index, it is counted backward
+ * from the end.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: An n-D tensor.
+ * * 1: An {@link OperandType::INT32} scalar specifying the dimension
+ * index to expand. Must be in the range [-(n + 1), (n + 1)).
+ *
+ * Outputs:
+ * * 0: An (n + 1)-D tensor with the same {@link OperandType} and data as
+ * input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ EXPAND_DIMS = 50,
+ /**
+ * Gathers values along an axis.
+ *
+ * Produces an output tensor with shape
+ * input0.dimension[:axis] + indices.dimension + input0.dimension[axis + 1:]
+ * where:
+ * # Vector indices (output is rank(input0)).
+ * output[a_0, ..., a_n, i, b_0, ..., b_n] =
+ * input0[a_0, ..., a_n, indices[i], b_0, ..., b_n]
+ *
+ * # Higher rank indices (output is rank(input0) + rank(indices) - 1).
+ * output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] =
+ * input0[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n]
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: An n-D tensor from which to gather values.
+ * * 1: An {@link OperandType::INT32} scalar specifying the axis.
+ * Negative index is used to specify axis from the end
+ * (e.g. -1 for the last axis). Must be in the range [-n, n).
+ * * 2: A k-D tensor {@link OperandType::TENSOR_INT32} of indices.
+ * The values must be in the bounds of the corresponding dimensions
+ * of input0.
+ *
+ * Outputs:
+ * * 0: An (n + k - 1)-D tensor with the same {@link OperandType} as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ GATHER = 51,
+ /**
+ * Generate aixs-aligned bounding box proposals.
+ *
+ * Bounding box proposals are generated by applying transformation on a set
+ * of predefined anchors with the bounding box deltas from bounding box
+ * regression. A final step of hard NMS is applied to limit the number of
+ * returned boxes.
+ *
+ * Axis-aligned bounding boxes are represented by its upper-left corner
+ * coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid
+ * bounding box should satisfy x1 <= x2 and y1 <= y2.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Inputs:
+ * * 0: A 4-D Tensor specifying the score of each anchor at each
+ * location. With "NHWC" data layout, the tensor shape is
+ * [batches, height, width, num_anchors]. With "NCHW" data layout,
+ * the tensor shape is [batches, num_anchors, height, width].
+ * * 1: A 4-D Tensor specifying the bounding box deltas. With "NHWC" data
+ * layout, the tensor shape is [batches, height, width, num_anchors * 4].
+ * With "NCHW" data layout, the tensor shape is
+ * [batches, num_anchors * 4, height, width]. The box deltas are encoded
+ * in the order of [dx, dy, dw, dh], where dx and dy is the linear-scale
+ * relative correction factor for the center position of the bounding box
+ * with respect to the width and height, dw and dh is the log-scale
+ * relative correction factor for the width and height. The last
+ * dimensions is the channel dimension.
+ * * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each
+ * predefined anchor, with format [x1, y1, x2, y2]. For input0 of type
+ * {@link OperandType::TENSOR_QUANT8_ASYMM} or
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}, this tensor should be of
+ * {@link OperandType::TENSOR_QUANT16_SYMM}, with scale of 0.125.
+ * * 3: A 2-D Tensor of shape [batches, 2], specifying the size of
+ * each image in the batch, with format [image_height, image_width].
+ * For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM} or
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}, this
+ * tensor should be of {@link OperandType::TENSOR_QUANT16_SYMM}, with
+ * scale of 0.125.
+ * * 4: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+ * from the height of original image to the height of feature map.
+ * * 5: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+ * from the width of original image to the width of feature map.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the maximum
+ * number of boxes before going into the hard NMS algorithm. Boxes
+ * with the lowest scores are discarded to meet the limit. Set to
+ * a non-positive value for unlimited number.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the maximum
+ * number of boxes returning from the hard NMS algorithm. Boxes
+ * with the lowest scores are discarded to meet the limit. Set to
+ * a non-positive value for unlimited number.
+ * * 8: An {@link OperandType::FLOAT32} scalar, specifying the IoU
+ * threshold for hard NMS.
+ * * 9: An {@link OperandType::FLOAT32} scalar, min_size. Boxes with
+ * height or width lower than the absolute threshold are filtered out.
+ * * 10: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0 and input1. Set to false for NHWC.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0, of shape
+ * [num_output_rois], specifying the score of each output box.
+ * The boxes are grouped by batches, but the sequential order in
+ * each batch is not guaranteed. For type of
+ * {@link OperandType::TENSOR_QUANT8_ASYMM} or
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}, the scale and zero
+ * point must be the same as input0.
+ * * 1: A tensor of the same {@link OperandType} as input3, of shape
+ * [num_output_rois, 4], specifying the coordinates of each output
+ * bounding box for each class, with format [x1, y1, x2, y2].
+ * The sequential order of the boxes corresponds with output0.
+ * For type of {@link OperandType::TENSOR_QUANT16_ASYMM}, the
+ * scale must be 0.125 and the zero point must be 0.
+ * * 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.
+ */
+ GENERATE_PROPOSALS = 52,
+ /**
+ * For input tensors x and y, computes x > y elementwise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+ * with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ */
+ GREATER = 53,
+ /**
+ * For input tensors x and y, computes x >= y elementwise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+ * with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ */
+ GREATER_EQUAL = 54,
+ /**
+ * Performs a grouped 2-D convolution operation.
+ *
+ * Given an input tensor of shape [batches, height, width, depth_in] and a
+ * filter tensor of shape [depth_out, filter_height, filter_width, depth_group]
+ * containing depth_out convolutional filters of depth depth_group, GROUPED_CONV
+ * applies a group of different filters to each input channel group, then
+ * concatenates the results together.
+ *
+ * Specifically, the input channels are divided into num_groups groups, each with
+ * depth depth_group, i.e. depth_in = num_groups * depth_group. The convolutional
+ * filters are also divided into num_groups groups, i.e. depth_out is divisible
+ * by num_groups. GROUPED_CONV applies each group of filters to the corresponding
+ * input channel group, and the result are concatenated together.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, i, j, g * channel_multiplier + q] =
+ * sum_{di, dj, dk} (
+ * input[b, strides[1] * i + di, strides[2] * j + dj,
+ * g * depth_group + dk] *
+ * filter[g * channel_multiplier + q, di, dj, dk]
+ * ) + bias[channel]
+ *
+ * where channel_multiplier = depth_out / num_groups
+ *
+ * Supported tensor {@link OperandType} configurations:
+ * * 16 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
+ *
+ * * 32 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
+ *
+ * * 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).
+ *
+ * * Quantized signed (since HAL version 1.3):
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, filter, and output.
+ * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+ * * * input.scale * filter.scale).
+ *
+ * * Quantized with symmetric per channel quantization for the filter:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
+ * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+ * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+ * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+ *
+ * * Quantized signed with filter symmetric per channel quantization (since HAL version 1.3):
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, and output.
+ * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+ * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+ * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+ *
+ * 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].
+ *
+ * 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, where depth_in = num_groups * depth_group.
+ * * 1: A 4-D tensor, of shape
+ * [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 (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 type.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ * 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
+ * bias_scale[i] = input_scale * filter_scale[i].
+ * * 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
+ * the right, in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 9: An {@link OperandType::INT32} scalar, specifying the number of
+ * groups.
+ * * 10: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 11: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0 and output0. Set to false for NHWC.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input, where depth_in = num_groups * depth_group.
+ * * 1: A 4-D tensor, of shape
+ * [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 (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
+ * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same type.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ * 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
+ * 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
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the number of
+ * groups.
+ * * 7: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 8: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0 and output0. Set to false for NHWC.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth_out].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
+ */
+ GROUPED_CONV_2D = 55,
+ /**
+ * Localize the maximum keypoints from heatmaps.
+ *
+ * This operation approximates the accurate maximum keypoint scores and
+ * indices after bicubic upscaling by using Taylor expansion up to the
+ * quadratic term.
+ *
+ * The bounding box is represented by its upper-left corner coordinate
+ * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image.
+ * A valid bounding box should satisfy x1 <= x2 and y1 <= y2.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * 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].
+ *
+ * Inputs:
+ * * 0: A 4-D Tensor of shape
+ * [num_boxes, heatmap_size, heatmap_size, num_keypoints],
+ * specifying the heatmaps, the height and width of heatmaps should
+ * be the same, and must be greater than or equal to 2.
+ * * 1: A 2-D Tensor of shape [num_boxes, 4], specifying the bounding boxes,
+ * each with format [x1, y1, x2, y2]. For input0 of type
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}, this tensor should
+ * be of {@link OperandType::TENSOR_QUANT16_ASYMM}, with zeroPoint
+ * of 0 and scale of 0.125.
+ * For input0 of type
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}, this tensor
+ * should be of {@link OperandType::TENSOR_QUANT16_ASYMM}, with
+ * zeroPoint of -128 and scale of 0.125.
+ * * 2: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0. Set to false for NHWC.
+ *
+ * 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} or
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} 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].
+ * 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,
+ /**
+ * Applies instance normalization to the input tensor.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, h, w, c] =
+ * (input[b, h, w, c] - mean[b, c]) * gamma /
+ * sqrt(var[b, c] + epsilon) + beta
+ *
+ * Where the mean and variance are computed across the spatial dimensions:
+ *
+ * mean[b, c] =
+ * sum_{h, w}(input[b, h, w, c]) / sum(1)
+ *
+ * var[b, c] =
+ * sum_{h, w}(pow(input[b, h, w, c] - mean[b, c], 2)) / sum(1)
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@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].
+ *
+ * Inputs:
+ * * 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}.
+ * * 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}.
+ * * 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}.
+ * * 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.
+ */
+ INSTANCE_NORMALIZATION = 57,
+ /**
+ * For input tensors x and y, computes x < y elementwise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+ * with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ */
+ LESS = 58,
+ /**
+ * For input tensors x and y, computes x <= y elementwise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+ * with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ */
+ LESS_EQUAL = 59,
+ /**
+ * Computes natural logarithm of x element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ LOG = 60,
+ /**
+ * Returns the truth value of x AND y element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ * * 1: A tensor of {@link OperandType::TENSOR_BOOL8} and dimensions
+ * compatible with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ */
+ LOGICAL_AND = 61,
+ /**
+ * Computes the truth value of NOT x element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ LOGICAL_NOT = 62,
+ /**
+ * Returns the truth value of x OR y element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ * * 1: A tensor of {@link OperandType::TENSOR_BOOL8} and dimensions
+ * compatible with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ */
+ LOGICAL_OR = 63,
+ /**
+ * Computes the log softmax activations given logits.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = logits * beta - log(reduce_sum(exp(logits * beta), axis))
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor specifying the input logits.
+ * * 1: A scalar, specifying the positive scaling factor for the exponent,
+ * beta.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT16}, the beta
+ * value must be of {@link OperandType::FLOAT16}.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the beta
+ * value must be of {@link OperandType::FLOAT32}.
+ * * 2: An {@link OperandType::INT32} scalar specifying the axis to
+ * reduce across. Negative index is used to specify axis from the
+ * end (e.g. -1 for the last axis). Must be in the range [-n, n).
+ *
+ * Outputs:
+ * * 0: The output tensor of the same {@link OperandType} and shape as
+ * input0.
+ */
+ LOG_SOFTMAX = 64,
+ /**
+ * Returns the element-wise maximum of two tensors.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 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.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
+ */
+ MAXIMUM = 65,
+ /**
+ * Returns the element-wise minimum of two tensors.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 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.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
+ */
+ MINIMUM = 66,
+ /**
+ * Computes numerical negative value element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ NEG = 67,
+ /**
+ * For input tensors x and y, computes x != y elementwise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+ * with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ */
+ NOT_EQUAL = 68,
+ /**
+ * Pads a tensor with the given constant value according to the specified
+ * paddings.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the tensor to be padded.
+ * * 1: A 2-D Tensor of {@link OperandType::TENSOR_INT32}, the paddings
+ * for each spatial dimension of the input tensor. The shape of the
+ * tensor must be {rank(input0), 2}.
+ * padding[i, 0] specifies the number of elements to be padded in the
+ * front of dimension i.
+ * padding[i, 1] specifies the number of elements to be padded after
+ * the end of dimension i.
+ * * 2: An scalar specifying the value to use for padding input0.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT16}, the
+ * pad value must be of {@link OperandType::FLOAT16}.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the
+ * pad value must be of {@link OperandType::FLOAT32}.
+ * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * the pad value must be of {@link OperandType::INT32}. The
+ * scale and zeroPoint are assumed to be the same as in input0.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0. The
+ * output tensor has the same rank as input0, and each
+ * dimension of the output tensor has the same size as the
+ * corresponding dimension of the input tensor plus the size
+ * of the padding:
+ * output0.dimension[i] =
+ * padding[i, 0] + input0.dimension[i] + padding[i, 1]
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ PAD_V2 = 69,
+ /**
+ * Computes the power of one value to another.
+ *
+ * Given a tensor base and a tensor exponent, this operation computes
+ * base^exponent elementwise.
+ *
+ * This operations supports broadcasting. The size of the output is the
+ * maximum size along each dimension of the input operands. It starts with
+ * the trailing dimensions, and works its way forward.
+ *
+ * For example:
+ * base.dimension = {4, 1, 2}
+ * exponent.dimension = {5, 4, 3, 1}
+ * output.dimension = {5, 4, 3, 2}
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: A tensor specifying the base.
+ * * 1: A tensor specifying the exponent.
+ *
+ * Outputs:
+ * * 0: An output tensor.
+ */
+ POW = 70,
+ /**
+ * Parametric Rectified Linear Unit.
+ *
+ * It follows: f(x) = alpha * x for x < 0, f(x) = x for x >= 0, where alpha
+ * is a learned array with the same {@link OperandType} and compatible
+ * dimensions as input x.
+ *
+ * Two dimensions are compatible when:
+ * 1. they are equal, or
+ * 2. one of them is 1
+ *
+ * The size of the output is the maximum size along each dimension of the
+ * input operands. It starts with the trailing dimensions, and works its way
+ * forward.
+ *
+ * Example:
+ * input.dimension = {4, 1, 2}
+ * alpha.dimension = {5, 4, 3, 1}
+ * output.dimension = {5, 4, 3, 2}
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+ * as input0, specifying the alpha.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scales and zeroPoint can be different from input0 scale and zeroPoint.
+ */
+ PRELU = 71,
+ /**
+ * Quantizes the input tensor.
+ *
+ * The formula for {@link OperandType::TENSOR_QUANT8_ASYMM} output tensor is:
+ *
+ * output = max(0, min(255, round(input / scale) + zeroPoint)
+ *
+ * The formula for {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} output
+ * tensor is:
+ *
+ * output = max(-128, min(127, round(input / scale) + zeroPoint)
+ *
+ * Supported input tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported output tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: A tensor, may be zero-sized.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0, but with
+ * {@link OperandType::TENSOR_QUANT8_ASYMM} or.
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}.
+ */
+ QUANTIZE = 72,
+ /**
+ * A version of quantized LSTM, using 16 bit quantization for internal
+ * state.
+ *
+ * There is no projection layer, so cell state size is equal to the output
+ * size.
+ *
+ * Inputs:
+ * * 0: A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [numBatches, inputSize] specifying the input to the LSTM
+ * cell. Tensor is quantized with a fixed quantization range of
+ * [-1, 127/128] (scale = 1/128, zeroPoint = 128).
+ * * 1: The input-to-input weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, inputSize] specifying input-to-input part of
+ * weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 2: The input-to-forget weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, inputSize] specifying input-to-forget part of
+ * weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 3: The input-to-cell weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, inputSize] specifying input-to-cell part of
+ * weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 4: The input-to-output weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, inputSize] specifying input-to-output part of
+ * weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 5: The recurrent-to-input weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, outputSize] specifying recurrent-to-input part
+ * of weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 6: The recurrent-to-forget weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, outputSize] specifying recurrent-to-forget
+ * part of weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 7: The recurrent-to-cell weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, outputSize] specifying recurrent-to-cell part
+ * of weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 8: The recurrent-to-output weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, outputSize] specifying recurrent-to-output
+ * part of weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 9: The input gate bias.
+ * A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape
+ * [outputSize] specifying the bias for the fully-connected layer
+ * inside the LSTM cell. Bias is quantized with scale being a product
+ * of input and weights scales and zeroPoint equal to 0.
+ * * 10:The forget gate bias.
+ * A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape
+ * [outputSize] specifying the bias for the fully-connected layer
+ * inside the LSTM cell. Bias is quantized with scale being a product
+ * of input and weights scales and zeroPoint equal to 0.
+ * * 11:The cell bias.
+ * A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape
+ * [outputSize] specifying the bias for the fully-connected layer
+ * inside the LSTM cell. Bias is quantized with scale being a product
+ * of input and weights scales and zeroPoint equal to 0.
+ * * 12:The output gate bias.
+ * A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape
+ * [outputSize] specifying the bias for the fully-connected layer
+ * inside the LSTM cell. Bias is quantized with scale being a product
+ * of input and weights scales and zeroPoint equal to 0.
+ * * 13: A 2-D tensor of type {@link OperandType::TENSOR_QUANT16_SYMM}
+ * and shape [numBatches, outputSize] specifying the cell state from the
+ * previous time step of the LSTM cell. It is quantized using a
+ * quantization range of [-2^4, 2^4 * 32767/32768] (scale = 2^4 /
+ * 32768, zeroPoint = 0).
+ * * 14: A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [numBathes, outputSize] specifying the output of the LSTM
+ * cell from previous time-step. Tensor is quantized with a fixed
+ * quantization range of [-1, 127/128] (scale = 1/128, zeroPoint =
+ * 128).
+ *
+ *
+ * Outputs:
+ * * 0: A 2-D tensor of type {@link OperandType::TENSOR_QUANT16_SYMM}
+ * and shape [numBatches, outputSize] which contains a cell state from
+ * the current time step. Tensor is quantized using a quantization
+ * range of [-2^4, 2^4 * 32767/32768] (scale = 2^4 / 32768, zeroPoint =
+ * 0).
+ * * 1: A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [numBathes, outputSize] which contains the output value.
+ * Tensor is quantized with a fixed quantization range of [-1, 127/128]
+ * (scale = 1/128, zeroPoint = 128).
+ */
+ QUANTIZED_16BIT_LSTM = 73,
+ /**
+ * Draws samples from a multinomial distribution.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Inputs:
+ * * 0: A 2-D tensor with shape [batches, classes], specifying the
+ * unnormalized log-probabilities for all classes.
+ * * 1: A scalar {@link OperandType::INT32}, specifying the number of
+ * independent samples to draw for each row slice.
+ * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [2],
+ * specifying seeds used to initialize the random distribution. If both
+ * provided seeds are 0, both will be randomly generated.
+ * Outputs:
+ * * 0: A 2-D {@link OperandType::TENSOR_INT32} tensor with shape
+ * [batches, samples], containing the drawn samples.
+ */
+ RANDOM_MULTINOMIAL = 74,
+ /**
+ * Reduces a tensor by computing the "logical and" of elements along given
+ * dimensions.
+ *
+ * If keep_dims is true, the reduced dimensions are
+ * retained with length 1. Otherwise, the rank of the tensor is reduced by
+ * 1 for each entry in dimensions.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. Dimension values must be in the range [-n, n).
+ * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+ * retains reduced dimensions with length 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * If all dimensions are reduced and keep_dims is false, the output
+ * shape is [1].
+ */
+ REDUCE_ALL = 75,
+ /**
+ * Reduces a tensor by computing the "logical or" of elements along given
+ * dimensions.
+ *
+ * If keep_dims is true, the reduced dimensions are
+ * retained with length 1. Otherwise, the rank of the tensor is reduced by
+ * 1 for each entry in dimensions.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. Dimension values must be in the range [-n, n).
+ * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+ * retains reduced dimensions with length 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * If all dimensions are reduced and keep_dims is false, the output
+ * shape is [1].
+ */
+ REDUCE_ANY = 76,
+ /**
+ * Reduces a tensor by computing the maximum of elements along given
+ * dimensions.
+ *
+ * If keep_dims is true, the reduced dimensions are
+ * retained with length 1. Otherwise, the rank of the tensor is reduced by
+ * 1 for each entry in dimensions.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. Dimension values must be in the range [-n, n).
+ * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+ * retains reduced dimensions with length 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * If all dimensions are reduced and keep_dims is false, the output
+ * shape is [1].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ REDUCE_MAX = 77,
+ /**
+ * Reduces a tensor by computing the minimum of elements along given
+ * dimensions.
+ *
+ * If keep_dims is true, the reduced dimensions are
+ * retained with length 1. Otherwise, the rank of the tensor is reduced by
+ * 1 for each entry in dimensions.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. Dimension values must be in the range [-n, n).
+ * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+ * retains reduced dimensions with length 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * If all dimensions are reduced and keep_dims is false, the output
+ * shape is [1].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ REDUCE_MIN = 78,
+ /**
+ * Reduces a tensor by multiplying elements along given dimensions.
+ *
+ * If keep_dims is true, the reduced dimensions are
+ * retained with length 1. Otherwise, the rank of the tensor is reduced by
+ * 1 for each entry in dimensions.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. Dimension values must be in the range [-n, n).
+ * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+ * retains reduced dimensions with length 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * If all dimensions are reduced and keep_dims is false, the output
+ * shape is [1].
+ */
+ REDUCE_PROD = 79,
+ /**
+ * Reduces a tensor by summing elements along given dimensions.
+ *
+ * If keep_dims is true, the reduced dimensions are
+ * retained with length 1. Otherwise, the rank of the tensor is reduced by
+ * 1 for each entry in dimensions.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. Dimension values must be in the range [-n, n).
+ * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+ * retains reduced dimensions with length 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * If all dimensions are reduced and keep_dims is false, the output
+ * shape is [1].
+ */
+ REDUCE_SUM = 80,
+ /**
+ * Select and scale the feature map of each region of interest to a unified
+ * output size by average pooling sampling points from bilinear interpolation.
+ *
+ * The region of interest is represented by its upper-left corner coordinate
+ * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image.
+ * A spatial scaling factor is applied to map into feature map coordinate.
+ * A valid region of interest should satisfy x1 <= x2 and y1 <= y2.
+ *
+ * No rounding is applied in this operation. The sampling points are unified
+ * distributed in the pooling bin and their values are calculated by bilinear
+ * interpolation.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * 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].
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, specifying the feature map.
+ * * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of
+ * the regions of interest, each line with format [x1, y1, x2, y2].
+ * For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * this tensor should be of {@link OperandType::TENSOR_QUANT16_ASYMM},
+ * with zeroPoint of 0 and scale of 0.125. Zero num_rois is
+ * supported for this tensor.
+ * * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+ * [num_rois], specifying the batch index of each box. Boxes with
+ * the same batch index are grouped together. Zero num_rois is
+ * supported for this tensor.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the output
+ * height of the output tensor.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the output
+ * width of the output tensor.
+ * * 5: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+ * from the height of original image to the height of feature map.
+ * * 6: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+ * from the width of original image to the width of feature map.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the number of
+ * sampling points in height dimension used to compute the output.
+ * Set to 0 for adaptive value of ceil(roi_height/out_height).
+ * * 8: An {@link OperandType::INT32} scalar, specifying the number of
+ * sampling points in width dimension used to compute the output.
+ * Set to 0 for adaptive value of ceil(roi_width/out_width).
+ * * 9: 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} as input0. The output
+ * shape is [num_rois, out_height, out_width, depth].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint can be different from the input0 scale and zeroPoint.
+ */
+ ROI_ALIGN = 81,
+ /**
+ * Select and scale the feature map of each region of interest to a unified
+ * output size by max-pooling.
+ *
+ * The region of interest is represented by its upper-left corner coordinate
+ * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image.
+ * A spatial scaling factor is applied to map into feature map coordinate.
+ * A valid region of interest should satisfy x1 <= x2 and y1 <= y2.
+ *
+ * Rounding is applied in this operation to ensure integer boundary for
+ * regions of interest and pooling bins.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * 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].
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, specifying the feature map.
+ * * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of
+ * the regions of interest, each line with format [x1, y1, x2, y2].
+ * For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * this tensor should be of {@link OperandType::TENSOR_QUANT16_ASYMM},
+ * with zeroPoint of 0 and scale of 0.125.
+ * * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+ * [num_rois], specifying the batch index of each box. Boxes with
+ * the same batch index are grouped together.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the output
+ * height of the output tensor.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the output
+ * width of the output tensor.
+ * * 5: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+ * from the height of original image to the height of feature map.
+ * * 6: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+ * from the width of original image to the width of feature map.
+ * * 7: 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} as input0. The output
+ * shape is [num_rois, out_height, out_width, depth].
+ * For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ ROI_POOLING = 82,
+ /**
+ * Computes reciprocal of square root of x element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ RSQRT = 83,
+ /**
+ * Using a tensor of booleans c and input tensors x and y select values
+ * elementwise from both input tensors:
+ *
+ * O[i] = C[i] ? x[i] : y[i].
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: A tensor of type {@link OperandType::TENSOR_BOOL8} acting as a
+ * mask that chooses, based on the value at each element, whether the
+ * corresponding element in the output should be taken from input1 (if
+ * 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}
+ * and {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} 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,
+ /**
+ * Computes sin of x element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ SIN = 85,
+ /**
+ * Extracts a slice of specified size from the input tensor starting at a
+ * specified location.
+ *
+ * The starting location is specified as a 1-D tensor containing offsets
+ * 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.
+ *
+ * A sum of begin offset and a size of a slice must not exceed size of a
+ * corresponding dimension.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: An n-D tensor to take slice from, may be zero-sized.
+ * * 1: A 1-D tensor of type {@link OperandType::TENSOR_INT32} specifying
+ * the beginning indices of the slice in each dimension.
+ * * 2: A 1-D tensor of type {@link OperandType::TENSOR_INT32} specifying
+ * the size of the slice in each dimension.
+ *
+ * Outputs:
+ * * 0: An n-D tensor of the same type as the input containing the slice.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * its scale and zeroPoint has to be same as the input0 scale and zeroPoint.
+ */
+ SLICE = 86,
+ /**
+ * Splits a tensor along a given axis into num_splits subtensors.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: An n-D tensor to split.
+ * * 1: An {@link OperandType::INT32} scalar specifying the axis along
+ * which to split.
+ * * 2: An {@link OperandType::INT32} scalar indicating the number of
+ * splits along given axis. Must evenly divide axis size.
+ *
+ * Outputs:
+ * * 0 ~ (num_splits - 1): Resulting subtensors.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ SPLIT = 87,
+ /**
+ * Computes square root of x element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ SQRT = 88,
+ /**
+ * Constructs a tensor by tiling a given tensor.
+ *
+ * This operation creates a new tensor by replicating `input` `multiples`
+ * times. The output tensor's i-th dimension has `input.dims(i) * multiples[i]`
+ * elements, and the values of `input` are replicated `multiples[i]` times
+ * along the i-th dimension.
+ * For example, tiling `[a b c d]` by `[2]` produces `[a b c d a b c d]`.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: input, an n-D tensor specifying the input.
+ * * 1: multiples, a 1-D tensor of {@link OperandType::TENSOR_INT32}.
+ * The length of multiples must be n.
+ *
+ * Outputs:
+ * * 0: A tiled tensor of the same {@link OperandType} and rank as `input`.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ TILE = 89,
+ /**
+ * Finds values and indices of the k largest entries for the last dimension.
+ *
+ * Resulting values in each dimensions are sorted in descending order. If
+ * two values are equal, the one with larger index appears first.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: input, an n-D tensor specifying the input.
+ * * 1: k, an {@link OperandType::INT32} scalar, specifying the number of
+ * top elements to look for along the last dimension.
+ *
+ * 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} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} 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.
+ */
+ TOPK_V2 = 90,
+ /**
+ * Performs the transpose of 2-D convolution operation.
+ *
+ * This operation is sometimes called "deconvolution" after Deconvolutional
+ * Networks, but is actually the transpose (gradient) of
+ * {@link OperandType::CONV_2D} rather than an actual deconvolution.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * Supported tensor {@link OperandType} configurations:
+ * * 16 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
+ *
+ * * 32 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
+ *
+ * * 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).
+ *
+ * * Quantized with symmetric per channel quantization for the filter:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
+ * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+ * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+ * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+ *
+ * Available since HAL version 1.3:
+ * * Quantized signed (since HAL version 1.3):
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, filter, and output.
+ * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+ * * * input.scale * filter.scale).
+ *
+ * * Quantized signed with filter symmetric per channel quantization (since HAL version 1.3):
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, and output.
+ * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+ * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+ * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+ *
+ * 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].
+ *
+ * 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.
+ * * 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 (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 type.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * 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 must 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.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the right, in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 10: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0 and output0. Set to false for NHWC.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * * 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 (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
+ * same type.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * 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 must 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::TENSOR_INT32} tensor, specifying the output
+ * tensor shape.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the implicit
+ * padding scheme, has to be one of the
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 8: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0 and output0. Set to false for NHWC.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth_out].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
+ */
+ TRANSPOSE_CONV_2D = 91,
+ /**
+ * A recurrent neural network specified by an LSTM cell.
+ *
+ * Performs (fully) dynamic unrolling of input.
+ *
+ * This Op unrolls the input along the time dimension, and implements the
+ * following operation for each element in the sequence
+ * s = 1...sequence_length:
+ * outputs[s] = projection(state = activation(LSTMOp(inputs[s])))
+ *
+ * Where LSTMOp is the LSTM op as in {@link OperandType::LSTM},
+ * the "projection" is an optional projection layer from state and output
+ * and the “activation” is the function passed as the
+ * “fused_activation_function” argument (if not “NONE”).
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: 3, either time-major or batch-major.
+ *
+ * All input and output tensors must be of the same type.
+ *
+ * Inputs:
+ * * 0: The input (\f$x_t\f$).
+ * A 3-D tensor of shape:
+ * If time-major: [max_time, batch_size, input_size]
+ * If batch-major: [batch_size, max_time, input_size]
+ * where “max_time” is the number of timesteps (sequence length),
+ * “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 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 shape [num_units, input_size].
+ * * 3: The input-to-cell weights (\f$W_{xc}\f$).
+ * 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 shape [num_units, input_size].
+ * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
+ * 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 shape [num_units, output_size].
+ * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
+ * 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 shape [num_units, output_size].
+ * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 12:The input gate bias (\f$b_i\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 13:The forget gate bias (\f$b_f\f$).
+ * A 1-D tensor of shape [num_units].
+ * * 14:The cell bias (\f$b_c\f$).
+ * A 1-D tensor of shape [num_units].
+ * * 15:The output gate bias (\f$b_o\f$).
+ * A 1-D tensor of shape [num_units].
+ * * 16:The projection weights (\f$W_{proj}\f$). Optional.
+ * A 2-D tensor of shape [output_size, num_units].
+ * * 17:The projection bias (\f$b_{proj}\f$). Optional.
+ * A 1-D tensor of shape [output_size].
+ * * 18:The output state (in) (\f$h_{t-1}\f$).
+ * 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 shape [batch_size, num_units].
+ * * 20:The activation function (\f$g\f$).
+ * A value indicating the activation function:
+ * <ul>
+ * <li>0: None;
+ * <li>1: Relu;
+ * <li>3: Relu6;
+ * <li>4: Tanh;
+ * <li>6: Sigmoid.
+ * </ul>
+ * * 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.
+ * * 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.
+ * * 23:Time-major if true, batch-major if false.
+ * * 24:The input layer normalization weights. Optional.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at input gate.
+ * * 25:The forget layer normalization weights. Optional.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at forget gate.
+ * * 26:The cell layer normalization weights. Optional.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at cell gate.
+ * * 27:The output layer normalization weights. Optional.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at output gate.
+ *
+ * Outputs:
+ * * 0: The output (\f$o_t\f$).
+ * A 3-D tensor of shape:
+ * If time-major: [max_time, batch_size, output_size]
+ * If batch-major: [batch_size, max_time, output_size]
+ * * 1: A tensor of shape [batch_size, output_size] containing a hidden
+ * state from the last time step in the sequence. This output is
+ * optional and can be omitted. If this output is present then
+ * output #2 must be present as well.
+ * Available since HAL version 1.3.
+ * * 2: A tensor of shape [batch_size, cell_size] containing a cell state
+ * from the last time step in the sequence. This output is optional
+ * and can be omitted.
+ * Available since HAL version 1.3.
+ */
+ UNIDIRECTIONAL_SEQUENCE_LSTM = 92,
+ /**
+ * A recurrent neural network layer that applies a basic RNN cell to a
+ * sequence of inputs.
+ *
+ * This layer unrolls the input along the sequence dimension, and implements
+ * the following operation
+ * for each element in the sequence s = 1...sequence_length:
+ * outputs[s] = state = activation(inputs[s] * input_weights’ + state *
+ * recurrent_weights’ + bias)
+ *
+ * Where:
+ * * “input_weights” is a weight matrix that multiplies the inputs;
+ * * “recurrent_weights” is a weight matrix that multiplies the current
+ * “state” which itself is the output from the previous time step
+ * computation;
+ * * “bias” is a bias vector (added to each output vector in the batch);
+ * * “activation” is the function passed as the “fused_activation_function”
+ * argument (if not “NONE”).
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * The input tensors must all be the same type.
+ *
+ * Inputs:
+ * * 0: input.
+ * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
+ * it is set to 1, then the input has a shape [maxTime, batchSize,
+ * inputSize], otherwise the input has a shape [batchSize, maxTime,
+ * inputSize].
+ * * 1: weights.
+ * A 2-D tensor of shape [numUnits, inputSize].
+ * * 2: recurrent_weights.
+ * A 2-D tensor of shape [numUnits, numUnits].
+ * * 3: bias.
+ * A 1-D tensor of shape [numUnits].
+ * * 4: hidden state
+ * A 2-D tensor of shape [batchSize, numUnits]. Specifies a hidden
+ * state input for the first time step of the computation.
+ * * 5: fusedActivationFunction.
+ * A {@link FusedActivationFunc} value indicating the activation function. If
+ * “NONE” is specified then it results in a linear activation.
+ * * 6: timeMajor
+ * An {@link OperandType::INT32} scalar specifying the shape format
+ * of input and output tensors. Must be set to either 0 or 1.
+ * Outputs:
+ * * 0: output.
+ * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
+ * it is set to 1, then the output has a shape [maxTime, batchSize,
+ * numUnits], otherwise the output has a shape [batchSize, maxTime,
+ * numUnits].
+ * * 1: A tensor of shape [batchSize, numUnits] containing hidden state
+ * from the last time step in the sequence. This output is optional
+ * and can be omitted.
+ * Available since HAL version 1.3.
+ */
+ UNIDIRECTIONAL_SEQUENCE_RNN = 93,
+ /**
+ * Resizes images to given size using the nearest neighbor interpretation.
+ *
+ * Resized images must be distorted if their output aspect ratio is not the
+ * same as input aspect ratio. The corner pixels of output may not be the
+ * same as corner pixels of input.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * 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].
+ *
+ * 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. 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 {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * * 4: Align corners. An optional {@link OperandType::BOOL}
+ * scalar, default to false. If True, the centers of the 4 corner
+ * pixels of the input and output tensors are aligned, preserving the
+ * values at the corner pixels.
+ * Available since HAL version 1.3.
+ * * 5: Half pixel centers. An optional {@link OperandType::BOOL}
+ * scalar, default to false. If True, the pixel centers are assumed to
+ * be at (0.5, 0.5). This is the default behavior of image.resize in
+ * TF 2.0. If this parameter is True, then align_corners parameter
+ * must be False.
+ * Available since HAL version 1.3.
+ *
+ * Inputs (resizing by scale):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input. Zero batches is supported for this tensor.
+ * * 1: A scalar, specifying width_scale, the scaling factor of the width
+ * dimension from the input tensor to the output tensor. The output
+ * width is calculated as new_width = floor(width * width_scale).
+ * The scalar must be of {@link OperandType::FLOAT16} if input0 is
+ * of {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} otherwise.
+ * * 2: A scalar, specifying height_scale, the scaling factor of the height
+ * dimension from the input tensor to the output tensor. The output
+ * height is calculated as new_height = floor(height * height_scale).
+ * The scalar must be of {@link OperandType::FLOAT16} if input0 is
+ * of {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} otherwise.
+ * * 3: An {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * * 4: Align corners. An optional {@link OperandType::BOOL}
+ * scalar, default to false. If True, the centers of the 4 corner
+ * pixels of the input and output tensors are aligned, preserving the
+ * values at the corner pixels.
+ * Available since HAL version 1.3.
+ * * 5: Half pixel centers. An optional {@link OperandType::BOOL}
+ * scalar, default to false. If True, the pixel centers are assumed to
+ * be at (0.5, 0.5). This is the default behavior of image.resize in
+ * TF 2.0. If this parameter is True, then align_corners parameter
+ * must be False.
+ * Available since HAL version 1.3.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, new_height, new_width, depth].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ RESIZE_NEAREST_NEIGHBOR = 94,
+ /**
+ * Quantized version of {@link OperationType::LSTM}.
+ *
+ * The input and the output use asymmetric quantized types, while the rest
+ * use symmetric ones.
+ *
+ * Inputs:
+ * * 0: The input to the LSTM cell.
+ * Type: {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ * Shape: [batchSize, inputSize]
+ * * 1: The input-to-input weights. Optional.
+ * Type: {@link OperandType::TENSOR_QUANT8_SYMM}
+ * Shape: [numUnits, inputSize]
+ * * 2: The input-to-forget weights.
+ * Type: {@link OperandType::TENSOR_QUANT8_SYMM}
+ * Shape: [numUnits, inputSize]
+ * * 3: The input-to-cell weights.
+ * Type: {@link OperandType::TENSOR_QUANT8_SYMM}
+ * Shape: [numUnits, inputSize]
+ * * 4: The input-to-output weights.
+ * Type: {@link OperandType::TENSOR_QUANT8_SYMM}
+ * Shape: [numUnits, inputSize]
+ * * 5: The recurrent-to-input weights. Optional.
+ * Type: {@link OperandType::TENSOR_QUANT8_SYMM}
+ * Shape: [numUnits, outputSize]
+ * * 6: The recurrent-to-forget weights.
+ * Type: {@link OperandType::TENSOR_QUANT8_SYMM}
+ * Shape: [numUnits, outputSize]
+ * * 7: The recurrent-to-cell weights.
+ * Type: {@link OperandType::TENSOR_QUANT8_SYMM}
+ * Shape: [numUnits, outputSize]
+ * * 8: The recurrent-to-output weights.
+ * Type: {@link OperandType::TENSOR_QUANT8_SYMM}
+ * Shape: [numUnits, outputSize]
+ * * 9: The cell-to-input weights (for peephole). Optional.
+ * Type: {@link OperandType::TENSOR_QUANT16_SYMM}
+ * Shape: [numUnits]
+ * * 10: The cell-to-forget weights (for peephole). Optional.
+ * Type: {@link OperandType::TENSOR_QUANT16_SYMM}
+ * Shape: [numUnits]
+ * * 11: The cell-to-output weights (for peephole). Optional.
+ * Type: {@link OperandType::TENSOR_QUANT16_SYMM}
+ * Shape: [numUnits]
+ * * 12: The input gate bias. Quantized with scale being the
+ * product of input and weights scales and zeroPoint equal to 0.
+ * Optional.
+ * Type: {@link OperandType::TENSOR_INT32}
+ * Shape: [numUnits]
+ * * 13: The forget gate bias. Quantized with scale being the
+ * product of input and weights scales and zeroPoint equal to 0.
+ * Type: {@link OperandType::TENSOR_INT32}
+ * Shape: [numUnits]
+ * * 14: The cell bias. Quantized with scale being the
+ * product of input and weights scales and zeroPoint equal to 0.
+ * Type: {@link OperandType::TENSOR_INT32}
+ * Shape: [numUnits]
+ * * 15: The output gate bias. Quantized with scale being the
+ * product of input and weights scales and zeroPoint equal to 0.
+ * Type: {@link OperandType::TENSOR_INT32}
+ * Shape: [numUnits]
+ * * 16: The projection weights. Optional.
+ * Type: {@link OperandType::TENSOR_QUANT8_SYMM}
+ * Shape: [outputSize, numUnits]
+ * * 17: The projection bias. Quantized with scale being the
+ * product of input and weights scales and zeroPoint equal to 0.
+ * Optional.
+ * Type: {@link OperandType::TENSOR_INT32}
+ * Shape: [outputSize]
+ * * 18: The output from the previous time step.
+ * Type: {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ * Shape: [batchSize, outputSize]
+ * * 19: The cell state from the previous time step.
+ * Type: {@link OperandType::TENSOR_QUANT16_SYMM}
+ * Shape: [batchSize, numUnits]
+ * * 20: The input layer normalization weights. Used to rescale
+ * normalized inputs to activation at input gate. Optional.
+ * Type: {@link OperandType::TENSOR_QUANT16_SYMM}
+ * Shape: [numUnits]
+ * * 21: The forget layer normalization weights. Used to
+ * rescale normalized inputs to activation at forget gate. Optional.
+ * Type: {@link OperandType::TENSOR_QUANT16_SYMM}
+ * Shape: [numUnits]
+ * * 22: The cell layer normalization weights. Used to rescale
+ * normalized inputs to activation at cell gate. Optional.
+ * Type: {@link OperandType::TENSOR_QUANT16_SYMM}
+ * Shape: [numUnits]
+ * * 23: The output layer normalization weights. Used to
+ * rescale normalized inputs to activation at output gate. Optional.
+ * Type: {@link OperandType::TENSOR_QUANT16_SYMM}
+ * Shape: [numUnits]
+ * * 24: The cell clip. If provided the cell state is clipped
+ * by this value prior to the cell output activation. Optional.
+ * Type: {@link OperandType::FLOAT32}.
+ * * 25: The projection clip. If provided and projection is enabled,
+ * this is used for clipping the projected values. Optional.
+ * Type: {@link OperandType::FLOAT32}.
+ * * 26: The scale of the intermediate result of matmul,
+ * i.e. input to layer normalization, at input gate.
+ * Type: {@link OperandType::FLOAT32}.
+ * * 27: The scale of the intermediate result of matmul,
+ * i.e. input to layer normalization, at forget gate.
+ * Type: {@link OperandType::FLOAT32}.
+ * * 28: The scale of the intermediate result of matmul,
+ * i.e. input to layer normalization, at cell gate.
+ * Type: {@link OperandType::FLOAT32}.
+ * * 29: The scale of the intermediate result of matmul,
+ * i.e. input to layer normalization, at output gate.
+ * Type: {@link OperandType::FLOAT32}.
+ * * 30: The zero point of the hidden state, i.e. input to
+ * projection.
+ * Type: {@link OperandType::INT32}.
+ * * 31: The scale of the hidden state, i.e. input to
+ * projection.
+ * Type: {@link OperandType::FLOAT32}.
+ *
+ * Outputs:
+ * * 0: The output state (out).
+ * Type: {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ * Shape: [batchSize, outputSize]
+ * * 1: The cell state (out).
+ * Type: {@link OperandType::TENSOR_QUANT16_SYMM}
+ * Shape: [batchSize, numUnits]
+ * * 2: The output. This is effectively the same as the current
+ * "output state (out)" value.
+ * Type: {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ * Shape: [batchSize, outputSize]
+ */
+ QUANTIZED_LSTM = 95,
+ /**
+ * Executes one of the two referenced subgraphs as determined by a boolean
+ * value.
+ *
+ * The inputs and outputs of the two referenced subgraphs must agree with the
+ * signature of this operation. That is, if the operation has (3 + n) inputs
+ * and m outputs, both subgraphs must have n inputs and m outputs with the same
+ * types, ranks, dimensions, scales,
+ * zeroPoints, and extraParams as the corresponding operation
+ * inputs and outputs.
+ * All of the operands mentioned must have fully specified dimensions.
+ *
+ * Inputs:
+ * * 0: A value of type {@link OperandType::TENSOR_BOOL8} and shape [1]
+ * that determines which of the two referenced subgraphs to execute.
+ * The operand must have fully specified dimensions.
+ * * 1: A {@link OperandType::SUBGRAPH} reference to the subgraph to be
+ * executed if the condition is true.
+ * * 2: A {@link OperandType::SUBGRAPH} reference to the subgraph to be
+ * executed if the condition is false.
+ * * 3 ~ (n + 2): Inputs to be passed to the subgraph selected for execution.
+ *
+ * Outputs:
+ * * 0 ~ (m - 1): Outputs produced by the selected subgraph.
+ */
+ IF = 96,
+ /**
+ * Executes the body subgraph until the condition subgraph outputs false.
+ *
+ * The inputs to this operation are the condition subgraph, the body subgraph,
+ * and operand values for the first iteration of the loop. The values are
+ * implicitly split into three groups of input-output, state-only, and
+ * input-only values, as described below.
+ *
+ * The outputs of this operation are the final values of input-output
+ * operands.
+ *
+ * Both the condition and body subgraph receive (m + k + n) inputs.
+ * * The first m (m >= 1) inputs are input-output operands. For the first
+ * iteration, these are initialized from the corresponding inputs of the
+ * WHILE operation. In subsequent iterations, their values come from the
+ * corresponding outputs of the body subgraph produced during the previous
+ * iteration.
+ * * The next k (k >= 0) inputs are state-only operands. They are similar to
+ * the input-output operands, except that their values are no longer
+ * available after the loop terminates.
+ * * The last n (n >= 0) inputs are input-only operands. Their values come
+ * from the corresponding inputs of the WHILE operation.
+ *
+ * The body subgraph produces (m + k) outputs.
+ * * The first m outputs are input-output operands. They become the outputs
+ * of the WHILE operation when a termination condition is reached.
+ * * The last k outputs are state-only operands. Their values are no longer
+ * available after the loop terminates.
+ *
+ * The numbers m, k, and n are inferred by the driver as follows:
+ * m = (WHILE operation output count)
+ * k = (body subgraph output count) - m
+ * n = (body subgraph input count) - m - k
+ *
+ * The pseudo-code below illustrates the flow of a WHILE operation with
+ * inputs condition, body, initial_input_output, initial_state, input_only
+ * (m = 1, k = 1, n = 1):
+ *
+ * input_output = initial_input_output
+ * state = initial_state
+ * while condition(input_output, state, input_only):
+ * input_output, state = body(input_output, state, input_only)
+ * return input_output
+ *
+ * Inputs:
+ * * 0: A {@link OperandType::SUBGRAPH} reference to the condition
+ * subgraph. The subgraph must have (m + k + n) inputs with
+ * the same types, ranks, dimensions,
+ * scales, zeroPoints, and extraParams as the
+ * corresponding inputs of the WHILE operation and exactly one output
+ * of {@link OperandType::TENSOR_BOOL8} and shape [1].
+ * All of the operands mentioned must have fully specified dimensions.
+ * * 1: A {@link OperandType::SUBGRAPH} reference to the body subgraph.
+ * The subgraph must have (m + k + n) inputs and (m + k) outputs with
+ * the same types, ranks, dimensions,
+ * scales, zeroPoints, and extraParams as the
+ * corresponding inputs and outputs of the WHILE operation.
+ * All of the operands mentioned must have fully specified dimensions.
+ * * (m inputs): Initial values for input-output operands.
+ * * (k inputs): Initial values for state-only operands.
+ * * (n inputs): Values for input-only operands.
+ *
+ * Outputs:
+ * * 0 ~ (m - 1): Outputs produced by the loop.
+ */
+ WHILE = 97,
+ /**
+ * Computes exponential linear activation on the input tensor element-wise.
+ *
+ * The output is calculated using the following formula:
+ *
+ * ELU(x) = max(0, x) + min(0, alpha * (exp(x) - 1))
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input. May be zero-sized.
+ * * 1: A scalar, specifying the alpha parameter.
+ * 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}.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape and type as input0.
+ */
+ ELU = 98,
+ /**
+ * Computes hard-swish activation on the input tensor element-wise.
+ *
+ * Hard swish activation is introduced in
+ * https://arxiv.org/pdf/1905.02244.pdf
+ *
+ * The output is calculated using the following formula:
+ *
+ * h-swish(x) = x * max(0, min(6, (x + 3))) / 6
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input. May be zero-sized.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape and type as input0.
+ * Scale and zero point of this tensor may be different from the input
+ * tensor's parameters.
+ */
+ HARD_SWISH = 99,
+ /**
+ * Creates a tensor filled with a scalar value.
+ *
+ * Supported output tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A 1-D tensor, specifying the desired output tensor shape.
+ * * 1: A scalar, specifying the value to fill the output tensors with.
+ * For output tensor of {@link OperandType::TENSOR_FLOAT16},
+ * the scalar must be of {@link OperandType::FLOAT16}.
+ * For output tensor of {@link OperandType::TENSOR_FLOAT32},
+ * the scalar must be of {@link OperandType::FLOAT32}.
+ * For output tensor of {@link OperandType::TENSOR_INT32},
+ * the scalar must be of {@link OperandType::INT32}.
+ *
+ * Outputs:
+ * * 0: The output tensor.
+ */
+ FILL = 100,
+ /**
+ * Returns the rank of a tensor.
+ *
+ * The rank of a tensor is the number of dimensions in it. Also known as
+ * "order", "degree", "ndims".
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT16_SYMM}
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+ * * {@link OperandType::TENSOR_QUANT16_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_SYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: The input tensor.
+ *
+ * Outputs:
+ * * 0: A scalar of {@link OperandType::INT32}, specifying the rank
+ * of the input tensor.
+ */
+ RANK = 101,
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/OutputShape.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/OutputShape.aidl
new file mode 100644
index 0000000..d206a25
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/OutputShape.aidl
@@ -0,0 +1,33 @@
+/*
+ * Copyright (C) 2020 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;
+
+/**
+ * Describes the shape information of an output operand after execution.
+ */
+@VintfStability
+parcelable OutputShape {
+ /**
+ * Dimensions of the operand.
+ */
+ int[] dimensions;
+ /**
+ * Whether the provided buffer size is sufficient for the output.
+ */
+ boolean isSufficient;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/PerformanceInfo.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/PerformanceInfo.aidl
new file mode 100644
index 0000000..6ee29c2
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/PerformanceInfo.aidl
@@ -0,0 +1,37 @@
+/*
+ * Copyright (C) 2020 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;
+
+/**
+ * Performance information for the reference workload.
+ *
+ * Used by a driver to report its performance characteristics.
+ */
+@VintfStability
+parcelable 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;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/Priority.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/Priority.aidl
new file mode 100644
index 0000000..fe87598
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/Priority.aidl
@@ -0,0 +1,29 @@
+/*
+ * Copyright (C) 2020 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;
+
+/**
+ * Priority given to a prepared model for execution.
+ */
+@VintfStability
+@Backing(type="int")
+enum Priority {
+ LOW,
+ MEDIUM,
+ HIGH,
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/Request.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/Request.aidl
new file mode 100644
index 0000000..396ff30
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/Request.aidl
@@ -0,0 +1,55 @@
+/*
+ * Copyright (C) 2020 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;
+
+import android.hardware.neuralnetworks.RequestArgument;
+import android.hardware.neuralnetworks.RequestMemoryPool;
+
+/**
+ * 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_POOL.
+ */
+@VintfStability
+parcelable 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.main.inputIndexes.
+ * E.g., input[i] corresponds to Model.main.inputIndexes[i].
+ */
+ 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.main.outputIndexes.
+ * E.g., output[i] corresponds to Model.main.outputIndexes[i].
+ */
+ RequestArgument[] outputs;
+ /**
+ * A collection of memory pools containing operand data for both the inputs and the outputs to a
+ * model.
+ */
+ RequestMemoryPool[] pools;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/RequestArgument.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/RequestArgument.aidl
new file mode 100644
index 0000000..e615fa6
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/RequestArgument.aidl
@@ -0,0 +1,53 @@
+/*
+ * Copyright (C) 2020 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;
+
+import android.hardware.neuralnetworks.DataLocation;
+
+/**
+ * Metadata information specifying the location of the input or output data and any updates to the
+ * input or output operand.
+ */
+@VintfStability
+parcelable 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.
+ */
+ boolean 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.
+ */
+ int[] dimensions;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/RequestMemoryPool.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/RequestMemoryPool.aidl
new file mode 100644
index 0000000..166746d
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/RequestMemoryPool.aidl
@@ -0,0 +1,36 @@
+/*
+ * Copyright (C) 2020 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;
+
+import android.hardware.neuralnetworks.Memory;
+
+/**
+ * A memory pool.
+ */
+@VintfStability
+union RequestMemoryPool {
+ /**
+ * Specifies a client-managed shared memory pool.
+ */
+ Memory pool;
+ /**
+ * Specifies a driver-managed buffer. It is the token returned from IDevice::allocate, and is
+ * specific to the IDevice object.
+ */
+ int token;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/Subgraph.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/Subgraph.aidl
new file mode 100644
index 0000000..0a76285
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/Subgraph.aidl
@@ -0,0 +1,51 @@
+/*
+ * Copyright (C) 2020 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;
+
+import android.hardware.neuralnetworks.Operand;
+import android.hardware.neuralnetworks.Operation;
+
+/**
+ * An excerpt of the execution graph.
+ */
+@VintfStability
+parcelable Subgraph {
+ /**
+ * All operands included in the subgraph.
+ */
+ Operand[] operands;
+ /**
+ * All operations included in the subgraph.
+ *
+ * The operations are sorted into execution order. Every operand with lifetime SUBGRAPH_OUTPUT
+ * or TEMPORARY_VARIABLE must be written before it is read.
+ */
+ Operation[] operations;
+ /**
+ * Input indexes of the subgraph. There must be at least one.
+ *
+ * Each value corresponds to the index of the operand in "operands".
+ */
+ int[] inputIndexes;
+ /**
+ * Output indexes of the subgraph. There must be at least one.
+ *
+ * Each value corresponds to the index of the operand in "operands".
+ */
+ int[] outputIndexes;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/SymmPerChannelQuantParams.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/SymmPerChannelQuantParams.aidl
new file mode 100644
index 0000000..8ae41a4
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/SymmPerChannelQuantParams.aidl
@@ -0,0 +1,33 @@
+/*
+ * Copyright (C) 2020 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;
+
+/**
+ * Parameters for TENSOR_QUANT8_SYMM_PER_CHANNEL operand.
+ */
+@VintfStability
+parcelable SymmPerChannelQuantParams {
+ /**
+ * Array of scaling values for each channel. Each value must be greater than zero.
+ */
+ float[] scales;
+ /**
+ * Index of the channel dimension
+ */
+ int channelDim;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/Timing.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/Timing.aidl
new file mode 100644
index 0000000..b04f74e
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/Timing.aidl
@@ -0,0 +1,37 @@
+/*
+ * Copyright (C) 2020 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;
+
+/**
+ * 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 nanoseconds. When a time is not available, it must be reported as -1.
+ */
+@VintfStability
+parcelable Timing {
+ /**
+ * Execution time on device (not driver, which runs on host processor).
+ */
+ long timeOnDevice;
+ /**
+ * Execution time in driver (including time on device).
+ */
+ long timeInDriver;
+}
diff --git a/neuralnetworks/aidl/utils/Android.bp b/neuralnetworks/aidl/utils/Android.bp
new file mode 100644
index 0000000..56017da
--- /dev/null
+++ b/neuralnetworks/aidl/utils/Android.bp
@@ -0,0 +1,32 @@
+//
+// Copyright (C) 2021 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_library_static {
+ name: "neuralnetworks_utils_hal_aidl",
+ defaults: ["neuralnetworks_utils_defaults"],
+ srcs: ["src/*"],
+ local_include_dirs: ["include/nnapi/hal/aidl/"],
+ export_include_dirs: ["include"],
+ static_libs: [
+ "neuralnetworks_types",
+ "neuralnetworks_utils_hal_common",
+ ],
+ shared_libs: [
+ "libhidlbase",
+ "android.hardware.neuralnetworks-V1-ndk_platform",
+ "libbinder_ndk",
+ ],
+}
diff --git a/neuralnetworks/aidl/utils/OWNERS b/neuralnetworks/aidl/utils/OWNERS
new file mode 100644
index 0000000..e4feee3
--- /dev/null
+++ b/neuralnetworks/aidl/utils/OWNERS
@@ -0,0 +1,11 @@
+# Neuralnetworks team
+butlermichael@google.com
+dgross@google.com
+galarragas@google.com
+jeanluc@google.com
+levp@google.com
+miaowang@google.com
+pszczepaniak@google.com
+slavash@google.com
+vddang@google.com
+xusongw@google.com
diff --git a/neuralnetworks/aidl/utils/include/nnapi/hal/aidl/Conversions.h b/neuralnetworks/aidl/utils/include/nnapi/hal/aidl/Conversions.h
new file mode 100644
index 0000000..35de5be
--- /dev/null
+++ b/neuralnetworks/aidl/utils/include/nnapi/hal/aidl/Conversions.h
@@ -0,0 +1,134 @@
+/*
+ * Copyright (C) 2021 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_INTERFACES_NEURALNETWORKS_AIDL_CONVERSIONS_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_AIDL_CONVERSIONS_H
+
+#include <aidl/android/hardware/neuralnetworks/BufferDesc.h>
+#include <aidl/android/hardware/neuralnetworks/BufferRole.h>
+#include <aidl/android/hardware/neuralnetworks/Capabilities.h>
+#include <aidl/android/hardware/neuralnetworks/DataLocation.h>
+#include <aidl/android/hardware/neuralnetworks/DeviceType.h>
+#include <aidl/android/hardware/neuralnetworks/ErrorStatus.h>
+#include <aidl/android/hardware/neuralnetworks/ExecutionPreference.h>
+#include <aidl/android/hardware/neuralnetworks/Extension.h>
+#include <aidl/android/hardware/neuralnetworks/ExtensionNameAndPrefix.h>
+#include <aidl/android/hardware/neuralnetworks/ExtensionOperandTypeInformation.h>
+#include <aidl/android/hardware/neuralnetworks/Memory.h>
+#include <aidl/android/hardware/neuralnetworks/Model.h>
+#include <aidl/android/hardware/neuralnetworks/Operand.h>
+#include <aidl/android/hardware/neuralnetworks/OperandExtraParams.h>
+#include <aidl/android/hardware/neuralnetworks/OperandLifeTime.h>
+#include <aidl/android/hardware/neuralnetworks/OperandPerformance.h>
+#include <aidl/android/hardware/neuralnetworks/OperandType.h>
+#include <aidl/android/hardware/neuralnetworks/Operation.h>
+#include <aidl/android/hardware/neuralnetworks/OperationType.h>
+#include <aidl/android/hardware/neuralnetworks/OutputShape.h>
+#include <aidl/android/hardware/neuralnetworks/PerformanceInfo.h>
+#include <aidl/android/hardware/neuralnetworks/Priority.h>
+#include <aidl/android/hardware/neuralnetworks/Request.h>
+#include <aidl/android/hardware/neuralnetworks/RequestArgument.h>
+#include <aidl/android/hardware/neuralnetworks/RequestMemoryPool.h>
+#include <aidl/android/hardware/neuralnetworks/Subgraph.h>
+#include <aidl/android/hardware/neuralnetworks/SymmPerChannelQuantParams.h>
+#include <aidl/android/hardware/neuralnetworks/Timing.h>
+
+#include <nnapi/Result.h>
+#include <nnapi/Types.h>
+#include <nnapi/hal/CommonUtils.h>
+
+#include <vector>
+
+namespace android::nn {
+
+GeneralResult<OperandType> unvalidatedConvert(const aidl_hal::OperandType& operandType);
+GeneralResult<OperationType> unvalidatedConvert(const aidl_hal::OperationType& operationType);
+GeneralResult<DeviceType> unvalidatedConvert(const aidl_hal::DeviceType& deviceType);
+GeneralResult<Priority> unvalidatedConvert(const aidl_hal::Priority& priority);
+GeneralResult<Capabilities> unvalidatedConvert(const aidl_hal::Capabilities& capabilities);
+GeneralResult<Capabilities::OperandPerformance> unvalidatedConvert(
+ const aidl_hal::OperandPerformance& operandPerformance);
+GeneralResult<Capabilities::PerformanceInfo> unvalidatedConvert(
+ const aidl_hal::PerformanceInfo& performanceInfo);
+GeneralResult<DataLocation> unvalidatedConvert(const aidl_hal::DataLocation& location);
+GeneralResult<Operand> unvalidatedConvert(const aidl_hal::Operand& operand);
+GeneralResult<Operand::ExtraParams> unvalidatedConvert(
+ const std::optional<aidl_hal::OperandExtraParams>& optionalExtraParams);
+GeneralResult<Operand::LifeTime> unvalidatedConvert(
+ const aidl_hal::OperandLifeTime& operandLifeTime);
+GeneralResult<Operand::SymmPerChannelQuantParams> unvalidatedConvert(
+ const aidl_hal::SymmPerChannelQuantParams& symmPerChannelQuantParams);
+GeneralResult<Operation> unvalidatedConvert(const aidl_hal::Operation& operation);
+GeneralResult<Model> unvalidatedConvert(const aidl_hal::Model& model);
+GeneralResult<Model::ExtensionNameAndPrefix> unvalidatedConvert(
+ const aidl_hal::ExtensionNameAndPrefix& extensionNameAndPrefix);
+GeneralResult<Model::OperandValues> unvalidatedConvert(const std::vector<uint8_t>& operandValues);
+GeneralResult<Model::Subgraph> unvalidatedConvert(const aidl_hal::Subgraph& subgraph);
+GeneralResult<OutputShape> unvalidatedConvert(const aidl_hal::OutputShape& outputShape);
+GeneralResult<MeasureTiming> unvalidatedConvert(bool measureTiming);
+GeneralResult<Memory> unvalidatedConvert(const aidl_hal::Memory& memory);
+GeneralResult<Timing> unvalidatedConvert(const aidl_hal::Timing& timing);
+GeneralResult<BufferDesc> unvalidatedConvert(const aidl_hal::BufferDesc& bufferDesc);
+GeneralResult<BufferRole> unvalidatedConvert(const aidl_hal::BufferRole& bufferRole);
+GeneralResult<Request> unvalidatedConvert(const aidl_hal::Request& request);
+GeneralResult<Request::Argument> unvalidatedConvert(
+ const aidl_hal::RequestArgument& requestArgument);
+GeneralResult<Request::MemoryPool> unvalidatedConvert(
+ const aidl_hal::RequestMemoryPool& memoryPool);
+GeneralResult<ErrorStatus> unvalidatedConvert(const aidl_hal::ErrorStatus& errorStatus);
+GeneralResult<ExecutionPreference> unvalidatedConvert(
+ const aidl_hal::ExecutionPreference& executionPreference);
+GeneralResult<Extension> unvalidatedConvert(const aidl_hal::Extension& extension);
+GeneralResult<Extension::OperandTypeInformation> unvalidatedConvert(
+ const aidl_hal::ExtensionOperandTypeInformation& operandTypeInformation);
+GeneralResult<SharedHandle> unvalidatedConvert(
+ const ::aidl::android::hardware::common::NativeHandle& handle);
+
+GeneralResult<ExecutionPreference> convert(
+ const aidl_hal::ExecutionPreference& executionPreference);
+GeneralResult<Memory> convert(const aidl_hal::Memory& memory);
+GeneralResult<Model> convert(const aidl_hal::Model& model);
+GeneralResult<Operand> convert(const aidl_hal::Operand& operand);
+GeneralResult<OperandType> convert(const aidl_hal::OperandType& operandType);
+GeneralResult<Priority> convert(const aidl_hal::Priority& priority);
+GeneralResult<Request::MemoryPool> convert(const aidl_hal::RequestMemoryPool& memoryPool);
+GeneralResult<Request> convert(const aidl_hal::Request& request);
+
+GeneralResult<std::vector<Operation>> convert(const std::vector<aidl_hal::Operation>& outputShapes);
+GeneralResult<std::vector<Memory>> convert(const std::vector<aidl_hal::Memory>& memories);
+
+GeneralResult<std::vector<uint32_t>> toUnsigned(const std::vector<int32_t>& vec);
+
+} // namespace android::nn
+
+namespace aidl::android::hardware::neuralnetworks::utils {
+
+namespace nn = ::android::nn;
+
+nn::GeneralResult<Memory> unvalidatedConvert(const nn::Memory& memory);
+nn::GeneralResult<OutputShape> unvalidatedConvert(const nn::OutputShape& outputShape);
+nn::GeneralResult<ErrorStatus> unvalidatedConvert(const nn::ErrorStatus& errorStatus);
+
+nn::GeneralResult<Memory> convert(const nn::Memory& memory);
+nn::GeneralResult<ErrorStatus> convert(const nn::ErrorStatus& errorStatus);
+nn::GeneralResult<std::vector<OutputShape>> convert(
+ const std::vector<nn::OutputShape>& outputShapes);
+
+nn::GeneralResult<std::vector<int32_t>> toSigned(const std::vector<uint32_t>& vec);
+
+} // namespace aidl::android::hardware::neuralnetworks::utils
+
+#endif // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_AIDL_CONVERSIONS_H
diff --git a/neuralnetworks/aidl/utils/include/nnapi/hal/aidl/Utils.h b/neuralnetworks/aidl/utils/include/nnapi/hal/aidl/Utils.h
new file mode 100644
index 0000000..79b511d
--- /dev/null
+++ b/neuralnetworks/aidl/utils/include/nnapi/hal/aidl/Utils.h
@@ -0,0 +1,57 @@
+/*
+ * Copyright (C) 2021 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_INTERFACES_NEURALNETWORKS_AIDL_UTILS_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_AIDL_UTILS_H
+
+#include "nnapi/hal/aidl/Conversions.h"
+
+#include <android-base/logging.h>
+#include <nnapi/Result.h>
+#include <nnapi/Types.h>
+#include <nnapi/Validation.h>
+
+namespace aidl::android::hardware::neuralnetworks::utils {
+
+constexpr auto kDefaultPriority = Priority::MEDIUM;
+constexpr auto kVersion = nn::Version::ANDROID_S;
+
+template <typename Type>
+nn::Result<void> validate(const Type& halObject) {
+ const auto maybeCanonical = nn::convert(halObject);
+ if (!maybeCanonical.has_value()) {
+ return nn::error() << maybeCanonical.error().message;
+ }
+ return {};
+}
+
+template <typename Type>
+bool valid(const Type& halObject) {
+ const auto result = utils::validate(halObject);
+ if (!result.has_value()) {
+ LOG(ERROR) << result.error();
+ }
+ return result.has_value();
+}
+
+nn::GeneralResult<Memory> clone(const Memory& memory);
+nn::GeneralResult<Request> clone(const Request& request);
+nn::GeneralResult<RequestMemoryPool> clone(const RequestMemoryPool& requestPool);
+nn::GeneralResult<Model> clone(const Model& model);
+
+} // namespace aidl::android::hardware::neuralnetworks::utils
+
+#endif // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_AIDL_UTILS_H
diff --git a/neuralnetworks/aidl/utils/src/Assertions.cpp b/neuralnetworks/aidl/utils/src/Assertions.cpp
new file mode 100644
index 0000000..0e88091
--- /dev/null
+++ b/neuralnetworks/aidl/utils/src/Assertions.cpp
@@ -0,0 +1,269 @@
+/*
+ * Copyright (C) 2021 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 <aidl/android/hardware/neuralnetworks/DeviceType.h>
+#include <aidl/android/hardware/neuralnetworks/ErrorStatus.h>
+#include <aidl/android/hardware/neuralnetworks/ExecutionPreference.h>
+#include <aidl/android/hardware/neuralnetworks/FusedActivationFunc.h>
+#include <aidl/android/hardware/neuralnetworks/IDevice.h>
+#include <aidl/android/hardware/neuralnetworks/OperandLifeTime.h>
+#include <aidl/android/hardware/neuralnetworks/OperandType.h>
+#include <aidl/android/hardware/neuralnetworks/OperationType.h>
+#include <aidl/android/hardware/neuralnetworks/Priority.h>
+
+#include <ControlFlow.h>
+#include <nnapi/OperandTypes.h>
+#include <nnapi/OperationTypes.h>
+#include <nnapi/Types.h>
+#include <type_traits>
+
+namespace {
+
+#define COMPARE_ENUMS_TYPES(lhsType, rhsType) \
+ static_assert( \
+ std::is_same_v< \
+ std::underlying_type_t<::aidl::android::hardware::neuralnetworks::lhsType>, \
+ std::underlying_type_t<::android::nn::rhsType>>, \
+ "::aidl::android::hardware::neuralnetworks::" #lhsType \
+ " does not have the same underlying type as ::android::nn::" #rhsType)
+
+COMPARE_ENUMS_TYPES(OperandType, OperandType);
+COMPARE_ENUMS_TYPES(OperationType, OperationType);
+COMPARE_ENUMS_TYPES(Priority, Priority);
+COMPARE_ENUMS_TYPES(OperandLifeTime, Operand::LifeTime);
+COMPARE_ENUMS_TYPES(ErrorStatus, ErrorStatus);
+
+#undef COMPARE_ENUMS_TYPES
+
+#define COMPARE_ENUMS_FULL(lhsSymbol, rhsSymbol, lhsType, rhsType) \
+ static_assert( \
+ static_cast< \
+ std::underlying_type_t<::aidl::android::hardware::neuralnetworks::lhsType>>( \
+ ::aidl::android::hardware::neuralnetworks::lhsType::lhsSymbol) == \
+ static_cast<std::underlying_type_t<::android::nn::rhsType>>( \
+ ::android::nn::rhsType::rhsSymbol), \
+ "::aidl::android::hardware::neuralnetworks::" #lhsType "::" #lhsSymbol \
+ " does not match ::android::nn::" #rhsType "::" #rhsSymbol)
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, symbol, OperandType, OperandType)
+
+COMPARE_ENUMS(FLOAT32);
+COMPARE_ENUMS(INT32);
+COMPARE_ENUMS(UINT32);
+COMPARE_ENUMS(TENSOR_FLOAT32);
+COMPARE_ENUMS(TENSOR_INT32);
+COMPARE_ENUMS(TENSOR_QUANT8_ASYMM);
+COMPARE_ENUMS(BOOL);
+COMPARE_ENUMS(TENSOR_QUANT16_SYMM);
+COMPARE_ENUMS(TENSOR_FLOAT16);
+COMPARE_ENUMS(TENSOR_BOOL8);
+COMPARE_ENUMS(FLOAT16);
+COMPARE_ENUMS(TENSOR_QUANT8_SYMM_PER_CHANNEL);
+COMPARE_ENUMS(TENSOR_QUANT16_ASYMM);
+COMPARE_ENUMS(TENSOR_QUANT8_SYMM);
+COMPARE_ENUMS(TENSOR_QUANT8_ASYMM_SIGNED);
+COMPARE_ENUMS(SUBGRAPH);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, symbol, OperationType, OperationType)
+
+COMPARE_ENUMS(ADD);
+COMPARE_ENUMS(AVERAGE_POOL_2D);
+COMPARE_ENUMS(CONCATENATION);
+COMPARE_ENUMS(CONV_2D);
+COMPARE_ENUMS(DEPTHWISE_CONV_2D);
+COMPARE_ENUMS(DEPTH_TO_SPACE);
+COMPARE_ENUMS(DEQUANTIZE);
+COMPARE_ENUMS(EMBEDDING_LOOKUP);
+COMPARE_ENUMS(FLOOR);
+COMPARE_ENUMS(FULLY_CONNECTED);
+COMPARE_ENUMS(HASHTABLE_LOOKUP);
+COMPARE_ENUMS(L2_NORMALIZATION);
+COMPARE_ENUMS(L2_POOL_2D);
+COMPARE_ENUMS(LOCAL_RESPONSE_NORMALIZATION);
+COMPARE_ENUMS(LOGISTIC);
+COMPARE_ENUMS(LSH_PROJECTION);
+COMPARE_ENUMS(LSTM);
+COMPARE_ENUMS(MAX_POOL_2D);
+COMPARE_ENUMS(MUL);
+COMPARE_ENUMS(RELU);
+COMPARE_ENUMS(RELU1);
+COMPARE_ENUMS(RELU6);
+COMPARE_ENUMS(RESHAPE);
+COMPARE_ENUMS(RESIZE_BILINEAR);
+COMPARE_ENUMS(RNN);
+COMPARE_ENUMS(SOFTMAX);
+COMPARE_ENUMS(SPACE_TO_DEPTH);
+COMPARE_ENUMS(SVDF);
+COMPARE_ENUMS(TANH);
+COMPARE_ENUMS(BATCH_TO_SPACE_ND);
+COMPARE_ENUMS(DIV);
+COMPARE_ENUMS(MEAN);
+COMPARE_ENUMS(PAD);
+COMPARE_ENUMS(SPACE_TO_BATCH_ND);
+COMPARE_ENUMS(SQUEEZE);
+COMPARE_ENUMS(STRIDED_SLICE);
+COMPARE_ENUMS(SUB);
+COMPARE_ENUMS(TRANSPOSE);
+COMPARE_ENUMS(ABS);
+COMPARE_ENUMS(ARGMAX);
+COMPARE_ENUMS(ARGMIN);
+COMPARE_ENUMS(AXIS_ALIGNED_BBOX_TRANSFORM);
+COMPARE_ENUMS(BIDIRECTIONAL_SEQUENCE_LSTM);
+COMPARE_ENUMS(BIDIRECTIONAL_SEQUENCE_RNN);
+COMPARE_ENUMS(BOX_WITH_NMS_LIMIT);
+COMPARE_ENUMS(CAST);
+COMPARE_ENUMS(CHANNEL_SHUFFLE);
+COMPARE_ENUMS(DETECTION_POSTPROCESSING);
+COMPARE_ENUMS(EQUAL);
+COMPARE_ENUMS(EXP);
+COMPARE_ENUMS(EXPAND_DIMS);
+COMPARE_ENUMS(GATHER);
+COMPARE_ENUMS(GENERATE_PROPOSALS);
+COMPARE_ENUMS(GREATER);
+COMPARE_ENUMS(GREATER_EQUAL);
+COMPARE_ENUMS(GROUPED_CONV_2D);
+COMPARE_ENUMS(HEATMAP_MAX_KEYPOINT);
+COMPARE_ENUMS(INSTANCE_NORMALIZATION);
+COMPARE_ENUMS(LESS);
+COMPARE_ENUMS(LESS_EQUAL);
+COMPARE_ENUMS(LOG);
+COMPARE_ENUMS(LOGICAL_AND);
+COMPARE_ENUMS(LOGICAL_NOT);
+COMPARE_ENUMS(LOGICAL_OR);
+COMPARE_ENUMS(LOG_SOFTMAX);
+COMPARE_ENUMS(MAXIMUM);
+COMPARE_ENUMS(MINIMUM);
+COMPARE_ENUMS(NEG);
+COMPARE_ENUMS(NOT_EQUAL);
+COMPARE_ENUMS(PAD_V2);
+COMPARE_ENUMS(POW);
+COMPARE_ENUMS(PRELU);
+COMPARE_ENUMS(QUANTIZE);
+COMPARE_ENUMS(QUANTIZED_16BIT_LSTM);
+COMPARE_ENUMS(RANDOM_MULTINOMIAL);
+COMPARE_ENUMS(REDUCE_ALL);
+COMPARE_ENUMS(REDUCE_ANY);
+COMPARE_ENUMS(REDUCE_MAX);
+COMPARE_ENUMS(REDUCE_MIN);
+COMPARE_ENUMS(REDUCE_PROD);
+COMPARE_ENUMS(REDUCE_SUM);
+COMPARE_ENUMS(ROI_ALIGN);
+COMPARE_ENUMS(ROI_POOLING);
+COMPARE_ENUMS(RSQRT);
+COMPARE_ENUMS(SELECT);
+COMPARE_ENUMS(SIN);
+COMPARE_ENUMS(SLICE);
+COMPARE_ENUMS(SPLIT);
+COMPARE_ENUMS(SQRT);
+COMPARE_ENUMS(TILE);
+COMPARE_ENUMS(TOPK_V2);
+COMPARE_ENUMS(TRANSPOSE_CONV_2D);
+COMPARE_ENUMS(UNIDIRECTIONAL_SEQUENCE_LSTM);
+COMPARE_ENUMS(UNIDIRECTIONAL_SEQUENCE_RNN);
+COMPARE_ENUMS(RESIZE_NEAREST_NEIGHBOR);
+COMPARE_ENUMS(QUANTIZED_LSTM);
+COMPARE_ENUMS(IF);
+COMPARE_ENUMS(WHILE);
+COMPARE_ENUMS(ELU);
+COMPARE_ENUMS(HARD_SWISH);
+COMPARE_ENUMS(FILL);
+COMPARE_ENUMS(RANK);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, symbol, Priority, Priority)
+
+COMPARE_ENUMS(LOW);
+COMPARE_ENUMS(MEDIUM);
+COMPARE_ENUMS(HIGH);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(lhsSymbol, rhsSymbol) \
+ COMPARE_ENUMS_FULL(lhsSymbol, rhsSymbol, OperandLifeTime, Operand::LifeTime)
+
+COMPARE_ENUMS(TEMPORARY_VARIABLE, TEMPORARY_VARIABLE);
+COMPARE_ENUMS(SUBGRAPH_INPUT, SUBGRAPH_INPUT);
+COMPARE_ENUMS(SUBGRAPH_OUTPUT, SUBGRAPH_OUTPUT);
+COMPARE_ENUMS(CONSTANT_COPY, CONSTANT_COPY);
+COMPARE_ENUMS(CONSTANT_POOL, CONSTANT_REFERENCE);
+COMPARE_ENUMS(NO_VALUE, NO_VALUE);
+COMPARE_ENUMS(SUBGRAPH, SUBGRAPH);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, symbol, ErrorStatus, ErrorStatus)
+
+COMPARE_ENUMS(NONE);
+COMPARE_ENUMS(DEVICE_UNAVAILABLE);
+COMPARE_ENUMS(GENERAL_FAILURE);
+COMPARE_ENUMS(OUTPUT_INSUFFICIENT_SIZE);
+COMPARE_ENUMS(INVALID_ARGUMENT);
+COMPARE_ENUMS(MISSED_DEADLINE_TRANSIENT);
+COMPARE_ENUMS(MISSED_DEADLINE_PERSISTENT);
+COMPARE_ENUMS(RESOURCE_EXHAUSTED_TRANSIENT);
+COMPARE_ENUMS(RESOURCE_EXHAUSTED_PERSISTENT);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) \
+ COMPARE_ENUMS_FULL(symbol, symbol, ExecutionPreference, ExecutionPreference)
+
+COMPARE_ENUMS(LOW_POWER);
+COMPARE_ENUMS(FAST_SINGLE_ANSWER);
+COMPARE_ENUMS(SUSTAINED_SPEED);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, symbol, DeviceType, DeviceType)
+
+COMPARE_ENUMS(OTHER);
+COMPARE_ENUMS(CPU);
+COMPARE_ENUMS(GPU);
+COMPARE_ENUMS(ACCELERATOR);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) \
+ COMPARE_ENUMS_FULL(symbol, symbol, FusedActivationFunc, FusedActivationFunc)
+
+COMPARE_ENUMS(NONE);
+COMPARE_ENUMS(RELU);
+COMPARE_ENUMS(RELU1);
+COMPARE_ENUMS(RELU6);
+
+#undef COMPARE_ENUMS
+
+#undef COMPARE_ENUMS_FULL
+
+#define COMPARE_CONSTANTS(halSymbol, canonicalSymbol) \
+ static_assert(::aidl::android::hardware::neuralnetworks::halSymbol == \
+ ::android::nn::canonicalSymbol);
+
+COMPARE_CONSTANTS(IDevice::BYTE_SIZE_OF_CACHE_TOKEN, kByteSizeOfCacheToken);
+COMPARE_CONSTANTS(IDevice::MAX_NUMBER_OF_CACHE_FILES, kMaxNumberOfCacheFiles);
+COMPARE_CONSTANTS(IDevice::EXTENSION_TYPE_HIGH_BITS_PREFIX, kExtensionPrefixBits - 1);
+COMPARE_CONSTANTS(IDevice::EXTENSION_TYPE_LOW_BITS_TYPE, kExtensionTypeBits);
+COMPARE_CONSTANTS(IPreparedModel::DEFAULT_LOOP_TIMEOUT_DURATION_NS,
+ operation_while::kTimeoutNsDefault);
+COMPARE_CONSTANTS(IPreparedModel::MAXIMUM_LOOP_TIMEOUT_DURATION_NS,
+ operation_while::kTimeoutNsMaximum);
+
+#undef COMPARE_CONSTANTS
+
+} // anonymous namespace
diff --git a/neuralnetworks/aidl/utils/src/Conversions.cpp b/neuralnetworks/aidl/utils/src/Conversions.cpp
new file mode 100644
index 0000000..0e93b02
--- /dev/null
+++ b/neuralnetworks/aidl/utils/src/Conversions.cpp
@@ -0,0 +1,582 @@
+/*
+ * Copyright (C) 2021 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 "Conversions.h"
+
+#include <aidl/android/hardware/common/NativeHandle.h>
+#include <android-base/logging.h>
+#include <nnapi/OperandTypes.h>
+#include <nnapi/OperationTypes.h>
+#include <nnapi/Result.h>
+#include <nnapi/SharedMemory.h>
+#include <nnapi/TypeUtils.h>
+#include <nnapi/Types.h>
+#include <nnapi/Validation.h>
+#include <nnapi/hal/CommonUtils.h>
+#include <nnapi/hal/HandleError.h>
+
+#include <algorithm>
+#include <chrono>
+#include <functional>
+#include <iterator>
+#include <limits>
+#include <type_traits>
+#include <utility>
+
+#define VERIFY_NON_NEGATIVE(value) \
+ while (UNLIKELY(value < 0)) return NN_ERROR()
+
+namespace {
+
+template <typename Type>
+constexpr std::underlying_type_t<Type> underlyingType(Type value) {
+ return static_cast<std::underlying_type_t<Type>>(value);
+}
+
+constexpr auto kVersion = android::nn::Version::ANDROID_S;
+
+} // namespace
+
+namespace android::nn {
+namespace {
+
+constexpr auto validOperandType(nn::OperandType operandType) {
+ switch (operandType) {
+ case nn::OperandType::FLOAT32:
+ case nn::OperandType::INT32:
+ case nn::OperandType::UINT32:
+ case nn::OperandType::TENSOR_FLOAT32:
+ case nn::OperandType::TENSOR_INT32:
+ case nn::OperandType::TENSOR_QUANT8_ASYMM:
+ case nn::OperandType::BOOL:
+ case nn::OperandType::TENSOR_QUANT16_SYMM:
+ case nn::OperandType::TENSOR_FLOAT16:
+ case nn::OperandType::TENSOR_BOOL8:
+ case nn::OperandType::FLOAT16:
+ case nn::OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
+ case nn::OperandType::TENSOR_QUANT16_ASYMM:
+ case nn::OperandType::TENSOR_QUANT8_SYMM:
+ case nn::OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
+ case nn::OperandType::SUBGRAPH:
+ return true;
+ case nn::OperandType::OEM:
+ case nn::OperandType::TENSOR_OEM_BYTE:
+ return false;
+ }
+ return nn::isExtension(operandType);
+}
+
+template <typename Input>
+using UnvalidatedConvertOutput =
+ std::decay_t<decltype(unvalidatedConvert(std::declval<Input>()).value())>;
+
+template <typename Type>
+GeneralResult<std::vector<UnvalidatedConvertOutput<Type>>> unvalidatedConvertVec(
+ const std::vector<Type>& arguments) {
+ std::vector<UnvalidatedConvertOutput<Type>> canonical;
+ canonical.reserve(arguments.size());
+ for (const auto& argument : arguments) {
+ canonical.push_back(NN_TRY(nn::unvalidatedConvert(argument)));
+ }
+ return canonical;
+}
+
+template <typename Type>
+GeneralResult<std::vector<UnvalidatedConvertOutput<Type>>> unvalidatedConvert(
+ const std::vector<Type>& arguments) {
+ return unvalidatedConvertVec(arguments);
+}
+
+template <typename Type>
+GeneralResult<UnvalidatedConvertOutput<Type>> validatedConvert(const Type& halObject) {
+ auto canonical = NN_TRY(nn::unvalidatedConvert(halObject));
+ const auto maybeVersion = validate(canonical);
+ if (!maybeVersion.has_value()) {
+ return error() << maybeVersion.error();
+ }
+ const auto version = maybeVersion.value();
+ if (version > kVersion) {
+ return NN_ERROR() << "Insufficient version: " << version << " vs required " << kVersion;
+ }
+ return canonical;
+}
+
+template <typename Type>
+GeneralResult<std::vector<UnvalidatedConvertOutput<Type>>> validatedConvert(
+ const std::vector<Type>& arguments) {
+ std::vector<UnvalidatedConvertOutput<Type>> canonical;
+ canonical.reserve(arguments.size());
+ for (const auto& argument : arguments) {
+ canonical.push_back(NN_TRY(validatedConvert(argument)));
+ }
+ return canonical;
+}
+
+} // anonymous namespace
+
+GeneralResult<OperandType> unvalidatedConvert(const aidl_hal::OperandType& operandType) {
+ VERIFY_NON_NEGATIVE(underlyingType(operandType)) << "Negative operand types are not allowed.";
+ return static_cast<OperandType>(operandType);
+}
+
+GeneralResult<OperationType> unvalidatedConvert(const aidl_hal::OperationType& operationType) {
+ VERIFY_NON_NEGATIVE(underlyingType(operationType))
+ << "Negative operation types are not allowed.";
+ return static_cast<OperationType>(operationType);
+}
+
+GeneralResult<DeviceType> unvalidatedConvert(const aidl_hal::DeviceType& deviceType) {
+ return static_cast<DeviceType>(deviceType);
+}
+
+GeneralResult<Priority> unvalidatedConvert(const aidl_hal::Priority& priority) {
+ return static_cast<Priority>(priority);
+}
+
+GeneralResult<Capabilities> unvalidatedConvert(const aidl_hal::Capabilities& capabilities) {
+ const bool validOperandTypes = std::all_of(
+ capabilities.operandPerformance.begin(), capabilities.operandPerformance.end(),
+ [](const aidl_hal::OperandPerformance& operandPerformance) {
+ const auto maybeType = unvalidatedConvert(operandPerformance.type);
+ return !maybeType.has_value() ? false : validOperandType(maybeType.value());
+ });
+ if (!validOperandTypes) {
+ return NN_ERROR() << "Invalid OperandType when unvalidatedConverting OperandPerformance in "
+ "Capabilities";
+ }
+
+ auto operandPerformance = NN_TRY(unvalidatedConvert(capabilities.operandPerformance));
+ auto table = NN_TRY(hal::utils::makeGeneralFailure(
+ Capabilities::OperandPerformanceTable::create(std::move(operandPerformance)),
+ nn::ErrorStatus::GENERAL_FAILURE));
+
+ return Capabilities{
+ .relaxedFloat32toFloat16PerformanceScalar = NN_TRY(
+ unvalidatedConvert(capabilities.relaxedFloat32toFloat16PerformanceScalar)),
+ .relaxedFloat32toFloat16PerformanceTensor = NN_TRY(
+ unvalidatedConvert(capabilities.relaxedFloat32toFloat16PerformanceTensor)),
+ .operandPerformance = std::move(table),
+ .ifPerformance = NN_TRY(unvalidatedConvert(capabilities.ifPerformance)),
+ .whilePerformance = NN_TRY(unvalidatedConvert(capabilities.whilePerformance)),
+ };
+}
+
+GeneralResult<Capabilities::OperandPerformance> unvalidatedConvert(
+ const aidl_hal::OperandPerformance& operandPerformance) {
+ return Capabilities::OperandPerformance{
+ .type = NN_TRY(unvalidatedConvert(operandPerformance.type)),
+ .info = NN_TRY(unvalidatedConvert(operandPerformance.info)),
+ };
+}
+
+GeneralResult<Capabilities::PerformanceInfo> unvalidatedConvert(
+ const aidl_hal::PerformanceInfo& performanceInfo) {
+ return Capabilities::PerformanceInfo{
+ .execTime = performanceInfo.execTime,
+ .powerUsage = performanceInfo.powerUsage,
+ };
+}
+
+GeneralResult<DataLocation> unvalidatedConvert(const aidl_hal::DataLocation& location) {
+ VERIFY_NON_NEGATIVE(location.poolIndex) << "DataLocation: pool index must not be negative";
+ VERIFY_NON_NEGATIVE(location.offset) << "DataLocation: offset must not be negative";
+ VERIFY_NON_NEGATIVE(location.length) << "DataLocation: length must not be negative";
+ if (location.offset > std::numeric_limits<uint32_t>::max()) {
+ return NN_ERROR() << "DataLocation: offset must be <= std::numeric_limits<uint32_t>::max()";
+ }
+ if (location.length > std::numeric_limits<uint32_t>::max()) {
+ return NN_ERROR() << "DataLocation: length must be <= std::numeric_limits<uint32_t>::max()";
+ }
+ return DataLocation{
+ .poolIndex = static_cast<uint32_t>(location.poolIndex),
+ .offset = static_cast<uint32_t>(location.offset),
+ .length = static_cast<uint32_t>(location.length),
+ };
+}
+
+GeneralResult<Operation> unvalidatedConvert(const aidl_hal::Operation& operation) {
+ return Operation{
+ .type = NN_TRY(unvalidatedConvert(operation.type)),
+ .inputs = NN_TRY(toUnsigned(operation.inputs)),
+ .outputs = NN_TRY(toUnsigned(operation.outputs)),
+ };
+}
+
+GeneralResult<Operand::LifeTime> unvalidatedConvert(
+ const aidl_hal::OperandLifeTime& operandLifeTime) {
+ return static_cast<Operand::LifeTime>(operandLifeTime);
+}
+
+GeneralResult<Operand> unvalidatedConvert(const aidl_hal::Operand& operand) {
+ return Operand{
+ .type = NN_TRY(unvalidatedConvert(operand.type)),
+ .dimensions = NN_TRY(toUnsigned(operand.dimensions)),
+ .scale = operand.scale,
+ .zeroPoint = operand.zeroPoint,
+ .lifetime = NN_TRY(unvalidatedConvert(operand.lifetime)),
+ .location = NN_TRY(unvalidatedConvert(operand.location)),
+ .extraParams = NN_TRY(unvalidatedConvert(operand.extraParams)),
+ };
+}
+
+GeneralResult<Operand::ExtraParams> unvalidatedConvert(
+ const std::optional<aidl_hal::OperandExtraParams>& optionalExtraParams) {
+ if (!optionalExtraParams.has_value()) {
+ return Operand::NoParams{};
+ }
+ const auto& extraParams = optionalExtraParams.value();
+ using Tag = aidl_hal::OperandExtraParams::Tag;
+ switch (extraParams.getTag()) {
+ case Tag::channelQuant:
+ return unvalidatedConvert(extraParams.get<Tag::channelQuant>());
+ case Tag::extension:
+ return extraParams.get<Tag::extension>();
+ }
+ return NN_ERROR() << "Unrecognized Operand::ExtraParams tag: "
+ << underlyingType(extraParams.getTag());
+}
+
+GeneralResult<Operand::SymmPerChannelQuantParams> unvalidatedConvert(
+ const aidl_hal::SymmPerChannelQuantParams& symmPerChannelQuantParams) {
+ VERIFY_NON_NEGATIVE(symmPerChannelQuantParams.channelDim)
+ << "Per-channel quantization channel dimension must not be negative.";
+ return Operand::SymmPerChannelQuantParams{
+ .scales = symmPerChannelQuantParams.scales,
+ .channelDim = static_cast<uint32_t>(symmPerChannelQuantParams.channelDim),
+ };
+}
+
+GeneralResult<Model> unvalidatedConvert(const aidl_hal::Model& model) {
+ return Model{
+ .main = NN_TRY(unvalidatedConvert(model.main)),
+ .referenced = NN_TRY(unvalidatedConvert(model.referenced)),
+ .operandValues = NN_TRY(unvalidatedConvert(model.operandValues)),
+ .pools = NN_TRY(unvalidatedConvert(model.pools)),
+ .relaxComputationFloat32toFloat16 = model.relaxComputationFloat32toFloat16,
+ .extensionNameToPrefix = NN_TRY(unvalidatedConvert(model.extensionNameToPrefix)),
+ };
+}
+
+GeneralResult<Model::Subgraph> unvalidatedConvert(const aidl_hal::Subgraph& subgraph) {
+ return Model::Subgraph{
+ .operands = NN_TRY(unvalidatedConvert(subgraph.operands)),
+ .operations = NN_TRY(unvalidatedConvert(subgraph.operations)),
+ .inputIndexes = NN_TRY(toUnsigned(subgraph.inputIndexes)),
+ .outputIndexes = NN_TRY(toUnsigned(subgraph.outputIndexes)),
+ };
+}
+
+GeneralResult<Model::ExtensionNameAndPrefix> unvalidatedConvert(
+ const aidl_hal::ExtensionNameAndPrefix& extensionNameAndPrefix) {
+ return Model::ExtensionNameAndPrefix{
+ .name = extensionNameAndPrefix.name,
+ .prefix = extensionNameAndPrefix.prefix,
+ };
+}
+
+GeneralResult<Extension> unvalidatedConvert(const aidl_hal::Extension& extension) {
+ return Extension{
+ .name = extension.name,
+ .operandTypes = NN_TRY(unvalidatedConvert(extension.operandTypes)),
+ };
+}
+
+GeneralResult<Extension::OperandTypeInformation> unvalidatedConvert(
+ const aidl_hal::ExtensionOperandTypeInformation& operandTypeInformation) {
+ VERIFY_NON_NEGATIVE(operandTypeInformation.byteSize)
+ << "Extension operand type byte size must not be negative";
+ return Extension::OperandTypeInformation{
+ .type = operandTypeInformation.type,
+ .isTensor = operandTypeInformation.isTensor,
+ .byteSize = static_cast<uint32_t>(operandTypeInformation.byteSize),
+ };
+}
+
+GeneralResult<OutputShape> unvalidatedConvert(const aidl_hal::OutputShape& outputShape) {
+ return OutputShape{
+ .dimensions = NN_TRY(toUnsigned(outputShape.dimensions)),
+ .isSufficient = outputShape.isSufficient,
+ };
+}
+
+GeneralResult<MeasureTiming> unvalidatedConvert(bool measureTiming) {
+ return measureTiming ? MeasureTiming::YES : MeasureTiming::NO;
+}
+
+GeneralResult<Memory> unvalidatedConvert(const aidl_hal::Memory& memory) {
+ VERIFY_NON_NEGATIVE(memory.size) << "Memory size must not be negative";
+ return Memory{
+ .handle = NN_TRY(unvalidatedConvert(memory.handle)),
+ .size = static_cast<uint32_t>(memory.size),
+ .name = memory.name,
+ };
+}
+
+GeneralResult<Model::OperandValues> unvalidatedConvert(const std::vector<uint8_t>& operandValues) {
+ return Model::OperandValues(operandValues.data(), operandValues.size());
+}
+
+GeneralResult<BufferDesc> unvalidatedConvert(const aidl_hal::BufferDesc& bufferDesc) {
+ return BufferDesc{.dimensions = NN_TRY(toUnsigned(bufferDesc.dimensions))};
+}
+
+GeneralResult<BufferRole> unvalidatedConvert(const aidl_hal::BufferRole& bufferRole) {
+ VERIFY_NON_NEGATIVE(bufferRole.modelIndex) << "BufferRole: modelIndex must not be negative";
+ VERIFY_NON_NEGATIVE(bufferRole.ioIndex) << "BufferRole: ioIndex must not be negative";
+ return BufferRole{
+ .modelIndex = static_cast<uint32_t>(bufferRole.modelIndex),
+ .ioIndex = static_cast<uint32_t>(bufferRole.ioIndex),
+ .frequency = bufferRole.frequency,
+ };
+}
+
+GeneralResult<Request> unvalidatedConvert(const aidl_hal::Request& request) {
+ return Request{
+ .inputs = NN_TRY(unvalidatedConvert(request.inputs)),
+ .outputs = NN_TRY(unvalidatedConvert(request.outputs)),
+ .pools = NN_TRY(unvalidatedConvert(request.pools)),
+ };
+}
+
+GeneralResult<Request::Argument> unvalidatedConvert(const aidl_hal::RequestArgument& argument) {
+ const auto lifetime = argument.hasNoValue ? Request::Argument::LifeTime::NO_VALUE
+ : Request::Argument::LifeTime::POOL;
+ return Request::Argument{
+ .lifetime = lifetime,
+ .location = NN_TRY(unvalidatedConvert(argument.location)),
+ .dimensions = NN_TRY(toUnsigned(argument.dimensions)),
+ };
+}
+
+GeneralResult<Request::MemoryPool> unvalidatedConvert(
+ const aidl_hal::RequestMemoryPool& memoryPool) {
+ using Tag = aidl_hal::RequestMemoryPool::Tag;
+ switch (memoryPool.getTag()) {
+ case Tag::pool:
+ return unvalidatedConvert(memoryPool.get<Tag::pool>());
+ case Tag::token: {
+ const auto token = memoryPool.get<Tag::token>();
+ VERIFY_NON_NEGATIVE(token) << "Memory pool token must not be negative";
+ return static_cast<Request::MemoryDomainToken>(token);
+ }
+ }
+ return NN_ERROR() << "Invalid Request::MemoryPool tag " << underlyingType(memoryPool.getTag());
+}
+
+GeneralResult<ErrorStatus> unvalidatedConvert(const aidl_hal::ErrorStatus& status) {
+ switch (status) {
+ case aidl_hal::ErrorStatus::NONE:
+ case aidl_hal::ErrorStatus::DEVICE_UNAVAILABLE:
+ case aidl_hal::ErrorStatus::GENERAL_FAILURE:
+ case aidl_hal::ErrorStatus::OUTPUT_INSUFFICIENT_SIZE:
+ case aidl_hal::ErrorStatus::INVALID_ARGUMENT:
+ case aidl_hal::ErrorStatus::MISSED_DEADLINE_TRANSIENT:
+ case aidl_hal::ErrorStatus::MISSED_DEADLINE_PERSISTENT:
+ case aidl_hal::ErrorStatus::RESOURCE_EXHAUSTED_TRANSIENT:
+ case aidl_hal::ErrorStatus::RESOURCE_EXHAUSTED_PERSISTENT:
+ return static_cast<ErrorStatus>(status);
+ }
+ return NN_ERROR() << "Invalid ErrorStatus " << underlyingType(status);
+}
+
+GeneralResult<ExecutionPreference> unvalidatedConvert(
+ const aidl_hal::ExecutionPreference& executionPreference) {
+ return static_cast<ExecutionPreference>(executionPreference);
+}
+
+GeneralResult<SharedHandle> unvalidatedConvert(
+ const ::aidl::android::hardware::common::NativeHandle& aidlNativeHandle) {
+ std::vector<base::unique_fd> fds;
+ fds.reserve(aidlNativeHandle.fds.size());
+ for (const auto& fd : aidlNativeHandle.fds) {
+ int dupFd = dup(fd.get());
+ if (dupFd == -1) {
+ // TODO(b/120417090): is ANEURALNETWORKS_UNEXPECTED_NULL the correct error to return
+ // here?
+ return NN_ERROR() << "Failed to dup the fd";
+ }
+ fds.emplace_back(dupFd);
+ }
+
+ return std::make_shared<const Handle>(Handle{
+ .fds = std::move(fds),
+ .ints = aidlNativeHandle.ints,
+ });
+}
+
+GeneralResult<ExecutionPreference> convert(
+ const aidl_hal::ExecutionPreference& executionPreference) {
+ return validatedConvert(executionPreference);
+}
+
+GeneralResult<Memory> convert(const aidl_hal::Memory& operand) {
+ return validatedConvert(operand);
+}
+
+GeneralResult<Model> convert(const aidl_hal::Model& model) {
+ return validatedConvert(model);
+}
+
+GeneralResult<Operand> convert(const aidl_hal::Operand& operand) {
+ return unvalidatedConvert(operand);
+}
+
+GeneralResult<OperandType> convert(const aidl_hal::OperandType& operandType) {
+ return unvalidatedConvert(operandType);
+}
+
+GeneralResult<Priority> convert(const aidl_hal::Priority& priority) {
+ return validatedConvert(priority);
+}
+
+GeneralResult<Request::MemoryPool> convert(const aidl_hal::RequestMemoryPool& memoryPool) {
+ return unvalidatedConvert(memoryPool);
+}
+
+GeneralResult<Request> convert(const aidl_hal::Request& request) {
+ return validatedConvert(request);
+}
+
+GeneralResult<std::vector<Operation>> convert(const std::vector<aidl_hal::Operation>& operations) {
+ return unvalidatedConvert(operations);
+}
+
+GeneralResult<std::vector<Memory>> convert(const std::vector<aidl_hal::Memory>& memories) {
+ return validatedConvert(memories);
+}
+
+GeneralResult<std::vector<uint32_t>> toUnsigned(const std::vector<int32_t>& vec) {
+ if (!std::all_of(vec.begin(), vec.end(), [](int32_t v) { return v >= 0; })) {
+ return NN_ERROR() << "Negative value passed to conversion from signed to unsigned";
+ }
+ return std::vector<uint32_t>(vec.begin(), vec.end());
+}
+
+} // namespace android::nn
+
+namespace aidl::android::hardware::neuralnetworks::utils {
+namespace {
+
+template <typename Input>
+using UnvalidatedConvertOutput =
+ std::decay_t<decltype(unvalidatedConvert(std::declval<Input>()).value())>;
+
+template <typename Type>
+nn::GeneralResult<std::vector<UnvalidatedConvertOutput<Type>>> unvalidatedConvertVec(
+ const std::vector<Type>& arguments) {
+ std::vector<UnvalidatedConvertOutput<Type>> halObject(arguments.size());
+ for (size_t i = 0; i < arguments.size(); ++i) {
+ halObject[i] = NN_TRY(unvalidatedConvert(arguments[i]));
+ }
+ return halObject;
+}
+
+template <typename Type>
+nn::GeneralResult<UnvalidatedConvertOutput<Type>> validatedConvert(const Type& canonical) {
+ const auto maybeVersion = nn::validate(canonical);
+ if (!maybeVersion.has_value()) {
+ return nn::error() << maybeVersion.error();
+ }
+ const auto version = maybeVersion.value();
+ if (version > kVersion) {
+ return NN_ERROR() << "Insufficient version: " << version << " vs required " << kVersion;
+ }
+ return utils::unvalidatedConvert(canonical);
+}
+
+template <typename Type>
+nn::GeneralResult<std::vector<UnvalidatedConvertOutput<Type>>> validatedConvert(
+ const std::vector<Type>& arguments) {
+ std::vector<UnvalidatedConvertOutput<Type>> halObject(arguments.size());
+ for (size_t i = 0; i < arguments.size(); ++i) {
+ halObject[i] = NN_TRY(validatedConvert(arguments[i]));
+ }
+ return halObject;
+}
+
+} // namespace
+
+nn::GeneralResult<common::NativeHandle> unvalidatedConvert(const nn::SharedHandle& sharedHandle) {
+ common::NativeHandle aidlNativeHandle;
+ aidlNativeHandle.fds.reserve(sharedHandle->fds.size());
+ for (const auto& fd : sharedHandle->fds) {
+ int dupFd = dup(fd.get());
+ if (dupFd == -1) {
+ // TODO(b/120417090): is ANEURALNETWORKS_UNEXPECTED_NULL the correct error to return
+ // here?
+ return NN_ERROR() << "Failed to dup the fd";
+ }
+ aidlNativeHandle.fds.emplace_back(dupFd);
+ }
+ aidlNativeHandle.ints = sharedHandle->ints;
+ return aidlNativeHandle;
+}
+
+nn::GeneralResult<Memory> unvalidatedConvert(const nn::Memory& memory) {
+ if (memory.size > std::numeric_limits<int64_t>::max()) {
+ return NN_ERROR() << "Memory size doesn't fit into int64_t.";
+ }
+ return Memory{
+ .handle = NN_TRY(unvalidatedConvert(memory.handle)),
+ .size = static_cast<int64_t>(memory.size),
+ .name = memory.name,
+ };
+}
+
+nn::GeneralResult<ErrorStatus> unvalidatedConvert(const nn::ErrorStatus& errorStatus) {
+ switch (errorStatus) {
+ case nn::ErrorStatus::NONE:
+ case nn::ErrorStatus::DEVICE_UNAVAILABLE:
+ case nn::ErrorStatus::GENERAL_FAILURE:
+ case nn::ErrorStatus::OUTPUT_INSUFFICIENT_SIZE:
+ case nn::ErrorStatus::INVALID_ARGUMENT:
+ case nn::ErrorStatus::MISSED_DEADLINE_TRANSIENT:
+ case nn::ErrorStatus::MISSED_DEADLINE_PERSISTENT:
+ case nn::ErrorStatus::RESOURCE_EXHAUSTED_TRANSIENT:
+ case nn::ErrorStatus::RESOURCE_EXHAUSTED_PERSISTENT:
+ return static_cast<ErrorStatus>(errorStatus);
+ default:
+ return ErrorStatus::GENERAL_FAILURE;
+ }
+}
+
+nn::GeneralResult<OutputShape> unvalidatedConvert(const nn::OutputShape& outputShape) {
+ return OutputShape{.dimensions = NN_TRY(toSigned(outputShape.dimensions)),
+ .isSufficient = outputShape.isSufficient};
+}
+
+nn::GeneralResult<Memory> convert(const nn::Memory& memory) {
+ return validatedConvert(memory);
+}
+
+nn::GeneralResult<ErrorStatus> convert(const nn::ErrorStatus& errorStatus) {
+ return validatedConvert(errorStatus);
+}
+
+nn::GeneralResult<std::vector<OutputShape>> convert(
+ const std::vector<nn::OutputShape>& outputShapes) {
+ return validatedConvert(outputShapes);
+}
+
+nn::GeneralResult<std::vector<int32_t>> toSigned(const std::vector<uint32_t>& vec) {
+ if (!std::all_of(vec.begin(), vec.end(),
+ [](uint32_t v) { return v <= std::numeric_limits<int32_t>::max(); })) {
+ return NN_ERROR() << "Vector contains a value that doesn't fit into int32_t.";
+ }
+ return std::vector<int32_t>(vec.begin(), vec.end());
+}
+
+} // namespace aidl::android::hardware::neuralnetworks::utils
diff --git a/neuralnetworks/aidl/utils/src/Utils.cpp b/neuralnetworks/aidl/utils/src/Utils.cpp
new file mode 100644
index 0000000..8d00e59
--- /dev/null
+++ b/neuralnetworks/aidl/utils/src/Utils.cpp
@@ -0,0 +1,95 @@
+/*
+ * Copyright (C) 2021 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 "Utils.h"
+
+#include <nnapi/Result.h>
+
+namespace aidl::android::hardware::neuralnetworks::utils {
+namespace {
+
+using ::android::nn::GeneralResult;
+
+template <typename Type>
+nn::GeneralResult<std::vector<Type>> cloneVec(const std::vector<Type>& arguments) {
+ std::vector<Type> clonedObjects;
+ clonedObjects.reserve(arguments.size());
+ for (const auto& argument : arguments) {
+ clonedObjects.push_back(NN_TRY(clone(argument)));
+ }
+ return clonedObjects;
+}
+
+template <typename Type>
+GeneralResult<std::vector<Type>> clone(const std::vector<Type>& arguments) {
+ return cloneVec(arguments);
+}
+
+} // namespace
+
+GeneralResult<Memory> clone(const Memory& memory) {
+ common::NativeHandle nativeHandle;
+ nativeHandle.ints = memory.handle.ints;
+ nativeHandle.fds.reserve(memory.handle.fds.size());
+ for (const auto& fd : memory.handle.fds) {
+ const int newFd = dup(fd.get());
+ if (newFd < 0) {
+ return NN_ERROR() << "Couldn't dup a file descriptor";
+ }
+ nativeHandle.fds.emplace_back(newFd);
+ }
+ return Memory{
+ .handle = std::move(nativeHandle),
+ .size = memory.size,
+ .name = memory.name,
+ };
+}
+
+GeneralResult<RequestMemoryPool> clone(const RequestMemoryPool& requestPool) {
+ using Tag = RequestMemoryPool::Tag;
+ switch (requestPool.getTag()) {
+ case Tag::pool:
+ return RequestMemoryPool::make<Tag::pool>(NN_TRY(clone(requestPool.get<Tag::pool>())));
+ case Tag::token:
+ return RequestMemoryPool::make<Tag::token>(requestPool.get<Tag::token>());
+ }
+ // Using explicit type conversion because std::variant inside the RequestMemoryPool confuses the
+ // compiler.
+ return (NN_ERROR() << "Unrecognized request pool tag: " << requestPool.getTag())
+ .
+ operator GeneralResult<RequestMemoryPool>();
+}
+
+GeneralResult<Request> clone(const Request& request) {
+ return Request{
+ .inputs = request.inputs,
+ .outputs = request.outputs,
+ .pools = NN_TRY(clone(request.pools)),
+ };
+}
+
+GeneralResult<Model> clone(const Model& model) {
+ return Model{
+ .main = model.main,
+ .referenced = model.referenced,
+ .operandValues = model.operandValues,
+ .pools = NN_TRY(clone(model.pools)),
+ .relaxComputationFloat32toFloat16 = model.relaxComputationFloat32toFloat16,
+ .extensionNameToPrefix = model.extensionNameToPrefix,
+ };
+}
+
+} // namespace aidl::android::hardware::neuralnetworks::utils
diff --git a/neuralnetworks/aidl/vts/OWNERS b/neuralnetworks/aidl/vts/OWNERS
new file mode 100644
index 0000000..6719a5b
--- /dev/null
+++ b/neuralnetworks/aidl/vts/OWNERS
@@ -0,0 +1,12 @@
+# 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
diff --git a/neuralnetworks/aidl/vts/functional/Android.bp b/neuralnetworks/aidl/vts/functional/Android.bp
new file mode 100644
index 0000000..aa7afbf
--- /dev/null
+++ b/neuralnetworks/aidl/vts/functional/Android.bp
@@ -0,0 +1,68 @@
+//
+// Copyright (C) 2021 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: "VtsHalNeuralnetworksTargetTest",
+ defaults: [
+ "neuralnetworks_vts_functional_defaults",
+ "use_libaidlvintf_gtest_helper_static",
+ ],
+ srcs: [
+ "BasicTests.cpp",
+ "Callbacks.cpp",
+ "CompilationCachingTests.cpp",
+ "GeneratedTestHarness.cpp",
+ "MemoryDomainTests.cpp",
+ "QualityOfServiceTests.cpp",
+ "TestAssertions.cpp",
+ "TestMain.cpp",
+ "Utils.cpp",
+ "ValidateModel.cpp",
+ "ValidateRequest.cpp",
+ "VtsHalNeuralnetworks.cpp",
+ ],
+ shared_libs: [
+ "libbinder_ndk",
+ "libnativewindow",
+ "libvndksupport",
+ ],
+ static_libs: [
+ "android.hardware.common-V2-ndk_platform",
+ "android.hardware.neuralnetworks-V1-ndk_platform",
+ "android.hidl.allocator@1.0",
+ "android.hidl.memory@1.0",
+ "libgmock",
+ "libhidlmemory",
+ "libneuralnetworks_generated_test_harness",
+ "libneuralnetworks_utils",
+ "libsync",
+ "neuralnetworks_utils_hal_aidl",
+ ],
+ 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: [
+ "libbase_headers",
+ "libneuralnetworks_headers",
+ ],
+ test_suites: [
+ "general-tests",
+ "vts",
+ ],
+}
diff --git a/neuralnetworks/aidl/vts/functional/AndroidTest.xml b/neuralnetworks/aidl/vts/functional/AndroidTest.xml
new file mode 100644
index 0000000..384d420
--- /dev/null
+++ b/neuralnetworks/aidl/vts/functional/AndroidTest.xml
@@ -0,0 +1,33 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- Copyright (C) 2020 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.
+-->
+<configuration description="Runs VtsHalNeuralnetworksTargetTest.">
+ <option name="test-suite-tag" value="apct" />
+ <option name="test-suite-tag" value="apct-native" />
+
+ <target_preparer class="com.android.tradefed.targetprep.RootTargetPreparer">
+ </target_preparer>
+
+ <target_preparer class="com.android.tradefed.targetprep.PushFilePreparer">
+ <option name="cleanup" value="true" />
+ <option name="push" value="VtsHalNeuralnetworksTargetTest->/data/local/tmp/VtsHalNeuralnetworksTargetTest" />
+ </target_preparer>
+
+ <test class="com.android.tradefed.testtype.GTest" >
+ <option name="native-test-device-path" value="/data/local/tmp" />
+ <option name="module-name" value="VtsHalNeuralnetworksTargetTest" />
+ <option name="native-test-timeout" value="20m" />
+ </test>
+</configuration>
diff --git a/neuralnetworks/aidl/vts/functional/BasicTests.cpp b/neuralnetworks/aidl/vts/functional/BasicTests.cpp
new file mode 100644
index 0000000..b2f4507
--- /dev/null
+++ b/neuralnetworks/aidl/vts/functional/BasicTests.cpp
@@ -0,0 +1,193 @@
+/*
+ * Copyright (C) 2021 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_aidl_hal_test"
+
+#include <aidl/android/hardware/neuralnetworks/Capabilities.h>
+#include <aidl/android/hardware/neuralnetworks/IDevice.h>
+#include <aidl/android/hardware/neuralnetworks/Operand.h>
+#include <aidl/android/hardware/neuralnetworks/OperandType.h>
+#include <aidl/android/hardware/neuralnetworks/Priority.h>
+#include <android/binder_interface_utils.h>
+
+#include "Utils.h"
+#include "VtsHalNeuralnetworks.h"
+
+namespace aidl::android::hardware::neuralnetworks::vts::functional {
+
+using implementation::PreparedModelCallback;
+
+// create device test
+TEST_P(NeuralNetworksAidlTest, CreateDevice) {}
+
+// initialization
+TEST_P(NeuralNetworksAidlTest, GetCapabilitiesTest) {
+ Capabilities capabilities;
+ const auto retStatus = kDevice->getCapabilities(&capabilities);
+ ASSERT_TRUE(retStatus.isOk());
+
+ 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(std::all_of(opPerf.begin(), opPerf.end(), [](const OperandPerformance& a) {
+ return a.type != OperandType::SUBGRAPH;
+ }));
+ EXPECT_TRUE(isPositive(capabilities.ifPerformance));
+ EXPECT_TRUE(isPositive(capabilities.whilePerformance));
+}
+
+// detect cycle
+TEST_P(NeuralNetworksAidlTest, CycleTest) {
+ // opnd0 = TENSOR_FLOAT32 // model input
+ // opnd1 = TENSOR_FLOAT32 // model input
+ // opnd2 = INT32 // model input
+ // opnd3 = ADD(opnd0, opnd4, opnd2)
+ // opnd4 = ADD(opnd1, opnd3, opnd2)
+ // opnd5 = ADD(opnd4, opnd0, opnd2) // model output
+ //
+ // +-----+
+ // | |
+ // v |
+ // 3 = ADD(0, 4, 2) |
+ // | |
+ // +----------+ |
+ // | |
+ // v |
+ // 4 = ADD(1, 3, 2) |
+ // | |
+ // +----------------+
+ // |
+ // |
+ // +-------+
+ // |
+ // v
+ // 5 = ADD(4, 0, 2)
+
+ const std::vector<Operand> operands = {
+ {
+ // operands[0]
+ .type = OperandType::TENSOR_FLOAT32,
+ .dimensions = {1},
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::SUBGRAPH_INPUT,
+ .location = {.poolIndex = 0, .offset = 0, .length = 0},
+ },
+ {
+ // operands[1]
+ .type = OperandType::TENSOR_FLOAT32,
+ .dimensions = {1},
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::SUBGRAPH_INPUT,
+ .location = {.poolIndex = 0, .offset = 0, .length = 0},
+ },
+ {
+ // operands[2]
+ .type = OperandType::INT32,
+ .dimensions = {},
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::SUBGRAPH_INPUT,
+ .location = {.poolIndex = 0, .offset = 0, .length = 0},
+ },
+ {
+ // operands[3]
+ .type = OperandType::TENSOR_FLOAT32,
+ .dimensions = {1},
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
+ .location = {.poolIndex = 0, .offset = 0, .length = 0},
+ },
+ {
+ // operands[4]
+ .type = OperandType::TENSOR_FLOAT32,
+ .dimensions = {1},
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
+ .location = {.poolIndex = 0, .offset = 0, .length = 0},
+ },
+ {
+ // operands[5]
+ .type = OperandType::TENSOR_FLOAT32,
+ .dimensions = {1},
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::SUBGRAPH_OUTPUT,
+ .location = {.poolIndex = 0, .offset = 0, .length = 0},
+ },
+ };
+
+ const std::vector<Operation> operations = {
+ {.type = OperationType::ADD, .inputs = {0, 4, 2}, .outputs = {3}},
+ {.type = OperationType::ADD, .inputs = {1, 3, 2}, .outputs = {4}},
+ {.type = OperationType::ADD, .inputs = {4, 0, 2}, .outputs = {5}},
+ };
+
+ Subgraph subgraph = {
+ .operands = operands,
+ .operations = operations,
+ .inputIndexes = {0, 1, 2},
+ .outputIndexes = {5},
+ };
+ const Model model = {
+ .main = std::move(subgraph),
+ .referenced = {},
+ .operandValues = {},
+ .pools = {},
+ };
+
+ // ensure that getSupportedOperations() checks model validity
+ std::vector<bool> supportedOps;
+ const auto supportedOpsStatus = kDevice->getSupportedOperations(model, &supportedOps);
+ ASSERT_FALSE(supportedOpsStatus.isOk());
+ ASSERT_EQ(supportedOpsStatus.getExceptionCode(), EX_SERVICE_SPECIFIC);
+ ASSERT_EQ(static_cast<ErrorStatus>(supportedOpsStatus.getServiceSpecificError()),
+ ErrorStatus::INVALID_ARGUMENT);
+
+ // ensure that prepareModel() checks model validity
+ auto preparedModelCallback = ndk::SharedRefBase::make<PreparedModelCallback>();
+ auto prepareLaunchStatus =
+ kDevice->prepareModel(model, ExecutionPreference::FAST_SINGLE_ANSWER, kDefaultPriority,
+ kNoDeadline, {}, {}, kEmptyCacheToken, preparedModelCallback);
+ // Note that preparation can fail for reasons other than an
+ // invalid model (invalid model should result in
+ // INVALID_ARGUMENT) -- for example, perhaps not all
+ // operations are supported, or perhaps the device hit some
+ // kind of capacity limit.
+ ASSERT_FALSE(prepareLaunchStatus.isOk());
+ EXPECT_EQ(prepareLaunchStatus.getExceptionCode(), EX_SERVICE_SPECIFIC);
+ EXPECT_NE(static_cast<ErrorStatus>(prepareLaunchStatus.getServiceSpecificError()),
+ ErrorStatus::NONE);
+
+ EXPECT_NE(preparedModelCallback->getStatus(), ErrorStatus::NONE);
+ EXPECT_EQ(preparedModelCallback->getPreparedModel(), nullptr);
+}
+
+} // namespace aidl::android::hardware::neuralnetworks::vts::functional
diff --git a/neuralnetworks/aidl/vts/functional/Callbacks.cpp b/neuralnetworks/aidl/vts/functional/Callbacks.cpp
new file mode 100644
index 0000000..ca2bb48
--- /dev/null
+++ b/neuralnetworks/aidl/vts/functional/Callbacks.cpp
@@ -0,0 +1,59 @@
+/*
+ * Copyright (C) 2021 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 "Callbacks"
+
+#include "Callbacks.h"
+
+#include <android-base/logging.h>
+#include <android/binder_auto_utils.h>
+#include <limits>
+
+namespace aidl::android::hardware::neuralnetworks::implementation {
+
+ndk::ScopedAStatus PreparedModelCallback::notify(
+ ErrorStatus errorStatus, const std::shared_ptr<IPreparedModel>& preparedModel) {
+ {
+ std::lock_guard<std::mutex> hold(mMutex);
+ // quick-return if object has already been notified
+ if (mNotified) {
+ return ndk::ScopedAStatus::ok();
+ }
+ // store results and mark as notified
+ mErrorStatus = errorStatus;
+ mPreparedModel = preparedModel;
+ mNotified = true;
+ }
+ mCondition.notify_all();
+ return ndk::ScopedAStatus::ok();
+}
+
+void PreparedModelCallback::wait() const {
+ std::unique_lock<std::mutex> lock(mMutex);
+ mCondition.wait(lock, [this] { return mNotified; });
+}
+
+ErrorStatus PreparedModelCallback::getStatus() const {
+ wait();
+ return mErrorStatus;
+}
+
+std::shared_ptr<IPreparedModel> PreparedModelCallback::getPreparedModel() const {
+ wait();
+ return mPreparedModel;
+}
+
+} // namespace aidl::android::hardware::neuralnetworks::implementation
diff --git a/neuralnetworks/aidl/vts/functional/Callbacks.h b/neuralnetworks/aidl/vts/functional/Callbacks.h
new file mode 100644
index 0000000..0eb4d5f
--- /dev/null
+++ b/neuralnetworks/aidl/vts/functional/Callbacks.h
@@ -0,0 +1,131 @@
+/*
+ * Copyright (C) 2021 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_AIDL_CALLBACKS_H
+#define ANDROID_HARDWARE_NEURALNETWORKS_AIDL_CALLBACKS_H
+
+#include <android-base/thread_annotations.h>
+#include <condition_variable>
+#include <mutex>
+
+#include <aidl/android/hardware/neuralnetworks/BnPreparedModelCallback.h>
+#include <aidl/android/hardware/neuralnetworks/ErrorStatus.h>
+#include <aidl/android/hardware/neuralnetworks/IPreparedModel.h>
+
+/*
+ * The Callback classes are used internally by the NeuralNetworks runtime to
+ * synchronize between different threads. An asynchronous task is launched
+ * paired with a callback object. When a client thread requires the output being
+ * generated by the asynchronous task, the client thread can wait for the result
+ * and be blocked until it has completed. Any wait may safely be called
+ * concurrently, even on the same callback object. When the asynchronous task
+ * has finished its workload, it must immediately call "notify". If the
+ * asynchronous task has failed to launch, the function that tried to launch the
+ * asynchronous task must immediately call "notify". This "notify" call
+ * awakens any client threads waiting on the callback object.
+ *
+ * These classes exist to enable synchronization across AIDL. When
+ * synchronization is only required in the same process, consider using
+ * std::future, std::mutex, std::condition_variable, or std::experimental::latch
+ * instead.
+ */
+
+namespace aidl::android::hardware::neuralnetworks::implementation {
+
+/**
+ * The PreparedModelCallback class is used to receive the error status of
+ * preparing a model as well as the prepared model from a task executing
+ * asynchronously with respect to the runtime. If a calling thread calls wait
+ * or get* on a PreparedModelCallback object and the corresponding asynchronous
+ * task has not finished preparing the model, the calling thread will block
+ * until the asynchronous task has called notify.
+ *
+ * If the callback object is notified more than once, only the results of the
+ * first call to notify are used, and the results from subsequent calls are
+ * discarded.
+ *
+ * This callback object is passed as an argument to IDevice::prepareModel*.
+ */
+class PreparedModelCallback : public BnPreparedModelCallback {
+ public:
+ /**
+ * IPreparedModelCallback::notify marks the callback object with the return
+ * status of the asynchronous model preparation along with the prepared
+ * model, and allows all prior and future wait calls on the
+ * PreparedModelCallback object to proceed.
+ *
+ * IPreparedModelCallback::notify must be called on a given PreparedModelCallback object.
+ *
+ * If the callback object is notified more than once, only the results of
+ * the first call to notify are used, and the results from subsequent calls
+ * are discarded.
+ *
+ * @param status Error status returned from asynchronously preparing the
+ * model; will be:
+ * - NONE if the asynchronous preparation was successful
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ * - INVALID_ARGUMENT if the input model is invalid
+ * @param preparedModel Returned model that has been prepared for execution,
+ * nullptr if the model was unable to be prepared.
+ */
+ ndk::ScopedAStatus notify(ErrorStatus status,
+ const std::shared_ptr<IPreparedModel>& preparedModel) override;
+
+ /**
+ * PreparedModelCallback::wait blocks until notify has been called on the
+ * callback object.
+ */
+ void wait() const;
+
+ /**
+ * Retrieves the error status returned from the asynchronous task launched
+ * by IDevice::prepareModel*. If IDevice::prepareModel* has not finished
+ * asynchronously preparing the model, this call will block until the
+ * asynchronous task notifies the object.
+ *
+ * @return status Error status returned from asynchronously preparing the
+ * model; will be:
+ * - NONE if the asynchronous preparation was successful
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ * - INVALID_ARGUMENT if the input model is invalid
+ */
+ ErrorStatus getStatus() const;
+
+ /**
+ * Retrieves the model that has been prepared for execution from the
+ * asynchronous task launched by IDevice::prepareModel*. If
+ * IDevice::prepareModel* has not finished asynchronously preparing the
+ * model, this call will block until the asynchronous task notifies the
+ * object.
+ *
+ * @return preparedModel Returned model that has been prepared for
+ * execution, nullptr if the model was unable to be prepared.
+ */
+ std::shared_ptr<IPreparedModel> getPreparedModel() const;
+
+ private:
+ mutable std::mutex mMutex;
+ mutable std::condition_variable mCondition;
+ bool mNotified GUARDED_BY(mMutex) = false;
+ ErrorStatus mErrorStatus = ErrorStatus::GENERAL_FAILURE;
+ std::shared_ptr<IPreparedModel> mPreparedModel;
+};
+
+} // namespace aidl::android::hardware::neuralnetworks::implementation
+
+#endif // ANDROID_HARDWARE_NEURALNETWORKS_AIDL_CALLBACKS_H
diff --git a/neuralnetworks/aidl/vts/functional/CompilationCachingTests.cpp b/neuralnetworks/aidl/vts/functional/CompilationCachingTests.cpp
new file mode 100644
index 0000000..e0b529f
--- /dev/null
+++ b/neuralnetworks/aidl/vts/functional/CompilationCachingTests.cpp
@@ -0,0 +1,1177 @@
+/*
+ * Copyright (C) 2021 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_aidl_hal_test"
+
+#include <android-base/logging.h>
+#include <android/binder_auto_utils.h>
+#include <android/binder_interface_utils.h>
+#include <android/binder_status.h>
+#include <fcntl.h>
+#include <ftw.h>
+#include <gtest/gtest.h>
+#include <hidlmemory/mapping.h>
+#include <unistd.h>
+
+#include <cstdio>
+#include <cstdlib>
+#include <iterator>
+#include <random>
+#include <thread>
+
+#include "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 aidl::android::hardware::neuralnetworks::vts::functional {
+
+using namespace test_helper;
+using 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 createCacheFds(const std::vector<std::string>& files, const std::vector<AccessMode>& mode,
+ std::vector<ndk::ScopedFileDescriptor>* fds) {
+ fds->clear();
+ fds->reserve(files.size());
+ for (uint32_t i = 0; i < files.size(); i++) {
+ const auto& file = files[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->emplace_back(fd);
+ }
+}
+
+void createCacheFds(const std::vector<std::string>& files, AccessMode mode,
+ std::vector<ndk::ScopedFileDescriptor>* fds) {
+ createCacheFds(files, std::vector<AccessMode>(files.size(), mode), fds);
+}
+
+// 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 {
+ .main = {.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(std::shared_ptr<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('/');
+
+ NumberOfCacheFiles numCacheFiles;
+ const auto ret = kDevice->getNumberOfCacheFilesNeeded(&numCacheFiles);
+ ASSERT_TRUE(ret.isOk());
+
+ mNumModelCache = numCacheFiles.numModelCache;
+ mNumDataCache = numCacheFiles.numDataCache;
+ ASSERT_GE(mNumModelCache, 0) << "Invalid numModelCache: " << mNumModelCache;
+ ASSERT_GE(mNumDataCache, 0) << "Invalid numDataCache: " << mNumDataCache;
+ 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)});
+ }
+ // Placeholder handles, use AccessMode::WRITE_ONLY for createCacheFds to create files.
+ std::vector<ndk::ScopedFileDescriptor> modelHandle, dataHandle, tmpHandle;
+ createCacheFds(mModelCache, AccessMode::WRITE_ONLY, &modelHandle);
+ createCacheFds(mDataCache, AccessMode::WRITE_ONLY, &dataHandle);
+ createCacheFds({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) {
+ std::vector<bool> supportedOps;
+ const auto supportedCall = kDevice->getSupportedOperations(model, &supportedOps);
+ EXPECT_TRUE(supportedCall.isOk());
+ EXPECT_EQ(supportedOps.size(), model.main.operations.size());
+ if (!supportedCall.isOk() || supportedOps.size() != model.main.operations.size()) {
+ return false;
+ }
+ return std::all_of(supportedOps.begin(), supportedOps.end(),
+ [](bool valid) { return valid; });
+ }
+
+ void saveModelToCache(const Model& model,
+ const std::vector<ndk::ScopedFileDescriptor>& modelCache,
+ const std::vector<ndk::ScopedFileDescriptor>& dataCache,
+ std::shared_ptr<IPreparedModel>* preparedModel = nullptr) {
+ if (preparedModel != nullptr) *preparedModel = nullptr;
+
+ // Launch prepare model.
+ std::shared_ptr<PreparedModelCallback> preparedModelCallback =
+ ndk::SharedRefBase::make<PreparedModelCallback>();
+ std::vector<uint8_t> cacheToken(std::begin(mToken), std::end(mToken));
+ const auto prepareLaunchStatus = kDevice->prepareModel(
+ model, ExecutionPreference::FAST_SINGLE_ANSWER, kDefaultPriority, kNoDeadline,
+ modelCache, dataCache, cacheToken, preparedModelCallback);
+ ASSERT_TRUE(prepareLaunchStatus.isOk());
+
+ // Retrieve prepared model.
+ preparedModelCallback->wait();
+ ASSERT_EQ(preparedModelCallback->getStatus(), ErrorStatus::NONE);
+ if (preparedModel != nullptr) {
+ *preparedModel = preparedModelCallback->getPreparedModel();
+ }
+ }
+
+ 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 std::vector<ndk::ScopedFileDescriptor>& modelCache,
+ const std::vector<ndk::ScopedFileDescriptor>& dataCache,
+ std::shared_ptr<IPreparedModel>* preparedModel,
+ ErrorStatus* status) {
+ // Launch prepare model from cache.
+ std::shared_ptr<PreparedModelCallback> preparedModelCallback =
+ ndk::SharedRefBase::make<PreparedModelCallback>();
+ std::vector<uint8_t> cacheToken(std::begin(mToken), std::end(mToken));
+ const auto prepareLaunchStatus = kDevice->prepareModelFromCache(
+ kNoDeadline, modelCache, dataCache, cacheToken, preparedModelCallback);
+ ASSERT_TRUE(prepareLaunchStatus.isOk() ||
+ prepareLaunchStatus.getExceptionCode() == EX_SERVICE_SPECIFIC)
+ << "prepareLaunchStatus: " << prepareLaunchStatus.getDescription();
+ if (!prepareLaunchStatus.isOk()) {
+ *preparedModel = nullptr;
+ *status = static_cast<ErrorStatus>(prepareLaunchStatus.getServiceSpecificError());
+ return;
+ }
+
+ // Retrieve prepared model.
+ preparedModelCallback->wait();
+ *status = preparedModelCallback->getStatus();
+ *preparedModel = preparedModelCallback->getPreparedModel();
+ }
+
+ // 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
+ // size = mNum{Model|Data}Cache. The outer vector corresponds to handles and the inner vector is
+ // for fds held by each handle.
+ std::vector<std::string> mModelCache;
+ 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>(IDevice::BYTE_SIZE_OF_CACHE_TOKEN)] = {};
+ uint32_t mNumModelCache;
+ uint32_t mNumDataCache;
+ uint32_t mIsCachingSupported;
+
+ const std::shared_ptr<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;
+ std::shared_ptr<IPreparedModel> preparedModel = nullptr;
+
+ // Save the compilation to cache.
+ {
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ saveModelToCache(model, modelCache, dataCache);
+ }
+
+ // Retrieve preparedModel from cache.
+ {
+ preparedModel = nullptr;
+ ErrorStatus status;
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(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(kDevice, preparedModel, testModel, /*testKind=*/TestKind::GENERAL);
+}
+
+TEST_P(CompilationCachingTest, CacheSavingAndRetrievalNonZeroOffset) {
+ // Create test HIDL model and compile.
+ const TestModel& testModel = createTestModel();
+ const Model model = createModel(testModel);
+ if (checkEarlyTermination(model)) return;
+ std::shared_ptr<IPreparedModel> preparedModel = nullptr;
+
+ // Save the compilation to cache.
+ {
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ uint8_t placeholderBytes[] = {0, 0};
+ // Write a placeholder 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].get(), &placeholderBytes, sizeof(placeholderBytes)),
+ sizeof(placeholderBytes));
+ }
+ for (uint32_t i = 0; i < dataCache.size(); i++) {
+ ASSERT_EQ(write(dataCache[i].get(), &placeholderBytes, sizeof(placeholderBytes)),
+ sizeof(placeholderBytes));
+ }
+ saveModelToCache(model, modelCache, dataCache);
+ }
+
+ // Retrieve preparedModel from cache.
+ {
+ preparedModel = nullptr;
+ ErrorStatus status;
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ uint8_t placeholderByte = 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].get(), &placeholderByte, 1), 0);
+ }
+ for (uint32_t i = 0; i < dataCache.size(); i++) {
+ ASSERT_GE(read(dataCache[i].get(), &placeholderByte, 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(kDevice, preparedModel, testModel, /*testKind=*/TestKind::GENERAL);
+}
+
+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.
+ {
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ // Pass an additional cache file for model cache.
+ mModelCache.push_back({mTmpCache});
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ mModelCache.pop_back();
+ std::shared_ptr<IPreparedModel> preparedModel = nullptr;
+ saveModelToCache(model, modelCache, dataCache, &preparedModel);
+ ASSERT_NE(preparedModel, nullptr);
+ // Execute and verify results.
+ EvaluatePreparedModel(kDevice, preparedModel, testModel, /*testKind=*/TestKind::GENERAL);
+ // 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) {
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ // Pop out the last cache file.
+ auto tmp = mModelCache.back();
+ mModelCache.pop_back();
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ mModelCache.push_back(tmp);
+ std::shared_ptr<IPreparedModel> preparedModel = nullptr;
+ saveModelToCache(model, modelCache, dataCache, &preparedModel);
+ ASSERT_NE(preparedModel, nullptr);
+ // Execute and verify results.
+ EvaluatePreparedModel(kDevice, preparedModel, testModel, /*testKind=*/TestKind::GENERAL);
+ // 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.
+ {
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ // Pass an additional cache file for data cache.
+ mDataCache.push_back({mTmpCache});
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ mDataCache.pop_back();
+ std::shared_ptr<IPreparedModel> preparedModel = nullptr;
+ saveModelToCache(model, modelCache, dataCache, &preparedModel);
+ ASSERT_NE(preparedModel, nullptr);
+ // Execute and verify results.
+ EvaluatePreparedModel(kDevice, preparedModel, testModel, /*testKind=*/TestKind::GENERAL);
+ // 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) {
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ // Pop out the last cache file.
+ auto tmp = mDataCache.back();
+ mDataCache.pop_back();
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ mDataCache.push_back(tmp);
+ std::shared_ptr<IPreparedModel> preparedModel = nullptr;
+ saveModelToCache(model, modelCache, dataCache, &preparedModel);
+ ASSERT_NE(preparedModel, nullptr);
+ // Execute and verify results.
+ EvaluatePreparedModel(kDevice, preparedModel, testModel, /*testKind=*/TestKind::GENERAL);
+ // 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.
+ {
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ saveModelToCache(model, modelCache, dataCache);
+ }
+
+ // Test with number of model cache files greater than mNumModelCache.
+ {
+ std::shared_ptr<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ mModelCache.push_back({mTmpCache});
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(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) {
+ std::shared_ptr<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ auto tmp = mModelCache.back();
+ mModelCache.pop_back();
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(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.
+ {
+ std::shared_ptr<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ mDataCache.push_back({mTmpCache});
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(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) {
+ std::shared_ptr<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ auto tmp = mDataCache.back();
+ mDataCache.pop_back();
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(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, 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++) {
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ modelCacheMode[i] = AccessMode::READ_ONLY;
+ createCacheFds(mModelCache, modelCacheMode, &modelCache);
+ createCacheFds(mDataCache, dataCacheMode, &dataCache);
+ modelCacheMode[i] = AccessMode::READ_WRITE;
+ std::shared_ptr<IPreparedModel> preparedModel = nullptr;
+ saveModelToCache(model, modelCache, dataCache, &preparedModel);
+ ASSERT_NE(preparedModel, nullptr);
+ // Execute and verify results.
+ EvaluatePreparedModel(kDevice, preparedModel, testModel, /*testKind=*/TestKind::GENERAL);
+ // 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++) {
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ dataCacheMode[i] = AccessMode::READ_ONLY;
+ createCacheFds(mModelCache, modelCacheMode, &modelCache);
+ createCacheFds(mDataCache, dataCacheMode, &dataCache);
+ dataCacheMode[i] = AccessMode::READ_WRITE;
+ std::shared_ptr<IPreparedModel> preparedModel = nullptr;
+ saveModelToCache(model, modelCache, dataCache, &preparedModel);
+ ASSERT_NE(preparedModel, nullptr);
+ // Execute and verify results.
+ EvaluatePreparedModel(kDevice, preparedModel, testModel, /*testKind=*/TestKind::GENERAL);
+ // 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.
+ {
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(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++) {
+ std::shared_ptr<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ modelCacheMode[i] = AccessMode::WRITE_ONLY;
+ createCacheFds(mModelCache, modelCacheMode, &modelCache);
+ createCacheFds(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++) {
+ std::shared_ptr<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ dataCacheMode[i] = AccessMode::WRITE_ONLY;
+ createCacheFds(mModelCache, modelCacheMode, &modelCache);
+ createCacheFds(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 files.
+// The vector sizes must match.
+static void copyCacheFiles(const std::vector<std::string>& from,
+ const 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++) {
+ int fromFd = open(from[i].c_str(), O_RDONLY);
+ int toFd = open(to[i].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.append("_mul");
+ }
+ {
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ createCacheFds(modelCacheMul, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(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.
+ {
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(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.
+ {
+ std::shared_ptr<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(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(kDevice, preparedModel, testModelAdd,
+ /*testKind=*/TestKind::GENERAL);
+ }
+ }
+ }
+}
+
+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.append("_mul");
+ }
+ {
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ createCacheFds(modelCacheMul, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(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.
+ {
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ saveModelToCache(modelAdd, modelCache, dataCache);
+ }
+
+ // Retrieve preparedModel from cache.
+ {
+ std::shared_ptr<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(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(kDevice, preparedModel, testModelAdd,
+ /*testKind=*/TestKind::GENERAL);
+ }
+ }
+ }
+}
+
+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.append("_mul");
+ }
+ {
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ createCacheFds(modelCacheMul, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ saveModelToCache(modelMul, modelCache, dataCache);
+ }
+
+ // Use a different token for modelAdd.
+ mToken[0]++;
+
+ // Save the modelAdd compilation to cache.
+ {
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(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.
+ {
+ std::shared_ptr<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+ ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+ ASSERT_EQ(preparedModel, nullptr);
+ }
+}
+
+// TODO(b/179270601): restore kNamedDeviceChoices.
+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);
+}
+
+GTEST_ALLOW_UNINSTANTIATED_PARAMETERIZED_TEST(CompilationCachingTest);
+INSTANTIATE_TEST_SUITE_P(TestCompilationCaching, CompilationCachingTest,
+ testing::Combine(testing::ValuesIn(getNamedDevices()),
+ 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.
+ {
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(mDataCache, AccessMode::READ_WRITE, &dataCache);
+ saveModelToCache(model, modelCache, dataCache);
+ }
+
+ bool skip = false;
+ modifier(&skip);
+ if (skip) return;
+
+ // Retrieve preparedModel from cache.
+ {
+ std::shared_ptr<IPreparedModel> preparedModel = nullptr;
+ ErrorStatus status;
+ std::vector<ndk::ScopedFileDescriptor> modelCache, dataCache;
+ createCacheFds(mModelCache, AccessMode::READ_WRITE, &modelCache);
+ createCacheFds(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], 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], 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], 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], 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>(IDevice::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));
+}
+
+GTEST_ALLOW_UNINSTANTIATED_PARAMETERIZED_TEST(CompilationCachingSecurityTest);
+INSTANTIATE_TEST_SUITE_P(TestCompilationCaching, CompilationCachingSecurityTest,
+ testing::Combine(testing::ValuesIn(getNamedDevices()), kOperandTypeChoices,
+ testing::Range(0U, 10U)),
+ printCompilationCachingSecurityTest);
+
+} // namespace aidl::android::hardware::neuralnetworks::vts::functional
diff --git a/neuralnetworks/aidl/vts/functional/GeneratedTestHarness.cpp b/neuralnetworks/aidl/vts/functional/GeneratedTestHarness.cpp
new file mode 100644
index 0000000..86d5f3f
--- /dev/null
+++ b/neuralnetworks/aidl/vts/functional/GeneratedTestHarness.cpp
@@ -0,0 +1,925 @@
+/*
+ * Copyright (C) 2021 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 <aidl/android/hardware/neuralnetworks/ErrorStatus.h>
+#include <android-base/logging.h>
+#include <android/binder_auto_utils.h>
+#include <android/sync.h>
+#include <gtest/gtest.h>
+
+#include <algorithm>
+#include <chrono>
+#include <iostream>
+#include <iterator>
+#include <numeric>
+#include <vector>
+
+#include <MemoryUtils.h>
+#include <android/binder_status.h>
+#include <nnapi/Result.h>
+#include <nnapi/SharedMemory.h>
+#include <nnapi/Types.h>
+#include <nnapi/hal/aidl/Conversions.h>
+#include <nnapi/hal/aidl/Utils.h>
+
+#include "Callbacks.h"
+#include "TestHarness.h"
+#include "Utils.h"
+#include "VtsHalNeuralnetworks.h"
+
+namespace aidl::android::hardware::neuralnetworks::vts::functional {
+
+namespace nn = ::android::nn;
+using namespace test_helper;
+using implementation::PreparedModelCallback;
+
+namespace {
+
+enum class OutputType { FULLY_SPECIFIED, UNSPECIFIED, INSUFFICIENT, MISSED_DEADLINE };
+
+struct TestConfig {
+ Executor executor;
+ bool measureTiming;
+ OutputType outputType;
+ MemoryType memoryType;
+ // `reportSkipping` indicates if a test should print an info message in case
+ // it is skipped. The field is set to true by default and is set to false in
+ // quantization coupling tests to suppress skipping a test
+ bool reportSkipping;
+ TestConfig(Executor executor, bool measureTiming, OutputType outputType, MemoryType memoryType)
+ : executor(executor),
+ measureTiming(measureTiming),
+ outputType(outputType),
+ memoryType(memoryType),
+ reportSkipping(true) {}
+ TestConfig(Executor executor, bool measureTiming, OutputType outputType, MemoryType memoryType,
+ bool reportSkipping)
+ : executor(executor),
+ measureTiming(measureTiming),
+ outputType(outputType),
+ memoryType(memoryType),
+ reportSkipping(reportSkipping) {}
+};
+
+enum class IOType { INPUT, OUTPUT };
+
+class DeviceMemoryAllocator {
+ public:
+ DeviceMemoryAllocator(const std::shared_ptr<IDevice>& device,
+ const std::shared_ptr<IPreparedModel>& preparedModel,
+ const TestModel& testModel)
+ : kDevice(device), kPreparedModel(preparedModel), kTestModel(testModel) {}
+
+ // Allocate device memory for a target input/output operand.
+ // Return {IBuffer object, token} if successful.
+ // Return {nullptr, 0} if device memory is not supported.
+ template <IOType ioType>
+ std::pair<std::shared_ptr<IBuffer>, int32_t> allocate(uint32_t index) {
+ std::pair<std::shared_ptr<IBuffer>, int32_t> buffer;
+ allocateInternal<ioType>(index, &buffer);
+ return buffer;
+ }
+
+ private:
+ template <IOType ioType>
+ void allocateInternal(int32_t index, std::pair<std::shared_ptr<IBuffer>, int32_t>* result) {
+ ASSERT_NE(result, nullptr);
+
+ // Prepare arguments.
+ BufferRole role = {.modelIndex = 0, .ioIndex = index, .frequency = 1.0f};
+ std::vector<BufferRole> inputRoles, outputRoles;
+ if constexpr (ioType == IOType::INPUT) {
+ inputRoles = {role};
+ } else {
+ outputRoles = {role};
+ }
+
+ // Allocate device memory.
+ DeviceBuffer buffer;
+ IPreparedModelParcel parcel;
+ parcel.preparedModel = kPreparedModel;
+ const auto ret = kDevice->allocate({}, {parcel}, inputRoles, outputRoles, &buffer);
+
+ // Check allocation results.
+ if (ret.isOk()) {
+ ASSERT_NE(buffer.buffer, nullptr);
+ ASSERT_GT(buffer.token, 0);
+ } else {
+ ASSERT_EQ(ret.getExceptionCode(), EX_SERVICE_SPECIFIC);
+ ASSERT_EQ(static_cast<ErrorStatus>(ret.getServiceSpecificError()),
+ ErrorStatus::GENERAL_FAILURE);
+ buffer.buffer = nullptr;
+ buffer.token = 0;
+ }
+
+ // Initialize input data from TestBuffer.
+ if constexpr (ioType == IOType::INPUT) {
+ if (buffer.buffer != nullptr) {
+ // TestBuffer -> Shared memory.
+ const auto& testBuffer =
+ kTestModel.main.operands[kTestModel.main.inputIndexes[index]].data;
+ ASSERT_GT(testBuffer.size(), 0);
+ const auto sharedMemory = nn::createSharedMemory(testBuffer.size()).value();
+ const auto memory = utils::convert(sharedMemory).value();
+ const auto mapping = nn::map(sharedMemory).value();
+ uint8_t* inputPtr = static_cast<uint8_t*>(std::get<void*>(mapping.pointer));
+ ASSERT_NE(inputPtr, nullptr);
+ const uint8_t* begin = testBuffer.get<uint8_t>();
+ const uint8_t* end = begin + testBuffer.size();
+ std::copy(begin, end, inputPtr);
+
+ // Shared memory -> IBuffer.
+ auto ret = buffer.buffer->copyFrom(memory, {});
+ ASSERT_TRUE(ret.isOk());
+ }
+ }
+ *result = {std::move(buffer.buffer), buffer.token};
+ }
+
+ const std::shared_ptr<IDevice> kDevice;
+ const std::shared_ptr<IPreparedModel> kPreparedModel;
+ const TestModel& kTestModel;
+};
+
+Subgraph createSubgraph(const TestSubgraph& testSubgraph, uint32_t* constCopySize,
+ std::vector<const TestBuffer*>* constCopies, uint32_t* constRefSize,
+ std::vector<const TestBuffer*>* constReferences) {
+ CHECK(constCopySize != nullptr);
+ CHECK(constCopies != nullptr);
+ CHECK(constRefSize != nullptr);
+ CHECK(constReferences != nullptr);
+
+ // Operands.
+ std::vector<Operand> operands(testSubgraph.operands.size());
+ for (uint32_t i = 0; i < testSubgraph.operands.size(); i++) {
+ const auto& op = testSubgraph.operands[i];
+
+ DataLocation loc = {};
+ if (op.lifetime == TestOperandLifeTime::CONSTANT_COPY) {
+ loc = {
+ .poolIndex = 0,
+ .offset = *constCopySize,
+ .length = static_cast<int64_t>(op.data.size()),
+ };
+ constCopies->push_back(&op.data);
+ *constCopySize += op.data.alignedSize();
+ } else if (op.lifetime == TestOperandLifeTime::CONSTANT_REFERENCE) {
+ loc = {
+ .poolIndex = 0,
+ .offset = *constRefSize,
+ .length = static_cast<int64_t>(op.data.size()),
+ };
+ constReferences->push_back(&op.data);
+ *constRefSize += op.data.alignedSize();
+ } else if (op.lifetime == TestOperandLifeTime::SUBGRAPH) {
+ loc = {
+ .poolIndex = 0,
+ .offset = *op.data.get<uint32_t>(),
+ .length = 0,
+ };
+ }
+
+ std::optional<OperandExtraParams> extraParams;
+ if (op.type == TestOperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) {
+ using Tag = OperandExtraParams::Tag;
+ extraParams = OperandExtraParams::make<Tag::channelQuant>(SymmPerChannelQuantParams{
+ .scales = op.channelQuant.scales,
+ .channelDim = static_cast<int32_t>(op.channelQuant.channelDim)});
+ }
+
+ operands[i] = {.type = static_cast<OperandType>(op.type),
+ .dimensions = utils::toSigned(op.dimensions).value(),
+ .scale = op.scale,
+ .zeroPoint = op.zeroPoint,
+ .lifetime = static_cast<OperandLifeTime>(op.lifetime),
+ .location = loc,
+ .extraParams = std::move(extraParams)};
+ }
+
+ // Operations.
+ std::vector<Operation> operations(testSubgraph.operations.size());
+ std::transform(testSubgraph.operations.begin(), testSubgraph.operations.end(),
+ operations.begin(), [](const TestOperation& op) -> Operation {
+ return {.type = static_cast<OperationType>(op.type),
+ .inputs = utils::toSigned(op.inputs).value(),
+ .outputs = utils::toSigned(op.outputs).value()};
+ });
+
+ return {.operands = std::move(operands),
+ .operations = std::move(operations),
+ .inputIndexes = utils::toSigned(testSubgraph.inputIndexes).value(),
+ .outputIndexes = utils::toSigned(testSubgraph.outputIndexes).value()};
+}
+
+void copyTestBuffers(const std::vector<const TestBuffer*>& buffers, uint8_t* output) {
+ uint32_t offset = 0;
+ for (const TestBuffer* buffer : buffers) {
+ const uint8_t* begin = buffer->get<uint8_t>();
+ const uint8_t* end = begin + buffer->size();
+ std::copy(begin, end, output + offset);
+ offset += buffer->alignedSize();
+ }
+}
+
+} // namespace
+
+void waitForSyncFence(int syncFd) {
+ constexpr int kInfiniteTimeout = -1;
+ ASSERT_GT(syncFd, 0);
+ int r = sync_wait(syncFd, kInfiniteTimeout);
+ ASSERT_GE(r, 0);
+}
+
+Model createModel(const TestModel& testModel) {
+ uint32_t constCopySize = 0;
+ uint32_t constRefSize = 0;
+ std::vector<const TestBuffer*> constCopies;
+ std::vector<const TestBuffer*> constReferences;
+
+ Subgraph mainSubgraph = createSubgraph(testModel.main, &constCopySize, &constCopies,
+ &constRefSize, &constReferences);
+ std::vector<Subgraph> refSubgraphs(testModel.referenced.size());
+ std::transform(testModel.referenced.begin(), testModel.referenced.end(), refSubgraphs.begin(),
+ [&constCopySize, &constCopies, &constRefSize,
+ &constReferences](const TestSubgraph& testSubgraph) {
+ return createSubgraph(testSubgraph, &constCopySize, &constCopies,
+ &constRefSize, &constReferences);
+ });
+
+ // Constant copies.
+ std::vector<uint8_t> operandValues(constCopySize);
+ copyTestBuffers(constCopies, operandValues.data());
+
+ // Shared memory.
+ std::vector<nn::Memory> pools = {};
+ if (constRefSize > 0) {
+ const auto pool = nn::createSharedMemory(constRefSize).value();
+ pools.push_back(pool);
+
+ // load data
+ const auto mappedMemory = nn::map(pool).value();
+ uint8_t* mappedPtr = static_cast<uint8_t*>(std::get<void*>(mappedMemory.pointer));
+ CHECK(mappedPtr != nullptr);
+
+ copyTestBuffers(constReferences, mappedPtr);
+ }
+
+ std::vector<Memory> aidlPools;
+ aidlPools.reserve(pools.size());
+ for (auto& pool : pools) {
+ auto aidlPool = utils::convert(pool).value();
+ aidlPools.push_back(std::move(aidlPool));
+ }
+
+ return {.main = std::move(mainSubgraph),
+ .referenced = std::move(refSubgraphs),
+ .operandValues = std::move(operandValues),
+ .pools = std::move(aidlPools),
+ .relaxComputationFloat32toFloat16 = testModel.isRelaxed};
+}
+
+static bool isOutputSizeGreaterThanOne(const TestModel& testModel, uint32_t index) {
+ const auto byteSize = testModel.main.operands[testModel.main.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->main.outputIndexes) {
+ auto& dims = model->main.operands[i].dimensions;
+ std::fill(dims.begin(), dims.end(), 0);
+ }
+}
+
+// Manages the lifetime of memory resources used in an execution.
+class ExecutionContext {
+ public:
+ ExecutionContext(std::shared_ptr<IDevice> device, std::shared_ptr<IPreparedModel> preparedModel)
+ : kDevice(std::move(device)), kPreparedModel(std::move(preparedModel)) {}
+
+ std::optional<Request> createRequest(const TestModel& testModel, MemoryType memoryType);
+ std::vector<TestBuffer> getOutputBuffers(const TestModel& testModel,
+ const Request& request) const;
+
+ private:
+ // Get a TestBuffer with data copied from an IBuffer object.
+ void getBuffer(const std::shared_ptr<IBuffer>& buffer, size_t size,
+ TestBuffer* testBuffer) const;
+
+ static constexpr uint32_t kInputPoolIndex = 0;
+ static constexpr uint32_t kOutputPoolIndex = 1;
+ static constexpr uint32_t kDeviceMemoryBeginIndex = 2;
+
+ const std::shared_ptr<IDevice> kDevice;
+ const std::shared_ptr<IPreparedModel> kPreparedModel;
+ std::unique_ptr<TestMemoryBase> mInputMemory, mOutputMemory;
+ std::vector<std::shared_ptr<IBuffer>> mBuffers;
+};
+
+std::optional<Request> ExecutionContext::createRequest(const TestModel& testModel,
+ MemoryType memoryType) {
+ // Memory pools are organized as:
+ // - 0: Input shared memory pool
+ // - 1: Output shared memory pool
+ // - [2, 2+i): Input device memories
+ // - [2+i, 2+i+o): Output device memories
+ DeviceMemoryAllocator allocator(kDevice, kPreparedModel, testModel);
+ std::vector<int32_t> tokens;
+ mBuffers.clear();
+
+ // Model inputs.
+ std::vector<RequestArgument> inputs(testModel.main.inputIndexes.size());
+ size_t inputSize = 0;
+ for (uint32_t i = 0; i < testModel.main.inputIndexes.size(); i++) {
+ const auto& op = testModel.main.operands[testModel.main.inputIndexes[i]];
+ if (op.data.size() == 0) {
+ // Omitted input.
+ inputs[i] = {.hasNoValue = true};
+ continue;
+ } else if (memoryType == MemoryType::DEVICE) {
+ SCOPED_TRACE("Input index = " + std::to_string(i));
+ auto [buffer, token] = allocator.allocate<IOType::INPUT>(i);
+ if (buffer != nullptr) {
+ DataLocation loc = {.poolIndex = static_cast<int32_t>(mBuffers.size() +
+ kDeviceMemoryBeginIndex)};
+ mBuffers.push_back(std::move(buffer));
+ tokens.push_back(token);
+ inputs[i] = {.hasNoValue = false, .location = loc, .dimensions = {}};
+ continue;
+ }
+ }
+
+ // Reserve shared memory for input.
+ DataLocation loc = {.poolIndex = kInputPoolIndex,
+ .offset = static_cast<int64_t>(inputSize),
+ .length = static_cast<int64_t>(op.data.size())};
+ inputSize += op.data.alignedSize();
+ inputs[i] = {.hasNoValue = false, .location = loc, .dimensions = {}};
+ }
+
+ // Model outputs.
+ std::vector<RequestArgument> outputs(testModel.main.outputIndexes.size());
+ size_t outputSize = 0;
+ for (uint32_t i = 0; i < testModel.main.outputIndexes.size(); i++) {
+ const auto& op = testModel.main.operands[testModel.main.outputIndexes[i]];
+ if (memoryType == MemoryType::DEVICE) {
+ SCOPED_TRACE("Output index = " + std::to_string(i));
+ auto [buffer, token] = allocator.allocate<IOType::OUTPUT>(i);
+ if (buffer != nullptr) {
+ DataLocation loc = {.poolIndex = static_cast<int32_t>(mBuffers.size() +
+ kDeviceMemoryBeginIndex)};
+ mBuffers.push_back(std::move(buffer));
+ tokens.push_back(token);
+ outputs[i] = {.hasNoValue = false, .location = loc, .dimensions = {}};
+ continue;
+ }
+ }
+
+ // In the case of zero-sized output, we should at least provide a one-byte buffer.
+ // This is because zero-sized tensors are only supported internally to the driver, or
+ // reported in output shapes. It is illegal for the client to pre-specify a zero-sized
+ // tensor as model output. Otherwise, we will have two semantic conflicts:
+ // - "Zero dimension" conflicts with "unspecified dimension".
+ // - "Omitted operand buffer" conflicts with "zero-sized operand buffer".
+ size_t bufferSize = std::max<size_t>(op.data.size(), 1);
+
+ // Reserve shared memory for output.
+ DataLocation loc = {.poolIndex = kOutputPoolIndex,
+ .offset = static_cast<int64_t>(outputSize),
+ .length = static_cast<int64_t>(bufferSize)};
+ outputSize += op.data.size() == 0 ? TestBuffer::kAlignment : op.data.alignedSize();
+ outputs[i] = {.hasNoValue = false, .location = loc, .dimensions = {}};
+ }
+
+ if (memoryType == MemoryType::DEVICE && mBuffers.empty()) {
+ return std::nullopt;
+ }
+
+ // Memory pools.
+ if (memoryType == MemoryType::BLOB_AHWB) {
+ mInputMemory = TestBlobAHWB::create(std::max<size_t>(inputSize, 1));
+ mOutputMemory = TestBlobAHWB::create(std::max<size_t>(outputSize, 1));
+ } else {
+ mInputMemory = TestAshmem::create(std::max<size_t>(inputSize, 1));
+ mOutputMemory = TestAshmem::create(std::max<size_t>(outputSize, 1));
+ }
+ CHECK_NE(mInputMemory, nullptr);
+ CHECK_NE(mOutputMemory, nullptr);
+ std::vector<RequestMemoryPool> pools;
+ pools.reserve(kDeviceMemoryBeginIndex + mBuffers.size());
+
+ auto copiedInputMemory = utils::clone(*mInputMemory->getAidlMemory());
+ CHECK(copiedInputMemory.has_value()) << copiedInputMemory.error().message;
+ auto copiedOutputMemory = utils::clone(*mOutputMemory->getAidlMemory());
+ CHECK(copiedOutputMemory.has_value()) << copiedOutputMemory.error().message;
+
+ pools.push_back(RequestMemoryPool::make<RequestMemoryPool::Tag::pool>(
+ std::move(copiedInputMemory).value()));
+ pools.push_back(RequestMemoryPool::make<RequestMemoryPool::Tag::pool>(
+ std::move(copiedOutputMemory).value()));
+ for (const auto& token : tokens) {
+ pools.push_back(RequestMemoryPool::make<RequestMemoryPool::Tag::token>(token));
+ }
+
+ // Copy input data to the input shared memory pool.
+ uint8_t* inputPtr = mInputMemory->getPointer();
+ for (uint32_t i = 0; i < testModel.main.inputIndexes.size(); i++) {
+ if (!inputs[i].hasNoValue && inputs[i].location.poolIndex == kInputPoolIndex) {
+ const auto& op = testModel.main.operands[testModel.main.inputIndexes[i]];
+ const uint8_t* begin = op.data.get<uint8_t>();
+ const uint8_t* end = begin + op.data.size();
+ std::copy(begin, end, inputPtr + inputs[i].location.offset);
+ }
+ }
+ return Request{
+ .inputs = std::move(inputs), .outputs = std::move(outputs), .pools = std::move(pools)};
+}
+
+std::vector<TestBuffer> ExecutionContext::getOutputBuffers(const TestModel& testModel,
+ const Request& request) const {
+ // Copy out output results.
+ uint8_t* outputPtr = mOutputMemory->getPointer();
+ std::vector<TestBuffer> outputBuffers;
+ for (uint32_t i = 0; i < request.outputs.size(); i++) {
+ const auto& outputLoc = request.outputs[i].location;
+ if (outputLoc.poolIndex == kOutputPoolIndex) {
+ outputBuffers.emplace_back(outputLoc.length, outputPtr + outputLoc.offset);
+ } else {
+ const auto& op = testModel.main.operands[testModel.main.outputIndexes[i]];
+ if (op.data.size() == 0) {
+ outputBuffers.emplace_back(0, nullptr);
+ } else {
+ SCOPED_TRACE("Output index = " + std::to_string(i));
+ const uint32_t bufferIndex = outputLoc.poolIndex - kDeviceMemoryBeginIndex;
+ TestBuffer buffer;
+ getBuffer(mBuffers[bufferIndex], op.data.size(), &buffer);
+ outputBuffers.push_back(std::move(buffer));
+ }
+ }
+ }
+ return outputBuffers;
+}
+
+// Get a TestBuffer with data copied from an IBuffer object.
+void ExecutionContext::getBuffer(const std::shared_ptr<IBuffer>& buffer, size_t size,
+ TestBuffer* testBuffer) const {
+ // IBuffer -> Shared memory.
+ auto sharedMemory = nn::createSharedMemory(size).value();
+ auto aidlMemory = utils::convert(sharedMemory).value();
+ const auto ret = buffer->copyTo(aidlMemory);
+ ASSERT_TRUE(ret.isOk());
+
+ // Shared memory -> TestBuffer.
+ const auto outputMemory = nn::map(sharedMemory).value();
+ const uint8_t* outputPtr = std::visit(
+ [](auto* ptr) { return static_cast<const uint8_t*>(ptr); }, outputMemory.pointer);
+ ASSERT_NE(outputPtr, nullptr);
+ ASSERT_NE(testBuffer, nullptr);
+ *testBuffer = TestBuffer(size, outputPtr);
+}
+
+static bool hasZeroSizedOutput(const TestModel& testModel) {
+ return std::any_of(testModel.main.outputIndexes.begin(), testModel.main.outputIndexes.end(),
+ [&testModel](uint32_t index) {
+ return testModel.main.operands[index].data.size() == 0;
+ });
+}
+
+void EvaluatePreparedModel(const std::shared_ptr<IDevice>& device,
+ const std::shared_ptr<IPreparedModel>& preparedModel,
+ const TestModel& testModel, const TestConfig& testConfig,
+ bool* skipped = nullptr) {
+ if (skipped != nullptr) {
+ *skipped = false;
+ }
+ // If output0 does not have size larger than one byte, we can not test with insufficient buffer.
+ if (testConfig.outputType == OutputType::INSUFFICIENT &&
+ !isOutputSizeGreaterThanOne(testModel, 0)) {
+ return;
+ }
+
+ ExecutionContext context(device, preparedModel);
+ auto maybeRequest = context.createRequest(testModel, testConfig.memoryType);
+ // Skip if testing memory domain but no device memory has been allocated.
+ if (!maybeRequest.has_value()) {
+ return;
+ }
+
+ Request request = std::move(maybeRequest).value();
+
+ constexpr uint32_t kInsufficientOutputIndex = 0;
+ if (testConfig.outputType == OutputType::INSUFFICIENT) {
+ makeOutputInsufficientSize(kInsufficientOutputIndex, &request);
+ }
+
+ int64_t loopTimeoutDuration = kOmittedTimeoutDuration;
+ // OutputType::MISSED_DEADLINE is only used by
+ // TestKind::INTINITE_LOOP_TIMEOUT tests to verify that an infinite loop is
+ // aborted after a timeout.
+ if (testConfig.outputType == OutputType::MISSED_DEADLINE) {
+ // Override the default loop timeout duration with a small value to
+ // speed up test execution.
+ constexpr int64_t kMillisecond = 1'000'000;
+ loopTimeoutDuration = 1 * kMillisecond;
+ }
+
+ ErrorStatus executionStatus;
+ std::vector<OutputShape> outputShapes;
+ Timing timing = kNoTiming;
+ switch (testConfig.executor) {
+ case Executor::SYNC: {
+ SCOPED_TRACE("synchronous");
+
+ ExecutionResult executionResult;
+ // execute
+ const auto ret = preparedModel->executeSynchronously(request, testConfig.measureTiming,
+ kNoDeadline, loopTimeoutDuration,
+ &executionResult);
+ ASSERT_TRUE(ret.isOk() || ret.getExceptionCode() == EX_SERVICE_SPECIFIC)
+ << ret.getDescription();
+ if (ret.isOk()) {
+ executionStatus = executionResult.outputSufficientSize
+ ? ErrorStatus::NONE
+ : ErrorStatus::OUTPUT_INSUFFICIENT_SIZE;
+ outputShapes = std::move(executionResult.outputShapes);
+ timing = executionResult.timing;
+ } else {
+ executionStatus = static_cast<ErrorStatus>(ret.getServiceSpecificError());
+ }
+ break;
+ }
+ case Executor::FENCED: {
+ SCOPED_TRACE("fenced");
+ ErrorStatus result = ErrorStatus::NONE;
+ ndk::ScopedFileDescriptor syncFenceFd;
+ std::shared_ptr<IFencedExecutionCallback> fencedCallback;
+ auto ret = preparedModel->executeFenced(request, {}, testConfig.measureTiming,
+ kNoDeadline, loopTimeoutDuration, kNoDuration,
+ &syncFenceFd, &fencedCallback);
+ ASSERT_TRUE(ret.isOk() || ret.getExceptionCode() == EX_SERVICE_SPECIFIC)
+ << ret.getDescription();
+ if (!ret.isOk()) {
+ result = static_cast<ErrorStatus>(ret.getServiceSpecificError());
+ executionStatus = result;
+ } else if (syncFenceFd.get() != -1) {
+ std::vector<ndk::ScopedFileDescriptor> waitFor;
+ auto dupFd = dup(syncFenceFd.get());
+ ASSERT_NE(dupFd, -1);
+ waitFor.emplace_back(dupFd);
+ // If a sync fence is returned, try start another run waiting for the sync fence.
+ ret = preparedModel->executeFenced(request, waitFor, testConfig.measureTiming,
+ kNoDeadline, loopTimeoutDuration, kNoDuration,
+ &syncFenceFd, &fencedCallback);
+ ASSERT_TRUE(ret.isOk());
+ waitForSyncFence(syncFenceFd.get());
+ }
+ if (result == ErrorStatus::NONE) {
+ ASSERT_NE(fencedCallback, nullptr);
+ Timing timingFenced;
+ auto ret =
+ fencedCallback->getExecutionInfo(&timing, &timingFenced, &executionStatus);
+ ASSERT_TRUE(ret.isOk());
+ }
+ break;
+ }
+ default: {
+ FAIL() << "Unsupported execution mode for AIDL interface.";
+ }
+ }
+
+ if (testConfig.outputType != OutputType::FULLY_SPECIFIED &&
+ executionStatus == ErrorStatus::GENERAL_FAILURE) {
+ if (skipped != nullptr) {
+ *skipped = true;
+ }
+ if (!testConfig.reportSkipping) {
+ return;
+ }
+ 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 (!testConfig.measureTiming) {
+ EXPECT_EQ(timing, kNoTiming);
+ } else {
+ if (timing.timeOnDevice != -1 && timing.timeInDriver != -1) {
+ EXPECT_LE(timing.timeOnDevice, timing.timeInDriver);
+ }
+ }
+
+ switch (testConfig.outputType) {
+ case OutputType::FULLY_SPECIFIED:
+ if (testConfig.executor == Executor::FENCED && hasZeroSizedOutput(testModel)) {
+ // Executor::FENCED does not support zero-sized output.
+ ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, executionStatus);
+ return;
+ }
+ // 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.main.outputIndexes.size());
+ break;
+ case OutputType::UNSPECIFIED:
+ if (testConfig.executor == Executor::FENCED) {
+ // For Executor::FENCED, the output shape must be fully specified.
+ ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, executionStatus);
+ return;
+ }
+ // 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.main.outputIndexes.size());
+ break;
+ case OutputType::INSUFFICIENT:
+ if (testConfig.executor == Executor::FENCED) {
+ // For Executor::FENCED, the output shape must be fully specified.
+ ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, executionStatus);
+ return;
+ }
+ ASSERT_EQ(ErrorStatus::OUTPUT_INSUFFICIENT_SIZE, executionStatus);
+ ASSERT_EQ(outputShapes.size(), testModel.main.outputIndexes.size());
+ // Check that all returned output dimensions are at least as fully specified as the
+ // union of the information about the corresponding operand in the model and in the
+ // request. In this test, all model outputs have known rank with all dimensions
+ // unspecified, and no dimensional information is provided in the request.
+ for (uint32_t i = 0; i < outputShapes.size(); i++) {
+ ASSERT_EQ(outputShapes[i].isSufficient, i != kInsufficientOutputIndex);
+ const auto& actual = outputShapes[i].dimensions;
+ const auto& golden =
+ testModel.main.operands[testModel.main.outputIndexes[i]].dimensions;
+ ASSERT_EQ(actual.size(), golden.size());
+ for (uint32_t j = 0; j < actual.size(); j++) {
+ if (actual[j] == 0) continue;
+ EXPECT_EQ(actual[j], golden[j]) << "index: " << j;
+ }
+ }
+ return;
+ case OutputType::MISSED_DEADLINE:
+ ASSERT_TRUE(executionStatus == ErrorStatus::MISSED_DEADLINE_TRANSIENT ||
+ executionStatus == ErrorStatus::MISSED_DEADLINE_PERSISTENT)
+ << "executionStatus = " << executionStatus;
+ 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.main.operands[testModel.main.outputIndexes[i]].dimensions;
+ const auto unsignedActual = nn::toUnsigned(outputShapes[i].dimensions);
+ ASSERT_TRUE(unsignedActual.has_value());
+ const std::vector<uint32_t>& actual = unsignedActual.value();
+ EXPECT_EQ(expect, actual);
+ }
+
+ // Retrieve execution results.
+ const std::vector<TestBuffer> outputs = context.getOutputBuffers(testModel, request);
+
+ // We want "close-enough" results.
+ checkResults(testModel, outputs);
+}
+
+void EvaluatePreparedModel(const std::shared_ptr<IDevice>& device,
+ const std::shared_ptr<IPreparedModel>& preparedModel,
+ const TestModel& testModel, TestKind testKind) {
+ std::vector<OutputType> outputTypesList;
+ std::vector<bool> measureTimingList;
+ std::vector<Executor> executorList;
+ std::vector<MemoryType> memoryTypeList;
+
+ switch (testKind) {
+ case TestKind::GENERAL: {
+ outputTypesList = {OutputType::FULLY_SPECIFIED};
+ measureTimingList = {false, true};
+ executorList = {Executor::SYNC};
+ memoryTypeList = {MemoryType::ASHMEM};
+ } break;
+ case TestKind::DYNAMIC_SHAPE: {
+ outputTypesList = {OutputType::UNSPECIFIED, OutputType::INSUFFICIENT};
+ measureTimingList = {false, true};
+ executorList = {Executor::SYNC, Executor::FENCED};
+ memoryTypeList = {MemoryType::ASHMEM};
+ } break;
+ case TestKind::MEMORY_DOMAIN: {
+ outputTypesList = {OutputType::FULLY_SPECIFIED};
+ measureTimingList = {false};
+ executorList = {Executor::SYNC, Executor::FENCED};
+ memoryTypeList = {MemoryType::BLOB_AHWB, MemoryType::DEVICE};
+ } break;
+ case TestKind::FENCED_COMPUTE: {
+ outputTypesList = {OutputType::FULLY_SPECIFIED};
+ measureTimingList = {false, true};
+ executorList = {Executor::FENCED};
+ memoryTypeList = {MemoryType::ASHMEM};
+ } break;
+ case TestKind::QUANTIZATION_COUPLING: {
+ LOG(FATAL) << "Wrong TestKind for EvaluatePreparedModel";
+ return;
+ } break;
+ case TestKind::INTINITE_LOOP_TIMEOUT: {
+ outputTypesList = {OutputType::MISSED_DEADLINE};
+ measureTimingList = {false, true};
+ executorList = {Executor::SYNC, Executor::FENCED};
+ memoryTypeList = {MemoryType::ASHMEM};
+ } break;
+ }
+
+ for (const OutputType outputType : outputTypesList) {
+ for (const bool measureTiming : measureTimingList) {
+ for (const Executor executor : executorList) {
+ for (const MemoryType memoryType : memoryTypeList) {
+ const TestConfig testConfig(executor, measureTiming, outputType, memoryType);
+ EvaluatePreparedModel(device, preparedModel, testModel, testConfig);
+ }
+ }
+ }
+ }
+}
+
+void EvaluatePreparedCoupledModels(const std::shared_ptr<IDevice>& device,
+ const std::shared_ptr<IPreparedModel>& preparedModel,
+ const TestModel& testModel,
+ const std::shared_ptr<IPreparedModel>& preparedCoupledModel,
+ const TestModel& coupledModel) {
+ const std::vector<OutputType> outputTypesList = {OutputType::FULLY_SPECIFIED};
+ const std::vector<bool> measureTimingList = {false, true};
+ const std::vector<Executor> executorList = {Executor::SYNC, Executor::FENCED};
+
+ for (const OutputType outputType : outputTypesList) {
+ for (const bool measureTiming : measureTimingList) {
+ for (const Executor executor : executorList) {
+ const TestConfig testConfig(executor, measureTiming, outputType, MemoryType::ASHMEM,
+ /*reportSkipping=*/false);
+ bool baseSkipped = false;
+ EvaluatePreparedModel(device, preparedModel, testModel, testConfig, &baseSkipped);
+ bool coupledSkipped = false;
+ EvaluatePreparedModel(device, preparedCoupledModel, coupledModel, testConfig,
+ &coupledSkipped);
+ ASSERT_EQ(baseSkipped, coupledSkipped);
+ if (baseSkipped) {
+ 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();
+ }
+ }
+ }
+ }
+}
+
+void Execute(const std::shared_ptr<IDevice>& device, const TestModel& testModel,
+ TestKind testKind) {
+ Model model = createModel(testModel);
+ if (testKind == TestKind::DYNAMIC_SHAPE) {
+ makeOutputDimensionsUnspecified(&model);
+ }
+
+ std::shared_ptr<IPreparedModel> preparedModel;
+ switch (testKind) {
+ case TestKind::GENERAL:
+ case TestKind::DYNAMIC_SHAPE:
+ case TestKind::MEMORY_DOMAIN:
+ case TestKind::FENCED_COMPUTE:
+ case TestKind::INTINITE_LOOP_TIMEOUT: {
+ createPreparedModel(device, model, &preparedModel);
+ if (preparedModel == nullptr) return;
+ EvaluatePreparedModel(device, preparedModel, testModel, testKind);
+ } break;
+ case TestKind::QUANTIZATION_COUPLING: {
+ ASSERT_TRUE(testModel.hasQuant8CoupledOperands());
+ createPreparedModel(device, model, &preparedModel,
+ /*reportSkipping*/ false);
+ TestModel signedQuantizedModel = convertQuant8AsymmOperandsToSigned(testModel);
+ std::shared_ptr<IPreparedModel> preparedCoupledModel;
+ createPreparedModel(device, createModel(signedQuantizedModel), &preparedCoupledModel,
+ /*reportSkipping*/ false);
+ // If we couldn't prepare a model with unsigned quantization, we must
+ // fail to prepare a model with signed quantization as well.
+ if (preparedModel == nullptr) {
+ ASSERT_EQ(preparedCoupledModel, nullptr);
+ // If we failed to prepare both of the models, we can safely skip
+ // the test.
+ 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_NE(preparedCoupledModel, nullptr);
+ EvaluatePreparedCoupledModels(device, preparedModel, testModel, preparedCoupledModel,
+ signedQuantizedModel);
+ } break;
+ }
+}
+
+void GeneratedTestBase::SetUp() {
+ testing::TestWithParam<GeneratedTestParam>::SetUp();
+ ASSERT_NE(kDevice, nullptr);
+}
+
+std::vector<NamedModel> getNamedModels(const FilterFn& filter) {
+ return TestModelManager::get().getTestModels(filter);
+}
+
+std::vector<NamedModel> getNamedModels(const FilterNameFn& 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 {};
+
+// Tag for the memory domain tests
+class MemoryDomainTest : public GeneratedTest {};
+
+// Tag for the fenced compute tests
+class FencedComputeTest : public GeneratedTest {};
+
+// Tag for the dynamic output shape tests
+class QuantizationCouplingTest : public GeneratedTest {};
+
+// Tag for the loop timeout tests
+class InfiniteLoopTimeoutTest : public GeneratedTest {};
+
+TEST_P(GeneratedTest, Test) {
+ Execute(kDevice, kTestModel, TestKind::GENERAL);
+}
+
+TEST_P(DynamicOutputShapeTest, Test) {
+ Execute(kDevice, kTestModel, TestKind::DYNAMIC_SHAPE);
+}
+
+TEST_P(MemoryDomainTest, Test) {
+ Execute(kDevice, kTestModel, TestKind::MEMORY_DOMAIN);
+}
+
+TEST_P(FencedComputeTest, Test) {
+ Execute(kDevice, kTestModel, TestKind::FENCED_COMPUTE);
+}
+
+TEST_P(QuantizationCouplingTest, Test) {
+ Execute(kDevice, kTestModel, TestKind::QUANTIZATION_COUPLING);
+}
+
+TEST_P(InfiniteLoopTimeoutTest, Test) {
+ Execute(kDevice, kTestModel, TestKind::INTINITE_LOOP_TIMEOUT);
+}
+
+INSTANTIATE_GENERATED_TEST(GeneratedTest,
+ [](const TestModel& testModel) { return !testModel.expectFailure; });
+
+INSTANTIATE_GENERATED_TEST(DynamicOutputShapeTest, [](const TestModel& testModel) {
+ return !testModel.expectFailure && !testModel.hasScalarOutputs();
+});
+
+INSTANTIATE_GENERATED_TEST(MemoryDomainTest,
+ [](const TestModel& testModel) { return !testModel.expectFailure; });
+
+INSTANTIATE_GENERATED_TEST(FencedComputeTest,
+ [](const TestModel& testModel) { return !testModel.expectFailure; });
+
+INSTANTIATE_GENERATED_TEST(QuantizationCouplingTest, [](const TestModel& testModel) {
+ return !testModel.expectFailure && testModel.hasQuant8CoupledOperands() &&
+ testModel.main.operations.size() == 1;
+});
+
+INSTANTIATE_GENERATED_TEST(InfiniteLoopTimeoutTest, [](const TestModel& testModel) {
+ return testModel.isInfiniteLoopTimeoutTest();
+});
+
+} // namespace aidl::android::hardware::neuralnetworks::vts::functional
diff --git a/neuralnetworks/aidl/vts/functional/GeneratedTestHarness.h b/neuralnetworks/aidl/vts/functional/GeneratedTestHarness.h
new file mode 100644
index 0000000..ad40f06
--- /dev/null
+++ b/neuralnetworks/aidl/vts/functional/GeneratedTestHarness.h
@@ -0,0 +1,88 @@
+/*
+ * Copyright (C) 2021 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_AIDL_GENERATED_TEST_HARNESS_H
+#define ANDROID_HARDWARE_NEURALNETWORKS_AIDL_GENERATED_TEST_HARNESS_H
+
+#include <functional>
+#include <vector>
+
+#include <TestHarness.h>
+#include "Utils.h"
+#include "VtsHalNeuralnetworks.h"
+
+namespace aidl::android::hardware::neuralnetworks::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 std::shared_ptr<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);
+
+using FilterNameFn = std::function<bool(const std::string&)>;
+std::vector<NamedModel> getNamedModels(const FilterNameFn& filter);
+
+std::string printGeneratedTest(const testing::TestParamInfo<GeneratedTestParam>& info);
+
+#define INSTANTIATE_GENERATED_TEST(TestSuite, filter) \
+ GTEST_ALLOW_UNINSTANTIATED_PARAMETERIZED_TEST(TestSuite); \
+ 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 std::shared_ptr<IDevice>& device, const Model& model,
+ std::shared_ptr<IPreparedModel>* preparedModel);
+
+enum class TestKind {
+ // Runs a test model and compares the results to a golden data
+ GENERAL,
+ // Same as GENERAL but sets dimensions for the output tensors to zeros
+ DYNAMIC_SHAPE,
+ // Same as GENERAL but use device memories for inputs and outputs
+ MEMORY_DOMAIN,
+ // Same as GENERAL but use executeFenced for exeuction
+ FENCED_COMPUTE,
+ // Tests if quantized model with TENSOR_QUANT8_ASYMM produces the same result
+ // (OK/SKIPPED/FAILED) as the model with all such tensors converted to
+ // TENSOR_QUANT8_ASYMM_SIGNED.
+ QUANTIZATION_COUPLING,
+ // Runs a test model and verifies that MISSED_DEADLINE_* is returned.
+ INTINITE_LOOP_TIMEOUT
+};
+
+void EvaluatePreparedModel(const std::shared_ptr<IDevice>& device,
+ const std::shared_ptr<IPreparedModel>& preparedModel,
+ const test_helper::TestModel& testModel, TestKind testKind);
+
+void waitForSyncFence(int syncFd);
+
+} // namespace aidl::android::hardware::neuralnetworks::vts::functional
+
+#endif // ANDROID_HARDWARE_NEURALNETWORKS_AIDL_GENERATED_TEST_HARNESS_H
diff --git a/neuralnetworks/aidl/vts/functional/LogTestCaseToLogcat.h b/neuralnetworks/aidl/vts/functional/LogTestCaseToLogcat.h
new file mode 100644
index 0000000..c9fd432
--- /dev/null
+++ b/neuralnetworks/aidl/vts/functional/LogTestCaseToLogcat.h
@@ -0,0 +1,40 @@
+/*
+ * Copyright (C) 2021 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_AIDL_LOG_TEST_CASE_TO_LOGCAT_H
+#define ANDROID_HARDWARE_NEURALNETWORKS_AIDL_LOG_TEST_CASE_TO_LOGCAT_H
+
+#include <android-base/logging.h>
+#include <gtest/gtest.h>
+
+namespace aidl::android::hardware::neuralnetworks {
+
+class LogTestCaseToLogcat : public ::testing::EmptyTestEventListener {
+ public:
+ void OnTestStart(const ::testing::TestInfo& test_info) override {
+ LOG(INFO) << "[Test Case] " << test_info.test_suite_name() << "." << test_info.name()
+ << " BEGIN";
+ }
+
+ void OnTestEnd(const ::testing::TestInfo& test_info) override {
+ LOG(INFO) << "[Test Case] " << test_info.test_suite_name() << "." << test_info.name()
+ << " END";
+ }
+};
+
+} // namespace aidl::android::hardware::neuralnetworks
+
+#endif // ANDROID_HARDWARE_NEURALNETWORKS_AIDL_LOG_TEST_CASE_TO_LOGCAT_H
diff --git a/neuralnetworks/aidl/vts/functional/MemoryDomainTests.cpp b/neuralnetworks/aidl/vts/functional/MemoryDomainTests.cpp
new file mode 100644
index 0000000..a37a0ca
--- /dev/null
+++ b/neuralnetworks/aidl/vts/functional/MemoryDomainTests.cpp
@@ -0,0 +1,1176 @@
+/*
+ * Copyright (C) 2021 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_aidl_hal_test"
+
+#include <android-base/logging.h>
+#include <android/binder_auto_utils.h>
+#include <android/binder_interface_utils.h>
+#include <android/binder_status.h>
+#include <gtest/gtest.h>
+
+#include <LegacyUtils.h>
+#include <TestHarness.h>
+#include <Utils.h>
+#include <nnapi/SharedMemory.h>
+#include <nnapi/hal/aidl/Conversions.h>
+#include <nnapi/hal/aidl/Utils.h>
+
+#include "AidlHalInterfaces.h"
+#include "Callbacks.h"
+#include "GeneratedTestHarness.h"
+#include "MemoryUtils.h"
+#include "Utils.h"
+#include "VtsHalNeuralnetworks.h"
+
+namespace aidl::android::hardware::neuralnetworks::vts::functional {
+
+using namespace test_helper;
+using implementation::PreparedModelCallback;
+
+namespace {
+
+// An AIDL driver is likely to support at least one of the following operand types.
+const std::vector<TestOperandType> kTestOperandTypeChoicesVector = {
+ TestOperandType::TENSOR_FLOAT32,
+ TestOperandType::TENSOR_FLOAT16,
+ TestOperandType::TENSOR_QUANT8_ASYMM,
+ TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED,
+};
+const auto kTestOperandTypeChoices = testing::ValuesIn(kTestOperandTypeChoicesVector);
+// TODO(b/179270601): restore kNamedDeviceChoices
+
+bool isInChoices(TestOperandType type) {
+ return std::count(kTestOperandTypeChoicesVector.begin(), kTestOperandTypeChoicesVector.end(),
+ type) > 0;
+}
+
+bool isFloat(TestOperandType type) {
+ CHECK(isInChoices(type));
+ return type == TestOperandType::TENSOR_FLOAT32 || type == TestOperandType::TENSOR_FLOAT16;
+}
+
+// Create placeholder buffers for model constants as well as inputs and outputs.
+// We only care about the size here because we will not check accuracy in validation tests.
+void createDummyData(TestModel* testModel) {
+ for (auto& operand : testModel->main.operands) {
+ if (operand.data != nullptr) continue;
+ switch (operand.lifetime) {
+ case TestOperandLifeTime::SUBGRAPH_INPUT:
+ case TestOperandLifeTime::SUBGRAPH_OUTPUT:
+ case TestOperandLifeTime::CONSTANT_COPY:
+ case TestOperandLifeTime::CONSTANT_REFERENCE: {
+ const uint32_t size = nn::nonExtensionOperandSizeOfData(
+ static_cast<nn::OperandType>(operand.type), operand.dimensions);
+ operand.data = TestBuffer(size);
+ } break;
+ default:
+ break;
+ }
+ }
+}
+
+TestOperand createInt32Scalar(int32_t value) {
+ return {
+ .type = TestOperandType::INT32,
+ .dimensions = {},
+ .numberOfConsumers = 1,
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = TestOperandLifeTime::CONSTANT_COPY,
+ .data = TestBuffer::createFromVector<int32_t>({value}),
+ };
+}
+
+// Construct a test model with multiple CONV_2D operations with the given operand as inputs.
+// The dimensions of the filters are chosen to ensure outputs has the same dimensions as inputs.
+// We choose CONV_2D operation because it is commonly supported by most drivers.
+TestModel createConvModel(const TestOperand& operand, uint32_t numOperations) {
+ CHECK(isInChoices(operand.type));
+
+ TestOperand weight = {.type = operand.type,
+ .dimensions = {operand.dimensions[3], 3, 3, operand.dimensions[3]},
+ .numberOfConsumers = 1,
+ .scale = isFloat(operand.type) ? 0.0f : 1.0f,
+ .zeroPoint = 0,
+ .lifetime = TestOperandLifeTime::CONSTANT_COPY};
+
+ TestOperand bias = {
+ .type = isFloat(operand.type) ? operand.type : TestOperandType::TENSOR_INT32,
+ .dimensions = {operand.dimensions[3]},
+ .numberOfConsumers = 1,
+ .scale = operand.scale * weight.scale,
+ .zeroPoint = 0,
+ .lifetime = TestOperandLifeTime::CONSTANT_COPY};
+
+ TestOperand output = operand;
+ output.numberOfConsumers = 0;
+ output.lifetime = TestOperandLifeTime::SUBGRAPH_OUTPUT;
+
+ const std::vector<TestOperand> operands = {
+ operand,
+ std::move(weight),
+ std::move(bias),
+ createInt32Scalar(1), // same padding
+ createInt32Scalar(1), // width stride
+ createInt32Scalar(1), // height stride
+ createInt32Scalar(0), // activation = NONE
+ std::move(output),
+ };
+
+ TestModel model;
+ for (uint32_t i = 0; i < numOperations; i++) {
+ model.main.operands.insert(model.main.operands.end(), operands.begin(), operands.end());
+ const uint32_t inputIndex = operands.size() * i;
+ const uint32_t outputIndex = inputIndex + operands.size() - 1;
+ std::vector<uint32_t> inputs(operands.size() - 1);
+ std::iota(inputs.begin(), inputs.end(), inputIndex);
+ model.main.operations.push_back({.type = TestOperationType::CONV_2D,
+ .inputs = std::move(inputs),
+ .outputs = {outputIndex}});
+ model.main.inputIndexes.push_back(inputIndex);
+ model.main.outputIndexes.push_back(outputIndex);
+ }
+ createDummyData(&model);
+ return model;
+}
+
+// Construct a test model with a single ADD operation with the given operand as input0 and input1.
+// This is to cover additional cases that the CONV_2D model does not support, e.g. arbitrary input
+// operand rank, scalar input operand. We choose ADD operation because it is commonly supported by
+// most drivers.
+TestModel createSingleAddModel(const TestOperand& operand) {
+ CHECK(isInChoices(operand.type));
+
+ TestOperand act = {
+ .type = TestOperandType::INT32,
+ .dimensions = {},
+ .numberOfConsumers = 1,
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = TestOperandLifeTime::SUBGRAPH_INPUT,
+ };
+
+ TestOperand output = operand;
+ output.numberOfConsumers = 0;
+ output.lifetime = TestOperandLifeTime::SUBGRAPH_OUTPUT;
+
+ TestModel model = {
+ .main =
+ {
+ .operands =
+ {
+ operand,
+ operand,
+ std::move(act),
+ output,
+ },
+ .operations = {{.type = TestOperationType::ADD,
+ .inputs = {0, 1, 2},
+ .outputs = {3}}},
+ .inputIndexes = {0, 1, 2},
+ .outputIndexes = {3},
+ },
+ };
+ createDummyData(&model);
+ return model;
+}
+
+// A placeholder invalid IPreparedModel class for MemoryDomainAllocateTest.InvalidPreparedModel
+class InvalidPreparedModel : public BnPreparedModel {
+ public:
+ ndk::ScopedAStatus executeSynchronously(const Request&, bool, int64_t, int64_t,
+ ExecutionResult*) override {
+ return ndk::ScopedAStatus::fromServiceSpecificError(
+ static_cast<int32_t>(ErrorStatus::GENERAL_FAILURE));
+ }
+ ndk::ScopedAStatus executeFenced(const Request&, const std::vector<ndk::ScopedFileDescriptor>&,
+ bool, int64_t, int64_t, int64_t, ndk::ScopedFileDescriptor*,
+ std::shared_ptr<IFencedExecutionCallback>*) override {
+ return ndk::ScopedAStatus::fromServiceSpecificError(
+ static_cast<int32_t>(ErrorStatus::GENERAL_FAILURE));
+ }
+};
+
+template <typename... Args>
+std::vector<RequestMemoryPool> createRequestMemoryPools(const Args&... pools) {
+ std::vector<RequestMemoryPool> memoryPools;
+ memoryPools.reserve(sizeof...(Args));
+ // This fold operator calls push_back on each of the function arguments.
+ (memoryPools.push_back(utils::clone(pools).value()), ...);
+ return memoryPools;
+};
+
+} // namespace
+
+class MemoryDomainTestBase : public testing::Test {
+ protected:
+ MemoryDomainTestBase(std::shared_ptr<IDevice> device, TestOperandType type)
+ : kDevice(std::move(device)),
+ kTestOperandType(type),
+ kTestOperand(kTestOperandMap.at(type)),
+ kTestOperandDataSize(nn::nonExtensionOperandSizeOfData(static_cast<nn::OperandType>(type),
+ kTestOperand.dimensions)) {}
+
+ void SetUp() override {
+ testing::Test::SetUp();
+ ASSERT_NE(kDevice, nullptr);
+ }
+
+ std::shared_ptr<IPreparedModel> createConvPreparedModel(const TestOperand& testOperand,
+ uint32_t numOperations = 1) {
+ const TestModel testModel = createConvModel(testOperand, numOperations);
+ const Model model = createModel(testModel);
+ std::shared_ptr<IPreparedModel> preparedModel;
+ createPreparedModel(kDevice, model, &preparedModel, /*reportSkipping=*/false);
+ return preparedModel;
+ }
+
+ std::shared_ptr<IPreparedModel> createAddPreparedModel(const TestOperand& testOperand) {
+ const TestModel testModel = createSingleAddModel(testOperand);
+ const Model model = createModel(testModel);
+ std::shared_ptr<IPreparedModel> preparedModel;
+ createPreparedModel(kDevice, model, &preparedModel, /*reportSkipping=*/false);
+ return preparedModel;
+ }
+
+ static const std::map<TestOperandType, TestOperand> kTestOperandMap;
+
+ const std::shared_ptr<IDevice> kDevice;
+ const TestOperandType kTestOperandType;
+ const TestOperand& kTestOperand;
+ const uint32_t kTestOperandDataSize;
+};
+
+const std::map<TestOperandType, TestOperand> MemoryDomainTestBase::kTestOperandMap = {
+ {TestOperandType::TENSOR_FLOAT32,
+ {
+ .type = TestOperandType::TENSOR_FLOAT32,
+ .dimensions = {1, 32, 32, 8},
+ .numberOfConsumers = 1,
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = TestOperandLifeTime::SUBGRAPH_INPUT,
+ }},
+ {TestOperandType::TENSOR_FLOAT16,
+ {
+ .type = TestOperandType::TENSOR_FLOAT16,
+ .dimensions = {1, 32, 32, 8},
+ .numberOfConsumers = 1,
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = TestOperandLifeTime::SUBGRAPH_INPUT,
+ }},
+ {TestOperandType::TENSOR_QUANT8_ASYMM,
+ {
+ .type = TestOperandType::TENSOR_QUANT8_ASYMM,
+ .dimensions = {1, 32, 32, 8},
+ .numberOfConsumers = 1,
+ .scale = 0.5f,
+ .zeroPoint = 0,
+ .lifetime = TestOperandLifeTime::SUBGRAPH_INPUT,
+ }},
+ {TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED,
+ {
+ .type = TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED,
+ .dimensions = {1, 32, 32, 8},
+ .numberOfConsumers = 1,
+ .scale = 0.5f,
+ .zeroPoint = 0,
+ .lifetime = TestOperandLifeTime::SUBGRAPH_INPUT,
+ }},
+};
+
+using MemoryDomainAllocateTestParam = std::tuple<NamedDevice, TestOperandType>;
+class MemoryDomainAllocateTest : public MemoryDomainTestBase,
+ public testing::WithParamInterface<MemoryDomainAllocateTestParam> {
+ protected:
+ MemoryDomainAllocateTest()
+ : MemoryDomainTestBase(getData(std::get<NamedDevice>(GetParam())),
+ std::get<TestOperandType>(GetParam())) {}
+
+ struct AllocateTestArgs {
+ std::vector<int32_t> dimensions;
+ std::vector<std::shared_ptr<IPreparedModel>> preparedModels;
+ std::vector<BufferRole> inputRoles;
+ std::vector<BufferRole> outputRoles;
+ };
+
+ // Validation test for IDevice::allocate. The driver is expected to fail with INVALID_ARGUMENT,
+ // or GENERAL_FAILURE if memory domain is not supported.
+ void validateAllocate(AllocateTestArgs args) {
+ std::vector<IPreparedModelParcel> preparedModelParcels;
+ preparedModelParcels.reserve(args.preparedModels.size());
+ for (const auto& model : args.preparedModels) {
+ preparedModelParcels.push_back({.preparedModel = model});
+ }
+ DeviceBuffer buffer;
+ const auto ret =
+ kDevice->allocate({.dimensions = std::move(args.dimensions)}, preparedModelParcels,
+ args.inputRoles, args.outputRoles, &buffer);
+
+ ASSERT_EQ(ret.getExceptionCode(), EX_SERVICE_SPECIFIC);
+ ASSERT_TRUE(static_cast<ErrorStatus>(ret.getServiceSpecificError()) ==
+ ErrorStatus::INVALID_ARGUMENT ||
+ static_cast<ErrorStatus>(ret.getServiceSpecificError()) ==
+ ErrorStatus::GENERAL_FAILURE);
+ }
+
+ void testConflictOperands(const std::shared_ptr<IPreparedModel>& model1,
+ const std::shared_ptr<IPreparedModel>& model2) {
+ validateAllocate({
+ .preparedModels = {model1, model2},
+ .inputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f},
+ {.modelIndex = 1, .ioIndex = 0, .frequency = 1.0f}},
+ });
+ validateAllocate({
+ .preparedModels = {model1, model2},
+ .inputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+ .outputRoles = {{.modelIndex = 1, .ioIndex = 0, .frequency = 1.0f}},
+ });
+ validateAllocate({
+ .preparedModels = {model1, model2},
+ .outputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f},
+ {.modelIndex = 1, .ioIndex = 0, .frequency = 1.0f}},
+ });
+ }
+};
+
+TEST_P(MemoryDomainAllocateTest, EmptyRole) {
+ // Test with empty prepared models and roles.
+ validateAllocate({});
+
+ auto preparedModel = createConvPreparedModel(kTestOperand);
+ if (preparedModel == nullptr) return;
+
+ // Test again with non-empty prepared models but empty roles.
+ validateAllocate({
+ .preparedModels = {preparedModel},
+ });
+}
+
+TEST_P(MemoryDomainAllocateTest, NullptrPreparedModel) {
+ // Test with nullptr prepared model as input role.
+ validateAllocate({
+ .preparedModels = {nullptr},
+ .inputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+ });
+
+ // Test with nullptr prepared model as output role.
+ validateAllocate({
+ .preparedModels = {nullptr},
+ .outputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+ });
+}
+
+TEST_P(MemoryDomainAllocateTest, InvalidPreparedModel) {
+ std::shared_ptr<InvalidPreparedModel> invalidPreparedModel =
+ ndk::SharedRefBase::make<InvalidPreparedModel>();
+
+ // Test with invalid prepared model as input role.
+ validateAllocate({
+ .preparedModels = {invalidPreparedModel},
+ .inputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+ });
+
+ // Test with invalid prepared model as output role.
+ validateAllocate({
+ .preparedModels = {invalidPreparedModel},
+ .outputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+ });
+}
+
+TEST_P(MemoryDomainAllocateTest, InvalidModelIndex) {
+ auto preparedModel = createConvPreparedModel(kTestOperand);
+ if (preparedModel == nullptr) return;
+
+ // This should fail, because the model index is out of bound.
+ validateAllocate({
+ .preparedModels = {preparedModel},
+ .inputRoles = {{.modelIndex = 1, .ioIndex = 0, .frequency = 1.0f}},
+ });
+
+ // This should fail, because the model index is out of bound.
+ validateAllocate({
+ .preparedModels = {preparedModel},
+ .outputRoles = {{.modelIndex = 1, .ioIndex = 0, .frequency = 1.0f}},
+ });
+}
+
+TEST_P(MemoryDomainAllocateTest, InvalidIOIndex) {
+ auto preparedModel = createConvPreparedModel(kTestOperand);
+ if (preparedModel == nullptr) return;
+
+ // This should fail, because the model only has one input.
+ validateAllocate({
+ .preparedModels = {preparedModel},
+ .inputRoles = {{.modelIndex = 0, .ioIndex = 1, .frequency = 1.0f}},
+ });
+
+ // This should fail, because the model only has one output.
+ validateAllocate({
+ .preparedModels = {preparedModel},
+ .outputRoles = {{.modelIndex = 0, .ioIndex = 1, .frequency = 1.0f}},
+ });
+}
+
+TEST_P(MemoryDomainAllocateTest, InvalidFrequency) {
+ auto preparedModel = createConvPreparedModel(kTestOperand);
+ if (preparedModel == nullptr) return;
+
+ for (float invalidFreq : {10.0f, 0.0f, -0.5f}) {
+ // Test with invalid frequency for input roles.
+ validateAllocate({
+ .preparedModels = {preparedModel},
+ .inputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = invalidFreq}},
+ });
+ // Test with invalid frequency for output roles.
+ validateAllocate({
+ .preparedModels = {preparedModel},
+ .outputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = invalidFreq}},
+ });
+ }
+}
+
+TEST_P(MemoryDomainAllocateTest, SameRoleSpecifiedTwice) {
+ auto preparedModel = createConvPreparedModel(kTestOperand);
+ if (preparedModel == nullptr) return;
+
+ // Same role with same model index.
+ validateAllocate({
+ .preparedModels = {preparedModel},
+ .inputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f},
+ {.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+ });
+ validateAllocate({
+ .preparedModels = {preparedModel},
+ .outputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f},
+ {.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+ });
+
+ // Different model indexes, but logically referring to the same role.
+ validateAllocate({
+ .preparedModels = {preparedModel, preparedModel},
+ .inputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f},
+ {.modelIndex = 1, .ioIndex = 0, .frequency = 1.0f}},
+ });
+ validateAllocate({
+ .preparedModels = {preparedModel, preparedModel},
+ .outputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f},
+ {.modelIndex = 1, .ioIndex = 0, .frequency = 1.0f}},
+ });
+}
+
+TEST_P(MemoryDomainAllocateTest, ConflictOperandType) {
+ const std::map<TestOperandType, TestOperandType> conflictTypeMap = {
+ {TestOperandType::TENSOR_FLOAT32, TestOperandType::TENSOR_FLOAT16},
+ {TestOperandType::TENSOR_FLOAT16, TestOperandType::TENSOR_FLOAT32},
+ {TestOperandType::TENSOR_QUANT8_ASYMM, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ {TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED, TestOperandType::TENSOR_QUANT8_ASYMM},
+ };
+
+ TestOperand conflictTestOperand = kTestOperand;
+ const auto it = conflictTypeMap.find(kTestOperandType);
+ ASSERT_FALSE(it == conflictTypeMap.end());
+ conflictTestOperand.type = it->second;
+
+ auto preparedModel = createConvPreparedModel(kTestOperand);
+ auto conflictPreparedModel = createConvPreparedModel(conflictTestOperand);
+ if (preparedModel == nullptr || conflictPreparedModel == nullptr) return;
+ testConflictOperands(preparedModel, conflictPreparedModel);
+}
+
+TEST_P(MemoryDomainAllocateTest, ConflictScale) {
+ if (isFloat(kTestOperandType)) return;
+
+ TestOperand conflictTestOperand = kTestOperand;
+ ASSERT_NE(conflictTestOperand.scale, 1.0f);
+ conflictTestOperand.scale = 1.0f;
+
+ auto preparedModel = createConvPreparedModel(kTestOperand);
+ auto conflictPreparedModel = createConvPreparedModel(conflictTestOperand);
+ if (preparedModel == nullptr || conflictPreparedModel == nullptr) return;
+ testConflictOperands(preparedModel, conflictPreparedModel);
+}
+
+TEST_P(MemoryDomainAllocateTest, ConflictZeroPoint) {
+ if (isFloat(kTestOperandType)) return;
+
+ TestOperand conflictTestOperand = kTestOperand;
+ ASSERT_NE(conflictTestOperand.zeroPoint, 10);
+ conflictTestOperand.zeroPoint = 10;
+
+ auto preparedModel = createConvPreparedModel(kTestOperand);
+ auto conflictPreparedModel = createConvPreparedModel(conflictTestOperand);
+ if (preparedModel == nullptr || conflictPreparedModel == nullptr) return;
+ testConflictOperands(preparedModel, conflictPreparedModel);
+}
+
+TEST_P(MemoryDomainAllocateTest, ConflictRankBetweenRoles) {
+ TestOperand conflictTestOperand = kTestOperand;
+ conflictTestOperand.dimensions.pop_back();
+
+ auto preparedModel = createAddPreparedModel(kTestOperand);
+ auto conflictPreparedModel = createAddPreparedModel(conflictTestOperand);
+ if (preparedModel == nullptr || conflictPreparedModel == nullptr) return;
+ testConflictOperands(preparedModel, conflictPreparedModel);
+}
+
+TEST_P(MemoryDomainAllocateTest, ConflictDimensionsBetweenRoles) {
+ TestOperand conflictTestOperand = kTestOperand;
+ conflictTestOperand.dimensions[0] = 4;
+
+ auto preparedModel = createConvPreparedModel(kTestOperand);
+ auto conflictPreparedModel = createConvPreparedModel(conflictTestOperand);
+ if (preparedModel == nullptr || conflictPreparedModel == nullptr) return;
+ testConflictOperands(preparedModel, conflictPreparedModel);
+}
+
+TEST_P(MemoryDomainAllocateTest, ConflictRankBetweenRoleAndDesc) {
+ auto preparedModel = createConvPreparedModel(kTestOperand);
+ if (preparedModel == nullptr) return;
+
+ auto badDimensions = utils::toSigned(kTestOperand.dimensions).value();
+ badDimensions.pop_back();
+
+ validateAllocate({
+ .dimensions = badDimensions,
+ .preparedModels = {preparedModel},
+ .inputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+ });
+ validateAllocate({
+ .dimensions = badDimensions,
+ .preparedModels = {preparedModel},
+ .outputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+ });
+}
+
+TEST_P(MemoryDomainAllocateTest, ConflictDimensionsBetweenRoleAndDesc) {
+ auto preparedModel = createConvPreparedModel(kTestOperand);
+ if (preparedModel == nullptr) return;
+
+ auto badDimensions = utils::toSigned(kTestOperand.dimensions).value();
+ badDimensions[0] = 4;
+
+ validateAllocate({
+ .dimensions = badDimensions,
+ .preparedModels = {preparedModel},
+ .inputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+ });
+ validateAllocate({
+ .dimensions = badDimensions,
+ .preparedModels = {preparedModel},
+ .outputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+ });
+}
+
+TEST_P(MemoryDomainAllocateTest, ConflictRankWithScalarRole) {
+ auto preparedModel = createAddPreparedModel(kTestOperand);
+ if (preparedModel == nullptr) return;
+
+ // This should fail, because the target operand is a scalar but a non-empty dimension is
+ // specified.
+ validateAllocate({
+ .dimensions = {1},
+ .preparedModels = {preparedModel},
+ .inputRoles = {{.modelIndex = 0, .ioIndex = 2, .frequency = 1.0f}},
+ });
+}
+
+std::string printMemoryDomainAllocateTest(
+ const testing::TestParamInfo<MemoryDomainAllocateTestParam>& info) {
+ const auto& [namedDevice, operandType] = info.param;
+ const std::string type = toString(static_cast<OperandType>(operandType));
+ return gtestCompliantName(getName(namedDevice) + "_" + type);
+}
+
+GTEST_ALLOW_UNINSTANTIATED_PARAMETERIZED_TEST(MemoryDomainAllocateTest);
+INSTANTIATE_TEST_SUITE_P(TestMemoryDomain, MemoryDomainAllocateTest,
+ testing::Combine(testing::ValuesIn(getNamedDevices()),
+ kTestOperandTypeChoices),
+ printMemoryDomainAllocateTest);
+
+class MemoryDomainCopyTestBase : public MemoryDomainTestBase {
+ protected:
+ MemoryDomainCopyTestBase(std::shared_ptr<IDevice> device, TestOperandType type)
+ : MemoryDomainTestBase(std::move(device), type) {}
+
+ // Allocates device memory for roles of a single prepared model.
+ // Returns {IBuffer, token} if success; returns {nullptr, 0} if not supported.
+ DeviceBuffer allocateBuffer(const std::shared_ptr<IPreparedModel>& preparedModel,
+ const std::vector<int32_t>& inputIndexes,
+ const std::vector<int32_t>& outputIndexes,
+ const std::vector<int32_t>& dimensions) {
+ if (preparedModel == nullptr) {
+ return {.buffer = nullptr, .token = 0};
+ }
+
+ std::vector<BufferRole> inputRoles(inputIndexes.size()), outputRoles(outputIndexes.size());
+ auto trans = [](int32_t ind) -> BufferRole {
+ return {.modelIndex = 0, .ioIndex = ind, .frequency = 1.0f};
+ };
+ std::transform(inputIndexes.begin(), inputIndexes.end(), inputRoles.begin(), trans);
+ std::transform(outputIndexes.begin(), outputIndexes.end(), outputRoles.begin(), trans);
+
+ IPreparedModelParcel parcel;
+ parcel.preparedModel = preparedModel;
+
+ DeviceBuffer buffer;
+
+ const auto ret = kDevice->allocate({.dimensions = dimensions}, {parcel}, inputRoles,
+ outputRoles, &buffer);
+
+ if (!ret.isOk()) {
+ EXPECT_EQ(ret.getExceptionCode(), EX_SERVICE_SPECIFIC);
+ EXPECT_EQ(static_cast<ErrorStatus>(ret.getServiceSpecificError()),
+ ErrorStatus::GENERAL_FAILURE);
+ return DeviceBuffer{
+ .buffer = nullptr,
+ .token = 0,
+ };
+ }
+
+ EXPECT_NE(buffer.buffer, nullptr);
+ EXPECT_GT(buffer.token, 0);
+
+ return buffer;
+ }
+
+ DeviceBuffer allocateBuffer(const std::shared_ptr<IPreparedModel>& preparedModel,
+ const std::vector<int32_t>& inputIndexes,
+ const std::vector<int32_t>& outputIndexes) {
+ return allocateBuffer(preparedModel, inputIndexes, outputIndexes, {});
+ }
+
+ Memory allocateSharedMemory(uint32_t size) {
+ const auto sharedMemory = nn::createSharedMemory(size).value();
+ auto memory = utils::convert(sharedMemory).value();
+ EXPECT_EQ(memory.size, size);
+ return memory;
+ }
+
+ void testCopyFrom(const std::shared_ptr<IBuffer>& buffer, const Memory& memory,
+ const std::vector<int32_t>& dimensions, ErrorStatus expectedStatus) {
+ const auto ret = buffer->copyFrom(memory, dimensions);
+ if (expectedStatus == ErrorStatus::NONE) {
+ ASSERT_TRUE(ret.isOk());
+ } else {
+ ASSERT_EQ(ret.getExceptionCode(), EX_SERVICE_SPECIFIC);
+ ASSERT_EQ(expectedStatus, static_cast<ErrorStatus>(ret.getServiceSpecificError()));
+ }
+ }
+
+ void testCopyTo(const std::shared_ptr<IBuffer>& buffer, const Memory& memory,
+ ErrorStatus expectedStatus) {
+ const auto ret = buffer->copyTo(memory);
+ if (expectedStatus == ErrorStatus::NONE) {
+ ASSERT_TRUE(ret.isOk());
+ } else {
+ ASSERT_EQ(ret.getExceptionCode(), EX_SERVICE_SPECIFIC);
+ ASSERT_EQ(expectedStatus, static_cast<ErrorStatus>(ret.getServiceSpecificError()));
+ }
+ }
+
+ void initializeDeviceMemory(const std::shared_ptr<IBuffer>& buffer) {
+ Memory memory = allocateSharedMemory(kTestOperandDataSize);
+ ASSERT_EQ(memory.size, kTestOperandDataSize);
+ testCopyFrom(buffer, memory, utils::toSigned(kTestOperand.dimensions).value(),
+ ErrorStatus::NONE);
+ }
+};
+
+using MemoryDomainCopyTestParam = std::tuple<NamedDevice, TestOperandType>;
+class MemoryDomainCopyTest : public MemoryDomainCopyTestBase,
+ public testing::WithParamInterface<MemoryDomainCopyTestParam> {
+ protected:
+ MemoryDomainCopyTest()
+ : MemoryDomainCopyTestBase(getData(std::get<NamedDevice>(GetParam())),
+ std::get<TestOperandType>(GetParam())) {}
+};
+
+TEST_P(MemoryDomainCopyTest, CopyFrom_InvalidMemorySize) {
+ auto preparedModel = createConvPreparedModel(kTestOperand);
+ auto [buffer, token] = allocateBuffer(preparedModel, {0}, {0});
+ if (buffer == nullptr) return;
+
+ uint32_t badMemorySize1 = kTestOperandDataSize / 2, badMemorySize2 = kTestOperandDataSize * 2;
+ Memory badMemory1 = allocateSharedMemory(badMemorySize1);
+ Memory badMemory2 = allocateSharedMemory(badMemorySize2);
+ testCopyFrom(buffer, badMemory1, {}, ErrorStatus::INVALID_ARGUMENT);
+ testCopyFrom(buffer, badMemory2, {}, ErrorStatus::INVALID_ARGUMENT);
+}
+
+TEST_P(MemoryDomainCopyTest, CopyFrom_InvalidMemorySize_DynamicShape) {
+ TestOperand testOperand = kTestOperand;
+ testOperand.dimensions[0] = 0;
+ auto preparedModel = createConvPreparedModel(testOperand);
+ auto [buffer, token] = allocateBuffer(preparedModel, {0}, {0});
+ if (buffer == nullptr) return;
+
+ uint32_t badMemorySize1 = kTestOperandDataSize / 2, badMemorySize2 = kTestOperandDataSize * 2;
+ Memory badMemory1 = allocateSharedMemory(badMemorySize1);
+ Memory badMemory2 = allocateSharedMemory(badMemorySize2);
+ Memory goodMemory = allocateSharedMemory(kTestOperandDataSize);
+
+ const auto goodDimensions = utils::toSigned(kTestOperand.dimensions).value();
+ auto badDimensions = goodDimensions;
+ badDimensions[0] = 2;
+
+ testCopyFrom(buffer, badMemory1, goodDimensions, ErrorStatus::INVALID_ARGUMENT);
+ testCopyFrom(buffer, badMemory2, goodDimensions, ErrorStatus::INVALID_ARGUMENT);
+ testCopyFrom(buffer, goodMemory, goodDimensions, ErrorStatus::NONE);
+ testCopyFrom(buffer, goodMemory, badDimensions, ErrorStatus::INVALID_ARGUMENT);
+}
+
+TEST_P(MemoryDomainCopyTest, CopyFrom_InvalidDimensions) {
+ auto preparedModel = createConvPreparedModel(kTestOperand);
+ auto [buffer, token] = allocateBuffer(preparedModel, {0}, {0});
+ if (buffer == nullptr) return;
+
+ Memory memory = allocateSharedMemory(kTestOperandDataSize);
+
+ const auto goodDimensions = utils::toSigned(kTestOperand.dimensions).value();
+ std::vector<int32_t> badDimensions = goodDimensions;
+ badDimensions.pop_back();
+ testCopyFrom(buffer, memory, badDimensions, ErrorStatus::INVALID_ARGUMENT);
+
+ badDimensions = goodDimensions;
+ badDimensions[0] = 2;
+ testCopyFrom(buffer, memory, badDimensions, ErrorStatus::INVALID_ARGUMENT);
+
+ badDimensions = goodDimensions;
+ badDimensions[0] = 0;
+ testCopyFrom(buffer, memory, badDimensions, ErrorStatus::INVALID_ARGUMENT);
+
+ testCopyFrom(buffer, memory, {}, ErrorStatus::NONE);
+ testCopyFrom(buffer, memory, goodDimensions, ErrorStatus::NONE);
+}
+
+TEST_P(MemoryDomainCopyTest, CopyFrom_InvalidDimensions_DynamicShape) {
+ TestOperand testOperand = kTestOperand;
+ testOperand.dimensions[0] = 0;
+ auto preparedModel = createConvPreparedModel(testOperand);
+ auto [buffer, token] = allocateBuffer(preparedModel, {0}, {0});
+ if (buffer == nullptr) return;
+
+ Memory memory = allocateSharedMemory(kTestOperandDataSize);
+
+ const auto goodDimensions = utils::toSigned(kTestOperand.dimensions).value();
+ std::vector<int32_t> badDimensions = goodDimensions;
+ badDimensions.pop_back();
+ testCopyFrom(buffer, memory, badDimensions, ErrorStatus::INVALID_ARGUMENT);
+
+ badDimensions = goodDimensions;
+ badDimensions[0] = 2;
+ badDimensions[3] = 4;
+ testCopyFrom(buffer, memory, badDimensions, ErrorStatus::INVALID_ARGUMENT);
+
+ badDimensions = goodDimensions;
+ badDimensions[0] = 1;
+ badDimensions[3] = 0;
+ testCopyFrom(buffer, memory, badDimensions, ErrorStatus::INVALID_ARGUMENT);
+
+ testCopyFrom(buffer, memory, {}, ErrorStatus::INVALID_ARGUMENT);
+ testCopyFrom(buffer, memory, goodDimensions, ErrorStatus::NONE);
+}
+
+TEST_P(MemoryDomainCopyTest, CopyTo_UninitializedMemory) {
+ auto preparedModel = createConvPreparedModel(kTestOperand);
+ auto [buffer, token] = allocateBuffer(preparedModel, {0}, {0});
+ if (buffer == nullptr) return;
+
+ Memory memory = allocateSharedMemory(kTestOperandDataSize);
+ testCopyTo(buffer, memory, ErrorStatus::GENERAL_FAILURE);
+}
+
+TEST_P(MemoryDomainCopyTest, CopyTo_InvalidMemorySize) {
+ auto preparedModel = createConvPreparedModel(kTestOperand);
+ auto [buffer, token] = allocateBuffer(preparedModel, {0}, {0});
+ if (buffer == nullptr) return;
+
+ uint32_t badMemorySize1 = kTestOperandDataSize / 2, badMemorySize2 = kTestOperandDataSize * 2;
+ Memory badMemory1 = allocateSharedMemory(badMemorySize1);
+ Memory badMemory2 = allocateSharedMemory(badMemorySize2);
+ Memory goodMemory = allocateSharedMemory(kTestOperandDataSize);
+
+ initializeDeviceMemory(buffer);
+ testCopyTo(buffer, badMemory1, ErrorStatus::INVALID_ARGUMENT);
+ testCopyTo(buffer, badMemory2, ErrorStatus::INVALID_ARGUMENT);
+ testCopyTo(buffer, goodMemory, ErrorStatus::NONE);
+}
+
+TEST_P(MemoryDomainCopyTest, CopyTo_InvalidMemorySize_DynamicShape) {
+ TestOperand testOperand = kTestOperand;
+ testOperand.dimensions[0] = 0;
+ auto preparedModel = createConvPreparedModel(testOperand);
+ auto [buffer, token] = allocateBuffer(preparedModel, {0}, {0});
+ if (buffer == nullptr) return;
+
+ uint32_t badMemorySize1 = kTestOperandDataSize / 2, badMemorySize2 = kTestOperandDataSize * 2;
+ Memory badMemory1 = allocateSharedMemory(badMemorySize1);
+ Memory badMemory2 = allocateSharedMemory(badMemorySize2);
+ Memory goodMemory = allocateSharedMemory(kTestOperandDataSize);
+
+ initializeDeviceMemory(buffer);
+ testCopyTo(buffer, badMemory1, ErrorStatus::INVALID_ARGUMENT);
+ testCopyTo(buffer, badMemory2, ErrorStatus::INVALID_ARGUMENT);
+ testCopyTo(buffer, goodMemory, ErrorStatus::NONE);
+}
+
+std::string printMemoryDomainCopyTest(
+ const testing::TestParamInfo<MemoryDomainCopyTestParam>& info) {
+ const auto& [namedDevice, operandType] = info.param;
+ const std::string type = toString(static_cast<OperandType>(operandType));
+ return gtestCompliantName(getName(namedDevice) + "_" + type);
+}
+
+GTEST_ALLOW_UNINSTANTIATED_PARAMETERIZED_TEST(MemoryDomainCopyTest);
+INSTANTIATE_TEST_SUITE_P(TestMemoryDomain, MemoryDomainCopyTest,
+ testing::Combine(testing::ValuesIn(getNamedDevices()),
+ kTestOperandTypeChoices),
+ printMemoryDomainCopyTest);
+
+using MemoryDomainExecutionTestParam = std::tuple<NamedDevice, TestOperandType, Executor>;
+class MemoryDomainExecutionTest
+ : public MemoryDomainCopyTestBase,
+ public testing::WithParamInterface<MemoryDomainExecutionTestParam> {
+ protected:
+ MemoryDomainExecutionTest()
+ : MemoryDomainCopyTestBase(getData(std::get<NamedDevice>(GetParam())),
+ std::get<TestOperandType>(GetParam())) {}
+
+ RequestMemoryPool createSharedMemoryPool(uint32_t size) {
+ return RequestMemoryPool(allocateSharedMemory(size));
+ }
+
+ RequestMemoryPool createDeviceMemoryPool(uint32_t token) {
+ return RequestMemoryPool(static_cast<int32_t>(token));
+ }
+
+ void testExecution(const std::shared_ptr<IPreparedModel>& preparedModel, const Request& request,
+ ErrorStatus expectedStatus) {
+ switch (kExecutor) {
+ case Executor::SYNC:
+ EXPECT_EQ(executeSync(preparedModel, request), expectedStatus);
+ break;
+ case Executor::FENCED:
+ EXPECT_EQ(executeFenced(preparedModel, request), expectedStatus);
+ break;
+ default:
+ ASSERT_TRUE(false);
+ }
+ }
+
+ ErrorStatus executeSync(const std::shared_ptr<IPreparedModel>& preparedModel,
+ const Request& request) {
+ ExecutionResult executionResult;
+ const auto ret = preparedModel->executeSynchronously(
+ request, false, kNoDeadline, kOmittedTimeoutDuration, &executionResult);
+
+ if (!ret.isOk()) {
+ EXPECT_EQ(ret.getExceptionCode(), EX_SERVICE_SPECIFIC);
+ return static_cast<ErrorStatus>(ret.getServiceSpecificError());
+ }
+ const ErrorStatus executionStatus = executionResult.outputSufficientSize
+ ? ErrorStatus::NONE
+ : ErrorStatus::OUTPUT_INSUFFICIENT_SIZE;
+ EXPECT_EQ(executionResult.timing, kNoTiming);
+ return executionStatus;
+ }
+
+ ErrorStatus executeFenced(const std::shared_ptr<IPreparedModel>& preparedModel,
+ const Request& request) {
+ ndk::ScopedFileDescriptor syncFence;
+ std::shared_ptr<IFencedExecutionCallback> fencedCallback;
+ const auto ret = preparedModel->executeFenced(request, {}, false, kNoDeadline,
+ kOmittedTimeoutDuration, kNoDuration,
+ &syncFence, &fencedCallback);
+ if (!ret.isOk()) {
+ EXPECT_EQ(ret.getExceptionCode(), EX_SERVICE_SPECIFIC);
+ return static_cast<ErrorStatus>(ret.getServiceSpecificError());
+ }
+ if (syncFence.get() != -1) {
+ waitForSyncFence(syncFence.get());
+ }
+ EXPECT_NE(fencedCallback, nullptr);
+
+ ErrorStatus executionStatus = ErrorStatus::GENERAL_FAILURE;
+ Timing time = kNoTiming;
+ Timing timeFenced = kNoTiming;
+ const auto retExecutionInfo =
+ fencedCallback->getExecutionInfo(&time, &timeFenced, &executionStatus);
+ EXPECT_TRUE(retExecutionInfo.isOk());
+ EXPECT_EQ(time, kNoTiming);
+ return executionStatus;
+ }
+
+ const Executor kExecutor = std::get<Executor>(GetParam());
+};
+
+TEST_P(MemoryDomainExecutionTest, InvalidToken) {
+ auto preparedModel = createConvPreparedModel(kTestOperand);
+ if (preparedModel == nullptr) return;
+
+ RequestMemoryPool sharedMemory = createSharedMemoryPool(kTestOperandDataSize);
+ RequestMemoryPool badDeviceMemory1 = createDeviceMemoryPool(0); // Invalid token.
+ RequestMemoryPool badDeviceMemory2 = createDeviceMemoryPool(100); // Unknown token.
+ RequestArgument sharedMemoryArg = {
+ .location = {.poolIndex = 0, .offset = 0, .length = kTestOperandDataSize}};
+ RequestArgument deviceMemoryArg = {.location = {.poolIndex = 1}};
+
+ testExecution(preparedModel,
+ {.inputs = {deviceMemoryArg},
+ .outputs = {sharedMemoryArg},
+ .pools = createRequestMemoryPools(sharedMemory, badDeviceMemory1)},
+ ErrorStatus::INVALID_ARGUMENT);
+ testExecution(preparedModel,
+ {.inputs = {deviceMemoryArg},
+ .outputs = {sharedMemoryArg},
+ .pools = createRequestMemoryPools(sharedMemory, badDeviceMemory2)},
+ ErrorStatus::INVALID_ARGUMENT);
+ testExecution(preparedModel,
+ {.inputs = {sharedMemoryArg},
+ .outputs = {deviceMemoryArg},
+ .pools = createRequestMemoryPools(sharedMemory, badDeviceMemory1)},
+ ErrorStatus::INVALID_ARGUMENT);
+ testExecution(preparedModel,
+ {.inputs = {sharedMemoryArg},
+ .outputs = {deviceMemoryArg},
+ .pools = createRequestMemoryPools(sharedMemory, badDeviceMemory2)},
+ ErrorStatus::INVALID_ARGUMENT);
+}
+
+TEST_P(MemoryDomainExecutionTest, InvalidPreparedModel) {
+ auto preparedModel = createConvPreparedModel(kTestOperand);
+ auto [buffer, token] = allocateBuffer(preparedModel, {0}, {0});
+ if (buffer == nullptr) return;
+ auto badPreparedModel = createConvPreparedModel(kTestOperand);
+ if (badPreparedModel == nullptr) return;
+
+ RequestMemoryPool sharedMemory = createSharedMemoryPool(kTestOperandDataSize);
+ RequestMemoryPool deviceMemory = createDeviceMemoryPool(token);
+ RequestArgument sharedMemoryArg = {
+ .location = {.poolIndex = 0, .offset = 0, .length = kTestOperandDataSize}};
+ RequestArgument deviceMemoryArg = {.location = {.poolIndex = 1}};
+
+ // This should fail, because the buffer is not allocated for badPreparedModel.
+ initializeDeviceMemory(buffer);
+ testExecution(badPreparedModel,
+ {.inputs = {deviceMemoryArg},
+ .outputs = {sharedMemoryArg},
+ .pools = createRequestMemoryPools(sharedMemory, deviceMemory)},
+ ErrorStatus::INVALID_ARGUMENT);
+ testExecution(badPreparedModel,
+ {.inputs = {sharedMemoryArg},
+ .outputs = {deviceMemoryArg},
+ .pools = createRequestMemoryPools(sharedMemory, deviceMemory)},
+ ErrorStatus::INVALID_ARGUMENT);
+}
+
+TEST_P(MemoryDomainExecutionTest, InvalidIOIndex) {
+ auto preparedModel = createConvPreparedModel(kTestOperand, 2);
+ auto [buffer, token] = allocateBuffer(preparedModel, {0}, {});
+ if (buffer == nullptr) return;
+
+ RequestMemoryPool sharedMemory1 = createSharedMemoryPool(kTestOperandDataSize);
+ RequestMemoryPool sharedMemory2 = createSharedMemoryPool(kTestOperandDataSize);
+ RequestMemoryPool sharedMemory3 = createSharedMemoryPool(kTestOperandDataSize);
+ RequestMemoryPool deviceMemory = createDeviceMemoryPool(token);
+ RequestArgument sharedMemoryArg1 = {
+ .location = {.poolIndex = 0, .offset = 0, .length = kTestOperandDataSize}};
+ RequestArgument sharedMemoryArg2 = {
+ .location = {.poolIndex = 1, .offset = 0, .length = kTestOperandDataSize}};
+ RequestArgument sharedMemoryArg3 = {
+ .location = {.poolIndex = 2, .offset = 0, .length = kTestOperandDataSize}};
+ RequestArgument deviceMemoryArg = {.location = {.poolIndex = 3}};
+
+ // This should fail, because the device memory is not allocated for input 1.
+ initializeDeviceMemory(buffer);
+ testExecution(preparedModel,
+ {.inputs = {sharedMemoryArg1, deviceMemoryArg},
+ .outputs = {sharedMemoryArg2, sharedMemoryArg3},
+ .pools = createRequestMemoryPools(sharedMemory1, sharedMemory2, sharedMemory3,
+ deviceMemory)},
+ ErrorStatus::INVALID_ARGUMENT);
+
+ // This should fail, because the device memory is not allocated for output 1.
+ testExecution(preparedModel,
+ {.inputs = {sharedMemoryArg1, sharedMemoryArg2},
+ .outputs = {sharedMemoryArg3, deviceMemoryArg},
+ .pools = createRequestMemoryPools(sharedMemory1, sharedMemory2, sharedMemory3,
+ deviceMemory)},
+ ErrorStatus::INVALID_ARGUMENT);
+}
+
+TEST_P(MemoryDomainExecutionTest, InvalidIOType) {
+ auto preparedModel = createConvPreparedModel(kTestOperand);
+ auto [inputBuffer, inputToken] = allocateBuffer(preparedModel, {0}, {});
+ auto [outputBuffer, outputToken] = allocateBuffer(preparedModel, {}, {0});
+ if (inputBuffer == nullptr || outputBuffer == nullptr) return;
+
+ RequestMemoryPool sharedMemory = createSharedMemoryPool(kTestOperandDataSize);
+ RequestMemoryPool deviceMemory = createDeviceMemoryPool(inputToken);
+ RequestArgument sharedMemoryArg = {
+ .location = {.poolIndex = 0, .offset = 0, .length = kTestOperandDataSize}};
+ RequestArgument deviceMemoryArg = {.location = {.poolIndex = 1}};
+
+ // This should fail, because the device memory is allocated for input but used as output.
+ testExecution(preparedModel,
+ {.inputs = {sharedMemoryArg},
+ .outputs = {deviceMemoryArg},
+ .pools = createRequestMemoryPools(sharedMemory, deviceMemory)},
+ ErrorStatus::INVALID_ARGUMENT);
+
+ // This should fail, because the device memory is allocated for output but used as input.
+ deviceMemory.set<RequestMemoryPool::Tag::token>(outputToken);
+ initializeDeviceMemory(outputBuffer);
+ testExecution(preparedModel,
+ {.inputs = {deviceMemoryArg},
+ .outputs = {sharedMemoryArg},
+ .pools = createRequestMemoryPools(sharedMemory, deviceMemory)},
+ ErrorStatus::INVALID_ARGUMENT);
+}
+
+TEST_P(MemoryDomainExecutionTest, UninitializedMemory) {
+ auto preparedModel = createConvPreparedModel(kTestOperand);
+ auto [buffer, token] = allocateBuffer(preparedModel, {0}, {0});
+ if (buffer == nullptr) return;
+
+ RequestMemoryPool sharedMemory = createSharedMemoryPool(kTestOperandDataSize);
+ RequestMemoryPool deviceMemory = createDeviceMemoryPool(token);
+ RequestArgument sharedMemoryArg = {
+ .location = {.poolIndex = 0, .offset = 0, .length = kTestOperandDataSize}};
+ RequestArgument deviceMemoryArg = {.location = {.poolIndex = 1}};
+
+ // This should fail, because the device memory is not initialized.
+ testExecution(preparedModel,
+ {.inputs = {deviceMemoryArg},
+ .outputs = {sharedMemoryArg},
+ .pools = createRequestMemoryPools(sharedMemory, deviceMemory)},
+ ErrorStatus::GENERAL_FAILURE);
+
+ // This should initialize the device memory.
+ testExecution(preparedModel,
+ {.inputs = {sharedMemoryArg},
+ .outputs = {deviceMemoryArg},
+ .pools = createRequestMemoryPools(sharedMemory, deviceMemory)},
+ ErrorStatus::NONE);
+
+ // Test again with initialized device memory.
+ testExecution(preparedModel,
+ {.inputs = {deviceMemoryArg},
+ .outputs = {sharedMemoryArg},
+ .pools = createRequestMemoryPools(sharedMemory, deviceMemory)},
+ ErrorStatus::NONE);
+}
+
+TEST_P(MemoryDomainExecutionTest, SameRequestMultipleRoles) {
+ auto preparedModel = createConvPreparedModel(kTestOperand, 2);
+ auto [buffer, token] = allocateBuffer(preparedModel, {0, 1}, {0, 1});
+ if (buffer == nullptr) return;
+
+ RequestMemoryPool sharedMemory1 = createSharedMemoryPool(kTestOperandDataSize);
+ RequestMemoryPool sharedMemory2 = createSharedMemoryPool(kTestOperandDataSize);
+ RequestMemoryPool deviceMemory = createDeviceMemoryPool(token);
+ RequestArgument sharedMemoryArg1 = {
+ .location = {.poolIndex = 0, .offset = 0, .length = kTestOperandDataSize}};
+ RequestArgument sharedMemoryArg2 = {
+ .location = {.poolIndex = 1, .offset = 0, .length = kTestOperandDataSize}};
+ RequestArgument deviceMemoryArg = {.location = {.poolIndex = 2}};
+
+ // This should fail, because the same device memory cannot be used for both input and output.
+ initializeDeviceMemory(buffer);
+ testExecution(preparedModel,
+ {.inputs = {deviceMemoryArg, sharedMemoryArg1},
+ .outputs = {deviceMemoryArg, sharedMemoryArg2},
+ .pools = createRequestMemoryPools(sharedMemory1, sharedMemory2, deviceMemory)},
+ ErrorStatus::INVALID_ARGUMENT);
+
+ // This should fail, because the same device memory cannot be used for multiple outputs.
+ testExecution(preparedModel,
+ {.inputs = {sharedMemoryArg1, sharedMemoryArg2},
+ .outputs = {deviceMemoryArg, deviceMemoryArg},
+ .pools = createRequestMemoryPools(sharedMemory1, sharedMemory2, deviceMemory)},
+ ErrorStatus::INVALID_ARGUMENT);
+
+ // The same device memory can be used for multiple inputs.
+ initializeDeviceMemory(buffer);
+ testExecution(preparedModel,
+ {.inputs = {deviceMemoryArg, deviceMemoryArg},
+ .outputs = {sharedMemoryArg1, sharedMemoryArg2},
+ .pools = createRequestMemoryPools(sharedMemory1, sharedMemory2, deviceMemory)},
+ ErrorStatus::NONE);
+}
+
+TEST_P(MemoryDomainExecutionTest, InvalidDimensions) {
+ // FENCED execution does not support dynamic shape.
+ if (kExecutor == Executor::FENCED) return;
+
+ TestOperand testOperand = kTestOperand;
+ testOperand.dimensions[0] = 0;
+ auto preparedModel = createConvPreparedModel(testOperand);
+ auto deviceBuffer = allocateBuffer(preparedModel, {0}, {0},
+ utils::toSigned(kTestOperand.dimensions).value());
+ if (deviceBuffer.buffer == nullptr) return;
+
+ RequestMemoryPool sharedMemory = createSharedMemoryPool(kTestOperandDataSize);
+ RequestMemoryPool deviceMemory = createDeviceMemoryPool(deviceBuffer.token);
+ auto badDimensions = utils::toSigned(kTestOperand.dimensions).value();
+ badDimensions[0] = 2;
+ RequestArgument sharedMemoryArg = {
+ .location = {.poolIndex = 0, .offset = 0, .length = kTestOperandDataSize},
+ .dimensions = badDimensions};
+ RequestArgument deviceMemoryArg = {.location = {.poolIndex = 1}};
+ RequestArgument deviceMemoryArgWithBadDimensions = {.location = {.poolIndex = 1},
+ .dimensions = badDimensions};
+
+ initializeDeviceMemory(deviceBuffer.buffer);
+ testExecution(preparedModel,
+ {.inputs = {deviceMemoryArgWithBadDimensions},
+ .outputs = {sharedMemoryArg},
+ .pools = createRequestMemoryPools(sharedMemory, deviceMemory)},
+ ErrorStatus::INVALID_ARGUMENT);
+
+ testExecution(preparedModel,
+ {.inputs = {sharedMemoryArg},
+ .outputs = {deviceMemoryArgWithBadDimensions},
+ .pools = createRequestMemoryPools(sharedMemory, deviceMemory)},
+ ErrorStatus::INVALID_ARGUMENT);
+
+ testExecution(preparedModel,
+ {.inputs = {sharedMemoryArg},
+ .outputs = {deviceMemoryArg},
+ .pools = createRequestMemoryPools(sharedMemory, deviceMemory)},
+ ErrorStatus::GENERAL_FAILURE);
+}
+
+const auto kExecutorChoices = testing::Values(Executor::SYNC, Executor::FENCED);
+
+std::string printMemoryDomainExecutionTest(
+ const testing::TestParamInfo<MemoryDomainExecutionTestParam>& info) {
+ const auto& [namedDevice, operandType, executor] = info.param;
+ const std::string type = toString(static_cast<OperandType>(operandType));
+ const std::string executorStr = toString(executor);
+ return gtestCompliantName(getName(namedDevice) + "_" + type + "_" + executorStr);
+}
+
+GTEST_ALLOW_UNINSTANTIATED_PARAMETERIZED_TEST(MemoryDomainExecutionTest);
+INSTANTIATE_TEST_SUITE_P(TestMemoryDomain, MemoryDomainExecutionTest,
+ testing::Combine(testing::ValuesIn(getNamedDevices()),
+ kTestOperandTypeChoices, kExecutorChoices),
+ printMemoryDomainExecutionTest);
+
+} // namespace aidl::android::hardware::neuralnetworks::vts::functional
diff --git a/neuralnetworks/aidl/vts/functional/QualityOfServiceTests.cpp b/neuralnetworks/aidl/vts/functional/QualityOfServiceTests.cpp
new file mode 100644
index 0000000..58db98f
--- /dev/null
+++ b/neuralnetworks/aidl/vts/functional/QualityOfServiceTests.cpp
@@ -0,0 +1,270 @@
+/*
+ * Copyright (C) 2021 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/binder_enums.h>
+#include <android/binder_interface_utils.h>
+#include <android/binder_status.h>
+
+#include <nnapi/hal/aidl/Conversions.h>
+
+#include "Callbacks.h"
+#include "GeneratedTestHarness.h"
+#include "Utils.h"
+
+namespace aidl::android::hardware::neuralnetworks::vts::functional {
+
+using implementation::PreparedModelCallback;
+using test_helper::TestBuffer;
+using test_helper::TestModel;
+
+enum class DeadlineBoundType { NOW, UNLIMITED, SHORT };
+constexpr std::array<DeadlineBoundType, 3> deadlineBounds = {
+ DeadlineBoundType::NOW, DeadlineBoundType::UNLIMITED, DeadlineBoundType::SHORT};
+std::string toString(DeadlineBoundType type) {
+ switch (type) {
+ case DeadlineBoundType::NOW:
+ return "NOW";
+ case DeadlineBoundType::UNLIMITED:
+ return "UNLIMITED";
+ case DeadlineBoundType::SHORT:
+ return "SHORT";
+ }
+ LOG(FATAL) << "Unrecognized DeadlineBoundType: " << static_cast<int>(type);
+ return {};
+}
+
+constexpr auto kShortDuration = std::chrono::milliseconds{5};
+
+using Results = std::tuple<ErrorStatus, std::vector<OutputShape>, Timing>;
+using MaybeResults = std::optional<Results>;
+
+static int64_t makeDeadline(DeadlineBoundType deadlineBoundType) {
+ const auto getNanosecondsSinceEpoch = [](const auto& time) -> int64_t {
+ const auto timeSinceEpoch = time.time_since_epoch();
+ return std::chrono::duration_cast<std::chrono::nanoseconds>(timeSinceEpoch).count();
+ };
+
+ std::chrono::steady_clock::time_point timePoint;
+ switch (deadlineBoundType) {
+ case DeadlineBoundType::NOW:
+ timePoint = std::chrono::steady_clock::now();
+ break;
+ case DeadlineBoundType::UNLIMITED:
+ timePoint = std::chrono::steady_clock::time_point::max();
+ break;
+ case DeadlineBoundType::SHORT:
+ timePoint = std::chrono::steady_clock::now() + kShortDuration;
+ break;
+ }
+
+ return getNanosecondsSinceEpoch(timePoint);
+}
+
+void runPrepareModelTest(const std::shared_ptr<IDevice>& device, const Model& model,
+ Priority priority, std::optional<DeadlineBoundType> deadlineBound) {
+ int64_t deadline = kNoDeadline;
+ if (deadlineBound.has_value()) {
+ deadline = makeDeadline(deadlineBound.value());
+ }
+
+ // see if service can handle model
+ std::vector<bool> supportedOps;
+ const auto supportedCallStatus = device->getSupportedOperations(model, &supportedOps);
+ ASSERT_TRUE(supportedCallStatus.isOk());
+ ASSERT_NE(0ul, supportedOps.size());
+ const bool fullySupportsModel =
+ std::all_of(supportedOps.begin(), supportedOps.end(), [](bool valid) { return valid; });
+
+ // launch prepare model
+ const std::shared_ptr<PreparedModelCallback> preparedModelCallback =
+ ndk::SharedRefBase::make<PreparedModelCallback>();
+ const auto prepareLaunchStatus =
+ device->prepareModel(model, ExecutionPreference::FAST_SINGLE_ANSWER, priority, deadline,
+ {}, {}, kEmptyCacheToken, preparedModelCallback);
+ ASSERT_TRUE(prepareLaunchStatus.isOk())
+ << "prepareLaunchStatus: " << prepareLaunchStatus.getDescription();
+
+ // retrieve prepared model
+ preparedModelCallback->wait();
+ const ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
+ const std::shared_ptr<IPreparedModel> preparedModel = preparedModelCallback->getPreparedModel();
+
+ // The getSupportedOperations call returns a list of operations that are guaranteed not to fail
+ // if prepareModel is called, and 'fullySupportsModel' is true i.f.f. the entire model is
+ // guaranteed. If a driver has any doubt that it can prepare an operation, it must return false.
+ // So here, if a driver isn't sure if it can support an operation, but reports that it
+ // successfully prepared the model, the test can continue.
+ if (!fullySupportsModel && prepareReturnStatus != ErrorStatus::NONE) {
+ ASSERT_EQ(nullptr, preparedModel.get());
+ return;
+ }
+
+ // verify return status
+ if (!deadlineBound.has_value()) {
+ EXPECT_EQ(ErrorStatus::NONE, prepareReturnStatus);
+ } else {
+ switch (deadlineBound.value()) {
+ case DeadlineBoundType::NOW:
+ case DeadlineBoundType::SHORT:
+ // Either the driver successfully completed the task or it
+ // aborted and returned MISSED_DEADLINE_*.
+ EXPECT_TRUE(prepareReturnStatus == ErrorStatus::NONE ||
+ prepareReturnStatus == ErrorStatus::MISSED_DEADLINE_TRANSIENT ||
+ prepareReturnStatus == ErrorStatus::MISSED_DEADLINE_PERSISTENT);
+ break;
+ case DeadlineBoundType::UNLIMITED:
+ // If an unlimited deadline is supplied, we expect the execution to
+ // proceed normally. In this case, check it normally by breaking out
+ // of the switch statement.
+ EXPECT_EQ(ErrorStatus::NONE, prepareReturnStatus);
+ break;
+ }
+ }
+ ASSERT_EQ(prepareReturnStatus == ErrorStatus::NONE, preparedModel.get() != nullptr);
+}
+
+void runPrepareModelTests(const std::shared_ptr<IDevice>& device, const Model& model) {
+ // test priority
+ for (auto priority : ndk::enum_range<Priority>{}) {
+ SCOPED_TRACE("priority: " + toString(priority));
+ if (priority == kDefaultPriority) continue;
+ runPrepareModelTest(device, model, priority, {});
+ }
+
+ // test deadline
+ for (auto deadlineBound : deadlineBounds) {
+ SCOPED_TRACE("deadlineBound: " + toString(deadlineBound));
+ runPrepareModelTest(device, model, kDefaultPriority, deadlineBound);
+ }
+}
+
+static MaybeResults executeSynchronously(const std::shared_ptr<IPreparedModel>& preparedModel,
+ const Request& request, int64_t deadline) {
+ SCOPED_TRACE("synchronous");
+ const bool measure = false;
+
+ // run execution
+ ExecutionResult executionResult;
+ const auto ret = preparedModel->executeSynchronously(request, measure, deadline,
+ kOmittedTimeoutDuration, &executionResult);
+ EXPECT_TRUE(ret.isOk() || ret.getExceptionCode() == EX_SERVICE_SPECIFIC)
+ << ret.getDescription();
+ if (!ret.isOk()) {
+ if (ret.getExceptionCode() != EX_SERVICE_SPECIFIC) {
+ return std::nullopt;
+ }
+ return MaybeResults(
+ {static_cast<ErrorStatus>(ret.getServiceSpecificError()), {}, kNoTiming});
+ }
+
+ // return results
+ return MaybeResults({executionResult.outputSufficientSize
+ ? ErrorStatus::NONE
+ : ErrorStatus::OUTPUT_INSUFFICIENT_SIZE,
+ std::move(executionResult.outputShapes), executionResult.timing});
+}
+
+void runExecutionTest(const std::shared_ptr<IPreparedModel>& preparedModel,
+ const TestModel& testModel, const Request& request,
+ const ExecutionContext& context, DeadlineBoundType deadlineBound) {
+ const auto deadline = makeDeadline(deadlineBound);
+
+ // Perform execution and unpack results.
+ const auto results = executeSynchronously(preparedModel, request, deadline);
+ if (!results.has_value()) return;
+ const auto& [status, outputShapes, timing] = results.value();
+
+ // Verify no timing information was returned
+ EXPECT_EQ(timing, kNoTiming);
+
+ // Validate deadline information if applicable.
+ switch (deadlineBound) {
+ case DeadlineBoundType::NOW:
+ case DeadlineBoundType::SHORT:
+ // Either the driver successfully completed the task or it
+ // aborted and returned MISSED_DEADLINE_*.
+ ASSERT_TRUE(status == ErrorStatus::NONE ||
+ status == ErrorStatus::MISSED_DEADLINE_TRANSIENT ||
+ status == ErrorStatus::MISSED_DEADLINE_PERSISTENT);
+ break;
+ case DeadlineBoundType::UNLIMITED:
+ // If an unlimited deadline is supplied, we expect the execution to
+ // proceed normally. In this case, check it normally by breaking out
+ // of the switch statement.
+ ASSERT_EQ(ErrorStatus::NONE, status);
+ break;
+ }
+
+ // 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_TRUE(outputShapes.size() == 0 ||
+ outputShapes.size() == testModel.main.outputIndexes.size());
+
+ // 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 =
+ utils::toSigned(testModel.main.operands[testModel.main.outputIndexes[i]].dimensions)
+ .value();
+ const std::vector<int32_t>& actual = outputShapes[i].dimensions;
+ EXPECT_EQ(expect, actual);
+ }
+
+ // Retrieve execution results.
+ const std::vector<TestBuffer> outputs = context.getOutputBuffers(request);
+
+ // We want "close-enough" results.
+ if (status == ErrorStatus::NONE) {
+ checkResults(testModel, outputs);
+ }
+}
+
+void runExecutionTests(const std::shared_ptr<IPreparedModel>& preparedModel,
+ const TestModel& testModel, const Request& request,
+ const ExecutionContext& context) {
+ for (auto deadlineBound : deadlineBounds) {
+ runExecutionTest(preparedModel, testModel, request, context, deadlineBound);
+ }
+}
+
+void runTests(const std::shared_ptr<IDevice>& device, const TestModel& testModel) {
+ // setup
+ const Model model = createModel(testModel);
+
+ // run prepare model tests
+ runPrepareModelTests(device, model);
+
+ // prepare model
+ std::shared_ptr<IPreparedModel> preparedModel;
+ createPreparedModel(device, model, &preparedModel);
+ if (preparedModel == nullptr) return;
+
+ // run execution tests
+ ExecutionContext context;
+ const Request request = context.createRequest(testModel);
+ runExecutionTests(preparedModel, testModel, request, context);
+}
+
+class DeadlineTest : public GeneratedTestBase {};
+
+TEST_P(DeadlineTest, Test) {
+ runTests(kDevice, kTestModel);
+}
+
+INSTANTIATE_GENERATED_TEST(DeadlineTest,
+ [](const TestModel& testModel) { return !testModel.expectFailure; });
+
+} // namespace aidl::android::hardware::neuralnetworks::vts::functional
diff --git a/neuralnetworks/aidl/vts/functional/TestAssertions.cpp b/neuralnetworks/aidl/vts/functional/TestAssertions.cpp
new file mode 100644
index 0000000..a9e9456
--- /dev/null
+++ b/neuralnetworks/aidl/vts/functional/TestAssertions.cpp
@@ -0,0 +1,153 @@
+/*
+ * Copyright (C) 2021 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 <aidl/android/hardware/neuralnetworks/IPreparedModel.h>
+#include <aidl/android/hardware/neuralnetworks/OperandType.h>
+#include <aidl/android/hardware/neuralnetworks/OperationType.h>
+
+#include <ControlFlow.h>
+#include <TestHarness.h>
+
+namespace aidl::android::hardware::neuralnetworks {
+
+namespace nn = ::android::nn;
+
+static_assert(static_cast<uint64_t>(IPreparedModel::DEFAULT_LOOP_TIMEOUT_DURATION_NS) ==
+ nn::operation_while::kTimeoutNsDefault);
+static_assert(static_cast<uint64_t>(IPreparedModel::MAXIMUM_LOOP_TIMEOUT_DURATION_NS) ==
+ nn::operation_while::kTimeoutNsMaximum);
+
+// 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)
+
+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 aidl::android::hardware::neuralnetworks
diff --git a/neuralnetworks/aidl/vts/functional/TestMain.cpp b/neuralnetworks/aidl/vts/functional/TestMain.cpp
new file mode 100644
index 0000000..1d58608
--- /dev/null
+++ b/neuralnetworks/aidl/vts/functional/TestMain.cpp
@@ -0,0 +1,27 @@
+/*
+ * Copyright (C) 2021 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/binder_process.h>
+#include <gtest/gtest.h>
+#include "LogTestCaseToLogcat.h"
+
+int main(int argc, char** argv) {
+ testing::InitGoogleTest(&argc, argv);
+ testing::UnitTest::GetInstance()->listeners().Append(
+ new aidl::android::hardware::neuralnetworks::LogTestCaseToLogcat());
+ ABinderProcess_startThreadPool();
+ return RUN_ALL_TESTS();
+}
diff --git a/neuralnetworks/aidl/vts/functional/Utils.cpp b/neuralnetworks/aidl/vts/functional/Utils.cpp
new file mode 100644
index 0000000..14a496a
--- /dev/null
+++ b/neuralnetworks/aidl/vts/functional/Utils.cpp
@@ -0,0 +1,252 @@
+/*
+ * Copyright (C) 2021 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 "Utils.h"
+
+#include <aidl/android/hardware/neuralnetworks/IPreparedModelParcel.h>
+#include <aidl/android/hardware/neuralnetworks/Operand.h>
+#include <aidl/android/hardware/neuralnetworks/OperandType.h>
+#include <android-base/logging.h>
+#include <android/binder_status.h>
+#include <android/hardware_buffer.h>
+
+#include <iostream>
+#include <limits>
+#include <numeric>
+
+#include <MemoryUtils.h>
+#include <nnapi/SharedMemory.h>
+#include <nnapi/hal/aidl/Conversions.h>
+#include <nnapi/hal/aidl/Utils.h>
+
+namespace aidl::android::hardware::neuralnetworks {
+
+using test_helper::TestBuffer;
+using test_helper::TestModel;
+
+uint32_t sizeOfData(OperandType type) {
+ switch (type) {
+ case OperandType::FLOAT32:
+ case OperandType::INT32:
+ case OperandType::UINT32:
+ case OperandType::TENSOR_FLOAT32:
+ case OperandType::TENSOR_INT32:
+ return 4;
+ case OperandType::TENSOR_QUANT16_SYMM:
+ case OperandType::TENSOR_FLOAT16:
+ case OperandType::FLOAT16:
+ case OperandType::TENSOR_QUANT16_ASYMM:
+ return 2;
+ case OperandType::TENSOR_QUANT8_ASYMM:
+ case OperandType::BOOL:
+ case OperandType::TENSOR_BOOL8:
+ case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
+ case OperandType::TENSOR_QUANT8_SYMM:
+ case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
+ return 1;
+ case OperandType::SUBGRAPH:
+ return 0;
+ default:
+ CHECK(false) << "Invalid OperandType " << static_cast<uint32_t>(type);
+ return 0;
+ }
+}
+
+static bool isTensor(OperandType type) {
+ switch (type) {
+ case OperandType::FLOAT32:
+ case OperandType::INT32:
+ case OperandType::UINT32:
+ case OperandType::FLOAT16:
+ case OperandType::BOOL:
+ case OperandType::SUBGRAPH:
+ return false;
+ case OperandType::TENSOR_FLOAT32:
+ case OperandType::TENSOR_INT32:
+ case OperandType::TENSOR_QUANT16_SYMM:
+ case OperandType::TENSOR_FLOAT16:
+ case OperandType::TENSOR_QUANT16_ASYMM:
+ case OperandType::TENSOR_QUANT8_ASYMM:
+ case OperandType::TENSOR_BOOL8:
+ case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
+ case OperandType::TENSOR_QUANT8_SYMM:
+ case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
+ return true;
+ default:
+ CHECK(false) << "Invalid OperandType " << static_cast<uint32_t>(type);
+ return false;
+ }
+}
+
+uint32_t sizeOfData(const Operand& operand) {
+ const uint32_t dataSize = sizeOfData(operand.type);
+ if (isTensor(operand.type) && operand.dimensions.size() == 0) return 0;
+ return std::accumulate(operand.dimensions.begin(), operand.dimensions.end(), dataSize,
+ std::multiplies<>{});
+}
+
+std::unique_ptr<TestAshmem> TestAshmem::create(uint32_t size) {
+ auto ashmem = std::make_unique<TestAshmem>(size);
+ return ashmem->mIsValid ? std::move(ashmem) : nullptr;
+}
+
+void TestAshmem::initialize(uint32_t size) {
+ mIsValid = false;
+ ASSERT_GT(size, 0);
+ const auto sharedMemory = nn::createSharedMemory(size).value();
+ mMappedMemory = nn::map(sharedMemory).value();
+ mPtr = static_cast<uint8_t*>(std::get<void*>(mMappedMemory.pointer));
+ CHECK_NE(mPtr, nullptr);
+ mAidlMemory = utils::convert(sharedMemory).value();
+ mIsValid = true;
+}
+
+std::unique_ptr<TestBlobAHWB> TestBlobAHWB::create(uint32_t size) {
+ auto ahwb = std::make_unique<TestBlobAHWB>(size);
+ return ahwb->mIsValid ? std::move(ahwb) : nullptr;
+}
+
+void TestBlobAHWB::initialize(uint32_t size) {
+ mIsValid = false;
+ ASSERT_GT(size, 0);
+ const auto usage = AHARDWAREBUFFER_USAGE_CPU_READ_OFTEN | AHARDWAREBUFFER_USAGE_CPU_WRITE_OFTEN;
+ const AHardwareBuffer_Desc desc = {
+ .width = size,
+ .height = 1,
+ .layers = 1,
+ .format = AHARDWAREBUFFER_FORMAT_BLOB,
+ .usage = usage,
+ .stride = size,
+ };
+
+ ASSERT_EQ(AHardwareBuffer_allocate(&desc, &mAhwb), 0);
+ ASSERT_NE(mAhwb, nullptr);
+
+ const auto sharedMemory = nn::createSharedMemoryFromAHWB(*mAhwb).value();
+ mMapping = nn::map(sharedMemory).value();
+ mPtr = static_cast<uint8_t*>(std::get<void*>(mMapping.pointer));
+ CHECK_NE(mPtr, nullptr);
+ mAidlMemory = utils::convert(sharedMemory).value();
+
+ mIsValid = true;
+}
+
+TestBlobAHWB::~TestBlobAHWB() {
+ if (mAhwb) {
+ AHardwareBuffer_unlock(mAhwb, nullptr);
+ AHardwareBuffer_release(mAhwb);
+ }
+}
+
+std::string gtestCompliantName(std::string name) {
+ // gtest test names must only contain alphanumeric characters
+ std::replace_if(
+ name.begin(), name.end(), [](char c) { return !std::isalnum(c); }, '_');
+ return name;
+}
+
+::std::ostream& operator<<(::std::ostream& os, ErrorStatus errorStatus) {
+ return os << toString(errorStatus);
+}
+
+Request ExecutionContext::createRequest(const TestModel& testModel, MemoryType memoryType) {
+ CHECK(memoryType == MemoryType::ASHMEM || memoryType == MemoryType::BLOB_AHWB);
+
+ // Model inputs.
+ std::vector<RequestArgument> inputs(testModel.main.inputIndexes.size());
+ size_t inputSize = 0;
+ for (uint32_t i = 0; i < testModel.main.inputIndexes.size(); i++) {
+ const auto& op = testModel.main.operands[testModel.main.inputIndexes[i]];
+ if (op.data.size() == 0) {
+ // Omitted input.
+ inputs[i] = {.hasNoValue = true};
+ } else {
+ DataLocation loc = {.poolIndex = kInputPoolIndex,
+ .offset = static_cast<int64_t>(inputSize),
+ .length = static_cast<int64_t>(op.data.size())};
+ inputSize += op.data.alignedSize();
+ inputs[i] = {.hasNoValue = false, .location = loc, .dimensions = {}};
+ }
+ }
+
+ // Model outputs.
+ std::vector<RequestArgument> outputs(testModel.main.outputIndexes.size());
+ size_t outputSize = 0;
+ for (uint32_t i = 0; i < testModel.main.outputIndexes.size(); i++) {
+ const auto& op = testModel.main.operands[testModel.main.outputIndexes[i]];
+
+ // In the case of zero-sized output, we should at least provide a one-byte buffer.
+ // This is because zero-sized tensors are only supported internally to the driver, or
+ // reported in output shapes. It is illegal for the client to pre-specify a zero-sized
+ // tensor as model output. Otherwise, we will have two semantic conflicts:
+ // - "Zero dimension" conflicts with "unspecified dimension".
+ // - "Omitted operand buffer" conflicts with "zero-sized operand buffer".
+ size_t bufferSize = std::max<size_t>(op.data.size(), 1);
+
+ DataLocation loc = {.poolIndex = kOutputPoolIndex,
+ .offset = static_cast<int64_t>(outputSize),
+ .length = static_cast<int64_t>(bufferSize)};
+ outputSize += op.data.size() == 0 ? TestBuffer::kAlignment : op.data.alignedSize();
+ outputs[i] = {.hasNoValue = false, .location = loc, .dimensions = {}};
+ }
+
+ // Allocate memory pools.
+ if (memoryType == MemoryType::ASHMEM) {
+ mInputMemory = TestAshmem::create(inputSize);
+ mOutputMemory = TestAshmem::create(outputSize);
+ } else {
+ mInputMemory = TestBlobAHWB::create(inputSize);
+ mOutputMemory = TestBlobAHWB::create(outputSize);
+ }
+ CHECK_NE(mInputMemory, nullptr);
+ CHECK_NE(mOutputMemory, nullptr);
+
+ auto copiedInputMemory = utils::clone(*mInputMemory->getAidlMemory());
+ CHECK(copiedInputMemory.has_value()) << copiedInputMemory.error().message;
+ auto copiedOutputMemory = utils::clone(*mOutputMemory->getAidlMemory());
+ CHECK(copiedOutputMemory.has_value()) << copiedOutputMemory.error().message;
+
+ std::vector<RequestMemoryPool> pools;
+ pools.push_back(RequestMemoryPool::make<RequestMemoryPool::Tag::pool>(
+ std::move(copiedInputMemory).value()));
+ pools.push_back(RequestMemoryPool::make<RequestMemoryPool::Tag::pool>(
+ std::move(copiedOutputMemory).value()));
+
+ // Copy input data to the memory pool.
+ uint8_t* inputPtr = mInputMemory->getPointer();
+ for (uint32_t i = 0; i < testModel.main.inputIndexes.size(); i++) {
+ const auto& op = testModel.main.operands[testModel.main.inputIndexes[i]];
+ if (op.data.size() > 0) {
+ const uint8_t* begin = op.data.get<uint8_t>();
+ const uint8_t* end = begin + op.data.size();
+ std::copy(begin, end, inputPtr + inputs[i].location.offset);
+ }
+ }
+
+ return {.inputs = std::move(inputs), .outputs = std::move(outputs), .pools = std::move(pools)};
+}
+
+std::vector<TestBuffer> ExecutionContext::getOutputBuffers(const Request& request) const {
+ // Copy out output results.
+ uint8_t* outputPtr = mOutputMemory->getPointer();
+ std::vector<TestBuffer> outputBuffers;
+ for (const auto& output : request.outputs) {
+ outputBuffers.emplace_back(output.location.length, outputPtr + output.location.offset);
+ }
+ return outputBuffers;
+}
+
+} // namespace aidl::android::hardware::neuralnetworks
diff --git a/neuralnetworks/aidl/vts/functional/Utils.h b/neuralnetworks/aidl/vts/functional/Utils.h
new file mode 100644
index 0000000..266301c
--- /dev/null
+++ b/neuralnetworks/aidl/vts/functional/Utils.h
@@ -0,0 +1,153 @@
+/*
+ * Copyright (C) 2021 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_AIDL_UTILS_H
+#define ANDROID_HARDWARE_NEURALNETWORKS_AIDL_UTILS_H
+
+#include <android-base/logging.h>
+#include <android/hardware_buffer.h>
+#include <gtest/gtest.h>
+
+#include <algorithm>
+#include <iosfwd>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include <aidl/android/hardware/neuralnetworks/IDevice.h>
+#include <aidl/android/hardware/neuralnetworks/Memory.h>
+#include <aidl/android/hardware/neuralnetworks/Operand.h>
+#include <aidl/android/hardware/neuralnetworks/OperandType.h>
+#include <aidl/android/hardware/neuralnetworks/Priority.h>
+#include <aidl/android/hardware/neuralnetworks/Request.h>
+
+#include <TestHarness.h>
+#include <nnapi/SharedMemory.h>
+
+namespace aidl::android::hardware::neuralnetworks {
+
+namespace nn = ::android::nn;
+
+inline constexpr Priority kDefaultPriority = Priority::MEDIUM;
+
+inline constexpr Timing kNoTiming = {.timeOnDevice = -1, .timeInDriver = -1};
+inline constexpr int64_t kNoDeadline = -1;
+inline constexpr int64_t kOmittedTimeoutDuration = -1;
+inline constexpr int64_t kNoDuration = -1;
+inline const std::vector<uint8_t> kEmptyCacheToken(IDevice::BYTE_SIZE_OF_CACHE_TOKEN);
+
+// Returns the amount of space needed to store a value of the specified type.
+//
+// Aborts if the specified type is an extension type or OEM type.
+uint32_t sizeOfData(OperandType type);
+
+// Returns the amount of space needed to store a value of the dimensions and
+// type of this operand. For a non-extension, non-OEM tensor with unspecified
+// rank or at least one unspecified dimension, returns zero.
+//
+// Aborts if the specified type is an extension type or OEM type.
+uint32_t sizeOfData(const Operand& operand);
+
+// Convenience class to manage the lifetime of memory resources.
+class TestMemoryBase {
+ DISALLOW_COPY_AND_ASSIGN(TestMemoryBase);
+
+ public:
+ TestMemoryBase() = default;
+ virtual ~TestMemoryBase() = default;
+ uint8_t* getPointer() const { return mPtr; }
+ const Memory* getAidlMemory() const { return &mAidlMemory; }
+
+ protected:
+ uint8_t* mPtr = nullptr;
+ Memory mAidlMemory;
+ bool mIsValid = false;
+};
+
+class TestAshmem : public TestMemoryBase {
+ public:
+ static std::unique_ptr<TestAshmem> create(uint32_t size);
+
+ // Prefer TestAshmem::create.
+ // The constructor calls initialize, which constructs the memory resources. This is a workaround
+ // that gtest macros cannot be used directly in a constructor.
+ TestAshmem(uint32_t size) { initialize(size); }
+
+ private:
+ void initialize(uint32_t size);
+ nn::Mapping mMappedMemory;
+};
+
+class TestBlobAHWB : public TestMemoryBase {
+ public:
+ static std::unique_ptr<TestBlobAHWB> create(uint32_t size);
+
+ // Prefer TestBlobAHWB::create.
+ // The constructor calls initialize, which constructs the memory resources. This is a
+ // workaround that gtest macros cannot be used directly in a constructor.
+ TestBlobAHWB(uint32_t size) { initialize(size); }
+ ~TestBlobAHWB();
+
+ private:
+ void initialize(uint32_t size);
+ AHardwareBuffer* mAhwb = nullptr;
+ nn::Mapping mMapping;
+};
+
+enum class MemoryType { ASHMEM, BLOB_AHWB, DEVICE };
+
+// Manages the lifetime of memory resources used in an execution.
+class ExecutionContext {
+ DISALLOW_COPY_AND_ASSIGN(ExecutionContext);
+
+ public:
+ static constexpr uint32_t kInputPoolIndex = 0;
+ static constexpr uint32_t kOutputPoolIndex = 1;
+
+ ExecutionContext() = default;
+
+ // Create HIDL Request from the TestModel struct.
+ Request createRequest(const test_helper::TestModel& testModel,
+ MemoryType memoryType = MemoryType::ASHMEM);
+
+ // After execution, copy out output results from the output memory pool.
+ std::vector<test_helper::TestBuffer> getOutputBuffers(const Request& request) const;
+
+ private:
+ std::unique_ptr<TestMemoryBase> mInputMemory, mOutputMemory;
+};
+
+template <typename Type>
+using Named = std::pair<std::string, Type>;
+
+template <typename Type>
+const std::string& getName(const Named<Type>& namedData) {
+ return namedData.first;
+}
+
+template <typename Type>
+const Type& getData(const Named<Type>& namedData) {
+ return namedData.second;
+}
+
+std::string gtestCompliantName(std::string name);
+
+// pretty-print values for error messages
+::std::ostream& operator<<(::std::ostream& os, ErrorStatus errorStatus);
+
+} // namespace aidl::android::hardware::neuralnetworks
+
+#endif // ANDROID_HARDWARE_NEURALNETWORKS_AIDL_UTILS_H
diff --git a/neuralnetworks/aidl/vts/functional/ValidateModel.cpp b/neuralnetworks/aidl/vts/functional/ValidateModel.cpp
new file mode 100644
index 0000000..b84d981
--- /dev/null
+++ b/neuralnetworks/aidl/vts/functional/ValidateModel.cpp
@@ -0,0 +1,1338 @@
+/*
+ * Copyright (C) 2021 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_aidl_hal_test"
+
+#include <aidl/android/hardware/common/NativeHandle.h>
+#include <android/binder_auto_utils.h>
+#include <android/binder_enums.h>
+#include <android/binder_interface_utils.h>
+#include <nnapi/TypeUtils.h>
+#include <nnapi/hal/aidl/Conversions.h>
+#include <nnapi/hal/aidl/Utils.h>
+
+#include <optional>
+#include <type_traits>
+#include <utility>
+
+#include "Callbacks.h"
+#include "GeneratedTestHarness.h"
+#include "Utils.h"
+#include "VtsHalNeuralnetworks.h"
+
+namespace aidl::android::hardware::neuralnetworks::vts::functional {
+
+using common::NativeHandle;
+using implementation::PreparedModelCallback;
+
+using PrepareModelMutation = std::function<void(Model*, ExecutionPreference*, Priority*)>;
+
+///////////////////////// UTILITY FUNCTIONS /////////////////////////
+
+static void validateGetSupportedOperations(const std::shared_ptr<IDevice>& device,
+ const std::string& message, const Model& model) {
+ SCOPED_TRACE(message + " [getSupportedOperations]");
+
+ std::vector<bool> supported;
+ const auto retStatus = device->getSupportedOperations(model, &supported);
+
+ ASSERT_FALSE(retStatus.isOk());
+ ASSERT_EQ(retStatus.getExceptionCode(), EX_SERVICE_SPECIFIC);
+ ASSERT_EQ(static_cast<ErrorStatus>(retStatus.getServiceSpecificError()),
+ ErrorStatus::INVALID_ARGUMENT);
+}
+
+static void validatePrepareModel(const std::shared_ptr<IDevice>& device, const std::string& message,
+ const Model& model, ExecutionPreference preference,
+ Priority priority) {
+ SCOPED_TRACE(message + " [prepareModel]");
+
+ std::shared_ptr<PreparedModelCallback> preparedModelCallback =
+ ndk::SharedRefBase::make<PreparedModelCallback>();
+ const auto prepareLaunchStatus =
+ device->prepareModel(model, preference, priority, kNoDeadline, {}, {}, kEmptyCacheToken,
+ preparedModelCallback);
+ ASSERT_FALSE(prepareLaunchStatus.isOk());
+ ASSERT_EQ(prepareLaunchStatus.getExceptionCode(), EX_SERVICE_SPECIFIC);
+ ASSERT_EQ(static_cast<ErrorStatus>(prepareLaunchStatus.getServiceSpecificError()),
+ ErrorStatus::INVALID_ARGUMENT);
+
+ preparedModelCallback->wait();
+ ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
+ ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, prepareReturnStatus);
+ std::shared_ptr<IPreparedModel> preparedModel = preparedModelCallback->getPreparedModel();
+ ASSERT_EQ(nullptr, preparedModel.get());
+}
+
+static bool validExecutionPreference(ExecutionPreference preference) {
+ return preference == ExecutionPreference::LOW_POWER ||
+ preference == ExecutionPreference::FAST_SINGLE_ANSWER ||
+ preference == ExecutionPreference::SUSTAINED_SPEED;
+}
+
+static bool validExecutionPriority(Priority priority) {
+ return priority == Priority::LOW || priority == Priority::MEDIUM || priority == Priority::HIGH;
+}
+
+// Primary validation function. This function will take a valid model, apply a
+// mutation to invalidate the model, the execution preference, or the priority,
+// then pass these to supportedOperations and/or prepareModel if that method is
+// called with an invalid argument.
+static void validate(const std::shared_ptr<IDevice>& device, const std::string& message,
+ const Model& originalModel, const PrepareModelMutation& mutate) {
+ Model model = utils::clone(originalModel).value();
+ ExecutionPreference preference = ExecutionPreference::FAST_SINGLE_ANSWER;
+ Priority priority = kDefaultPriority;
+ mutate(&model, &preference, &priority);
+
+ if (validExecutionPreference(preference) && validExecutionPriority(priority)) {
+ validateGetSupportedOperations(device, message, model);
+ }
+
+ validatePrepareModel(device, message, model, preference, priority);
+}
+
+static uint32_t addOperand(Model* model) {
+ model->main.operands.push_back({
+ .type = OperandType::INT32,
+ .dimensions = {},
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::SUBGRAPH_INPUT,
+ .location = {.poolIndex = 0, .offset = 0, .length = 0},
+ });
+ return model->main.operands.size() - 1;
+}
+
+static uint32_t addOperand(Model* model, OperandLifeTime lifetime) {
+ uint32_t index = addOperand(model);
+ model->main.operands[index].lifetime = lifetime;
+ return index;
+}
+
+// If we introduce a CONSTANT_COPY for an operand of size operandSize,
+// how much will this increase the size of the model? This assumes
+// that we can (re)use all of model.operandValues for the operand
+// value.
+static size_t constantCopyExtraSize(const Model& model, size_t operandSize) {
+ const size_t operandValuesSize = model.operandValues.size();
+ return (operandValuesSize < operandSize) ? (operandSize - operandValuesSize) : 0;
+}
+
+// Highly specialized utility routine for converting an operand to
+// CONSTANT_COPY lifetime.
+//
+// Expects that:
+// - operand has a known size
+// - operand->lifetime has already been set to CONSTANT_COPY
+// - operand->location has been zeroed out
+//
+// Does the following:
+// - initializes operand->location to point to the beginning of model->operandValues
+// - resizes model->operandValues (if necessary) to be large enough for the operand
+// value, padding it with zeroes on the end
+//
+// Potential problem:
+// By changing the operand to CONSTANT_COPY lifetime, this function is effectively initializing the
+// operand with unspecified (but deterministic) data. This means that the model may be invalidated
+// in two ways: not only is the lifetime of CONSTANT_COPY invalid, but the operand's value in the
+// graph may also be invalid (e.g., if the operand is used as an activation code and has an invalid
+// value). For now, this should be fine because it just means we're not testing what we think we're
+// testing in certain cases; but we can handwave this and assume we're probabilistically likely to
+// exercise the validation code over the span of the entire test set and operand space.
+//
+// Aborts if the specified operand type is an extension type or OEM type.
+static void becomeConstantCopy(Model* model, Operand* operand) {
+ // sizeOfData will abort if the specified type is an extension type or OEM type.
+ const size_t sizeOfOperand = sizeOfData(*operand);
+ EXPECT_NE(sizeOfOperand, size_t(0));
+ operand->location.poolIndex = 0;
+ operand->location.offset = 0;
+ operand->location.length = sizeOfOperand;
+ if (model->operandValues.size() < sizeOfOperand) {
+ model->operandValues.resize(sizeOfOperand);
+ }
+}
+
+// The sizeForBinder() functions estimate the size of the
+// representation of a value when sent to binder. It's probably a bit
+// of an under-estimate, because we don't know the size of the
+// metadata in the binder format (e.g., representation of the size of
+// a vector); but at least it adds up "big" things like vector
+// contents. However, it doesn't treat inter-field or end-of-struct
+// padding in a methodical way -- there's no attempt to be consistent
+// in whether or not padding in the native (C++) representation
+// contributes to the estimated size for the binder representation;
+// and there's no attempt to understand what padding (if any) is
+// needed in the binder representation.
+//
+// This assumes that non-metadata uses a fixed length encoding (e.g.,
+// a uint32_t is always encoded in sizeof(uint32_t) bytes, rather than
+// using an encoding whose length is related to the magnitude of the
+// encoded value).
+
+template <typename Type>
+static size_t sizeForBinder(const Type& val) {
+ static_assert(std::is_trivially_copyable_v<std::remove_reference_t<Type>>,
+ "expected a trivially copyable type");
+ return sizeof(val);
+}
+
+template <typename Type>
+static size_t sizeForBinder(const std::vector<Type>& vec) {
+ return std::accumulate(vec.begin(), vec.end(), 0,
+ [](size_t acc, const Type& x) { return acc + sizeForBinder(x); });
+}
+
+template <>
+size_t sizeForBinder(const SymmPerChannelQuantParams& symmPerChannelQuantParams) {
+ size_t size = 0;
+
+ size += sizeForBinder(symmPerChannelQuantParams.scales);
+ size += sizeForBinder(symmPerChannelQuantParams.channelDim);
+
+ return size;
+}
+
+template <>
+size_t sizeForBinder(const std::optional<OperandExtraParams>& optionalExtraParams) {
+ if (!optionalExtraParams.has_value()) {
+ return 0;
+ }
+ const auto& extraParams = optionalExtraParams.value();
+ using Tag = OperandExtraParams::Tag;
+ switch (extraParams.getTag()) {
+ case Tag::channelQuant:
+ return sizeForBinder(extraParams.get<Tag::channelQuant>());
+ case Tag::extension:
+ return sizeForBinder(extraParams.get<Tag::extension>());
+ }
+ LOG(FATAL) << "Unrecognized extraParams tag: " << static_cast<int>(extraParams.getTag());
+ return 0;
+}
+
+template <>
+size_t sizeForBinder(const Operand& operand) {
+ size_t size = 0;
+
+ size += sizeForBinder(operand.type);
+ size += sizeForBinder(operand.dimensions);
+ size += sizeForBinder(operand.scale);
+ size += sizeForBinder(operand.zeroPoint);
+ size += sizeForBinder(operand.lifetime);
+ size += sizeForBinder(operand.location);
+ size += sizeForBinder(operand.extraParams);
+
+ return size;
+}
+
+template <>
+size_t sizeForBinder(const Operation& operation) {
+ size_t size = 0;
+
+ size += sizeForBinder(operation.type);
+ size += sizeForBinder(operation.inputs);
+ size += sizeForBinder(operation.outputs);
+
+ return size;
+}
+
+template <>
+size_t sizeForBinder(const std::string& name) {
+ return name.size();
+}
+
+template <>
+size_t sizeForBinder(const Memory& memory) {
+ // This is just a guess.
+
+ size_t size = 0;
+ const NativeHandle& handle = memory.handle;
+ size += sizeof(decltype(handle.fds)::value_type) * handle.fds.size();
+ size += sizeof(decltype(handle.ints)::value_type) * handle.ints.size();
+ size += sizeForBinder(memory.name);
+ size += sizeof(memory);
+
+ return size;
+}
+
+template <>
+size_t sizeForBinder(const Subgraph& subgraph) {
+ size_t size = 0;
+
+ size += sizeForBinder(subgraph.operands);
+ size += sizeForBinder(subgraph.operations);
+ size += sizeForBinder(subgraph.inputIndexes);
+ size += sizeForBinder(subgraph.outputIndexes);
+
+ return size;
+}
+
+template <>
+size_t sizeForBinder(const ExtensionNameAndPrefix& extensionNameToPrefix) {
+ size_t size = 0;
+
+ size += sizeForBinder(extensionNameToPrefix.name);
+ size += sizeForBinder(extensionNameToPrefix.prefix);
+
+ return size;
+}
+
+template <>
+size_t sizeForBinder(const Model& model) {
+ size_t size = 0;
+
+ size += sizeForBinder(model.main);
+ size += sizeForBinder(model.referenced);
+ size += sizeForBinder(model.operandValues);
+ size += sizeForBinder(model.pools);
+ size += sizeForBinder(model.relaxComputationFloat32toFloat16);
+ size += sizeForBinder(model.extensionNameToPrefix);
+
+ return size;
+}
+
+// https://developer.android.com/reference/android/os/TransactionTooLargeException.html
+//
+// "The Binder transaction buffer has a limited fixed size,
+// currently 1Mb, which is shared by all transactions in progress
+// for the process."
+//
+// Will our representation fit under this limit? There are two complications:
+// - Our representation size is just approximate (see sizeForBinder()).
+// - This object may not be the only occupant of the Binder transaction buffer.
+// So we'll be very conservative: We want the representation size to be no
+// larger than half the transaction buffer size.
+//
+// If our representation grows large enough that it still fits within
+// the transaction buffer but combined with other transactions may
+// exceed the buffer size, then we may see intermittent HAL transport
+// errors.
+static bool exceedsBinderSizeLimit(size_t representationSize) {
+ // Instead of using this fixed buffer size, we might instead be able to use
+ // ProcessState::self()->getMmapSize(). However, this has a potential
+ // problem: The binder/mmap size of the current process does not necessarily
+ // indicate the binder/mmap size of the service (i.e., the other process).
+ // The only way it would be a good indication is if both the current process
+ // and the service use the default size.
+ static const size_t kHalfBufferSize = 1024 * 1024 / 2;
+
+ return representationSize > kHalfBufferSize;
+}
+
+///////////////////////// VALIDATE EXECUTION ORDER ////////////////////////////
+
+static void mutateExecutionOrderTest(const std::shared_ptr<IDevice>& device, const Model& model,
+ const std::vector<uint32_t>& numberOfConsumers) {
+ for (size_t operation = 0; operation < model.main.operations.size(); ++operation) {
+ const Operation& operationObj = model.main.operations[operation];
+ for (uint32_t input : operationObj.inputs) {
+ if (model.main.operands[input].lifetime == OperandLifeTime::TEMPORARY_VARIABLE ||
+ model.main.operands[input].lifetime == OperandLifeTime::SUBGRAPH_OUTPUT) {
+ // This operation reads an operand written by some
+ // other operation. Move this operation to the
+ // beginning of the sequence, ensuring that it reads
+ // the operand before that operand is written, thereby
+ // violating execution order rules.
+ const std::string message = "mutateExecutionOrderTest: operation " +
+ std::to_string(operation) + " is a reader";
+ validate(device, message, model,
+ [operation](Model* model, ExecutionPreference*, Priority*) {
+ auto& operations = model->main.operations;
+ std::rotate(operations.begin(), operations.begin() + operation,
+ operations.begin() + operation + 1);
+ });
+ break; // only need to do this once per operation
+ }
+ }
+ for (uint32_t output : operationObj.outputs) {
+ if (numberOfConsumers[output] > 0) {
+ // This operation writes an operand read by some other
+ // operation. Move this operation to the end of the
+ // sequence, ensuring that it writes the operand after
+ // that operand is read, thereby violating execution
+ // order rules.
+ const std::string message = "mutateExecutionOrderTest: operation " +
+ std::to_string(operation) + " is a writer";
+ validate(device, message, model,
+ [operation](Model* model, ExecutionPreference*, Priority*) {
+ auto& operations = model->main.operations;
+ std::rotate(operations.begin() + operation,
+ operations.begin() + operation + 1, operations.end());
+ });
+ break; // only need to do this once per operation
+ }
+ }
+ }
+}
+
+///////////////////////// VALIDATE MODEL OPERAND TYPE /////////////////////////
+
+static const int32_t invalidOperandTypes[] = {
+ -1,
+ static_cast<int32_t>(*(ndk::enum_range<OperandType>().end() - 1)) + 1,
+};
+
+static void mutateOperandTypeTest(const std::shared_ptr<IDevice>& device, const Model& model) {
+ for (size_t operand = 0; operand < model.main.operands.size(); ++operand) {
+ for (int32_t invalidOperandType : invalidOperandTypes) {
+ const std::string message = "mutateOperandTypeTest: operand " +
+ std::to_string(operand) + " set to value " +
+ std::to_string(invalidOperandType);
+ validate(device, message, model,
+ [operand, invalidOperandType](Model* model, ExecutionPreference*, Priority*) {
+ model->main.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 std::shared_ptr<IDevice>& device, const Model& model) {
+ for (size_t operand = 0; operand < model.main.operands.size(); ++operand) {
+ const uint32_t invalidRank = getInvalidRank(model.main.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, ExecutionPreference*, Priority*) {
+ model->main.operands[operand].dimensions =
+ std::vector<int32_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:
+ case OperandType::SUBGRAPH:
+ 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 std::shared_ptr<IDevice>& device, const Model& model) {
+ for (size_t operand = 0; operand < model.main.operands.size(); ++operand) {
+ const float invalidScale = getInvalidScale(model.main.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, ExecutionPreference*, Priority*) {
+ model->main.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:
+ case OperandType::SUBGRAPH:
+ 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 std::shared_ptr<IDevice>& device, const Model& model) {
+ for (size_t operand = 0; operand < model.main.operands.size(); ++operand) {
+ const std::vector<int32_t> invalidZeroPoints =
+ getInvalidZeroPoints(model.main.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, ExecutionPreference*, Priority*) {
+ model->main.operands[operand].zeroPoint = invalidZeroPoint;
+ });
+ }
+ }
+}
+
+///////////////////////// VALIDATE OPERAND LIFETIME /////////////////////////////////////////////
+
+static std::vector<OperandLifeTime> getInvalidLifeTimes(const Model& model, size_t modelSize,
+ const Operand& operand) {
+ // TODO: Support OperandLifeTime::CONSTANT_REFERENCE as an invalid lifetime
+ // TODO: Support OperandLifeTime::NO_VALUE as an invalid lifetime
+
+ // Ways to get an invalid lifetime:
+ // - change whether a lifetime means an operand should have a writer
+ std::vector<OperandLifeTime> ret;
+ switch (operand.lifetime) {
+ case OperandLifeTime::SUBGRAPH_OUTPUT:
+ case OperandLifeTime::TEMPORARY_VARIABLE:
+ ret = {
+ OperandLifeTime::SUBGRAPH_INPUT,
+ OperandLifeTime::CONSTANT_COPY,
+ };
+ break;
+ case OperandLifeTime::CONSTANT_COPY:
+ case OperandLifeTime::CONSTANT_POOL:
+ case OperandLifeTime::SUBGRAPH_INPUT:
+ ret = {
+ OperandLifeTime::TEMPORARY_VARIABLE,
+ OperandLifeTime::SUBGRAPH_OUTPUT,
+ };
+ break;
+ case OperandLifeTime::NO_VALUE:
+ // Not enough information to know whether
+ // TEMPORARY_VARIABLE or CONSTANT_COPY would be invalid --
+ // is this operand written (then CONSTANT_COPY would be
+ // invalid) or not (then TEMPORARY_VARIABLE would be
+ // invalid)?
+ break;
+ case OperandLifeTime::SUBGRAPH:
+ break;
+ default:
+ ADD_FAILURE();
+ break;
+ }
+
+ const size_t operandSize = sizeOfData(operand); // will be zero if shape is unknown
+ if (!operandSize ||
+ exceedsBinderSizeLimit(modelSize + constantCopyExtraSize(model, operandSize))) {
+ // Unknown size or too-large size
+ ret.erase(std::remove(ret.begin(), ret.end(), OperandLifeTime::CONSTANT_COPY), ret.end());
+ }
+
+ return ret;
+}
+
+static void mutateOperandLifeTimeTest(const std::shared_ptr<IDevice>& device, const Model& model) {
+ const size_t modelSize = sizeForBinder(model);
+ for (size_t operand = 0; operand < model.main.operands.size(); ++operand) {
+ const std::vector<OperandLifeTime> invalidLifeTimes =
+ getInvalidLifeTimes(model, modelSize, model.main.operands[operand]);
+ for (OperandLifeTime invalidLifeTime : invalidLifeTimes) {
+ const std::string message = "mutateOperandLifetimeTest: operand " +
+ std::to_string(operand) + " has lifetime " +
+ toString(invalidLifeTime) + " instead of lifetime " +
+ toString(model.main.operands[operand].lifetime);
+ validate(device, message, model,
+ [operand, invalidLifeTime](Model* model, ExecutionPreference*, Priority*) {
+ static const DataLocation kZeroDataLocation = {};
+ Operand& operandObj = model->main.operands[operand];
+ switch (operandObj.lifetime) {
+ case OperandLifeTime::SUBGRAPH_INPUT: {
+ auto& inputs = model->main.inputIndexes;
+ inputs.erase(std::remove(inputs.begin(), inputs.end(), operand),
+ inputs.end());
+ break;
+ }
+ case OperandLifeTime::SUBGRAPH_OUTPUT: {
+ auto& outputs = model->main.outputIndexes;
+ outputs.erase(std::remove(outputs.begin(), outputs.end(), operand),
+ outputs.end());
+ break;
+ }
+ default:
+ break;
+ }
+ operandObj.lifetime = invalidLifeTime;
+ operandObj.location = kZeroDataLocation;
+ switch (invalidLifeTime) {
+ case OperandLifeTime::CONSTANT_COPY: {
+ becomeConstantCopy(model, &operandObj);
+ break;
+ }
+ case OperandLifeTime::SUBGRAPH_INPUT:
+ model->main.inputIndexes.push_back(operand);
+ break;
+ case OperandLifeTime::SUBGRAPH_OUTPUT:
+ model->main.outputIndexes.push_back(operand);
+ break;
+ default:
+ break;
+ }
+ });
+ }
+ }
+}
+
+///////////////////////// VALIDATE OPERAND INPUT-or-OUTPUT //////////////////////////////////////
+
+static std::optional<OperandLifeTime> getInputOutputLifeTime(const Model& model, size_t modelSize,
+ const Operand& operand) {
+ // Ways to get an invalid lifetime (with respect to model inputIndexes and outputIndexes):
+ // - change whether a lifetime means an operand is a model input, a model output, or neither
+ // - preserve whether or not a lifetime means an operand should have a writer
+ switch (operand.lifetime) {
+ case OperandLifeTime::CONSTANT_COPY:
+ case OperandLifeTime::CONSTANT_POOL:
+ return OperandLifeTime::SUBGRAPH_INPUT;
+ case OperandLifeTime::SUBGRAPH_INPUT: {
+ const size_t operandSize = sizeOfData(operand); // will be zero if shape is unknown
+ if (!operandSize ||
+ exceedsBinderSizeLimit(modelSize + constantCopyExtraSize(model, operandSize))) {
+ // Unknown size or too-large size
+ break;
+ }
+ return OperandLifeTime::CONSTANT_COPY;
+ }
+ case OperandLifeTime::SUBGRAPH_OUTPUT:
+ return OperandLifeTime::TEMPORARY_VARIABLE;
+ case OperandLifeTime::TEMPORARY_VARIABLE:
+ return OperandLifeTime::SUBGRAPH_OUTPUT;
+ case OperandLifeTime::NO_VALUE:
+ // Not enough information to know whether
+ // TEMPORARY_VARIABLE or CONSTANT_COPY would be an
+ // appropriate choice -- is this operand written (then
+ // TEMPORARY_VARIABLE would be appropriate) or not (then
+ // CONSTANT_COPY would be appropriate)?
+ break;
+ case OperandLifeTime::SUBGRAPH:
+ break;
+ default:
+ ADD_FAILURE();
+ break;
+ }
+
+ return std::nullopt;
+}
+
+static void mutateOperandInputOutputTest(const std::shared_ptr<IDevice>& device,
+ const Model& model) {
+ const size_t modelSize = sizeForBinder(model);
+ for (size_t operand = 0; operand < model.main.operands.size(); ++operand) {
+ const std::optional<OperandLifeTime> changedLifeTime =
+ getInputOutputLifeTime(model, modelSize, model.main.operands[operand]);
+ if (changedLifeTime) {
+ const std::string message = "mutateOperandInputOutputTest: operand " +
+ std::to_string(operand) + " has lifetime " +
+ toString(*changedLifeTime) + " instead of lifetime " +
+ toString(model.main.operands[operand].lifetime);
+ validate(device, message, model,
+ [operand, changedLifeTime](Model* model, ExecutionPreference*, Priority*) {
+ static const DataLocation kZeroDataLocation = {};
+ Operand& operandObj = model->main.operands[operand];
+ operandObj.lifetime = *changedLifeTime;
+ operandObj.location = kZeroDataLocation;
+ if (*changedLifeTime == OperandLifeTime::CONSTANT_COPY) {
+ becomeConstantCopy(model, &operandObj);
+ }
+ });
+ }
+ }
+}
+
+///////////////////////// VALIDATE OPERAND NUMBER OF WRITERS ////////////////////////////////////
+
+static void mutateOperandAddWriterTest(const std::shared_ptr<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.main.operations.size(); ++operation) {
+ for (size_t badOutputNum = 0;
+ badOutputNum < model.main.operations[operation].outputs.size(); ++badOutputNum) {
+ const uint32_t outputOperandIndex =
+ model.main.operations[operation].outputs[badOutputNum];
+ const std::string message = "mutateOperandAddWriterTest: operation " +
+ std::to_string(operation) + " writes to " +
+ std::to_string(outputOperandIndex);
+ // We'll insert a copy of the operation, all of whose
+ // OTHER output operands are newly-created -- i.e.,
+ // there'll only be a duplicate write of ONE of that
+ // operation's output operands.
+ validate(device, message, model,
+ [operation, badOutputNum](Model* model, ExecutionPreference*, Priority*) {
+ Operation newOperation = model->main.operations[operation];
+ for (size_t outputNum = 0; outputNum < newOperation.outputs.size();
+ ++outputNum) {
+ if (outputNum == badOutputNum) continue;
+
+ Operand operandValue =
+ model->main.operands[newOperation.outputs[outputNum]];
+ if (operandValue.lifetime == OperandLifeTime::SUBGRAPH_OUTPUT) {
+ operandValue.lifetime = OperandLifeTime::TEMPORARY_VARIABLE;
+ } else {
+ ASSERT_EQ(operandValue.lifetime,
+ OperandLifeTime::TEMPORARY_VARIABLE);
+ }
+ newOperation.outputs[outputNum] = model->main.operands.size();
+ model->main.operands.push_back(operandValue);
+ }
+ // Where do we insert the extra writer (a new
+ // operation)? It has to be later than all the
+ // writers of its inputs. The easiest thing to do
+ // is to insert it at the end of the operation
+ // sequence.
+ model->main.operations.push_back(newOperation);
+ });
+ }
+ }
+}
+
+///////////////////////// VALIDATE EXTRA ??? /////////////////////////
+
+// 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 = {};
+ 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
+ : std::vector<int32_t>({1});
+ newOperand.scale = 0.0f;
+ newOperand.zeroPoint = 0;
+ break;
+ case OperandType::TENSOR_INT32:
+ newOperand.dimensions = operand->dimensions.size() > 0 ? operand->dimensions
+ : std::vector<int32_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
+ : std::vector<int32_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
+ : std::vector<int32_t>({1});
+ newOperand.scale = 0.0f;
+ newOperand.zeroPoint = 0;
+
+ SymmPerChannelQuantParams channelQuant;
+ channelQuant.channelDim = 0;
+ channelQuant.scales = std::vector<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->set<OperandExtraParams::Tag::channelQuant>(
+ std::move(channelQuant));
+ } break;
+ default:
+ break;
+ }
+ *operand = newOperand;
+}
+
+static bool mutateOperationOperandTypeSkip(size_t operand, OperandType type, const Model& model) {
+ if (type == model.main.operands[operand].type) {
+ return true;
+ }
+ for (const Operation& operation : model.main.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_ASYMM_SIGNED|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
+ // - AXIS_ALIGNED_BBOX_TRANSFORM bounding boxes (arg 1) can be of
+ // TENSOR_QUANT8_ASYMM or TENSOR_QUANT8_ASYMM_SIGNED.
+ // - RANK's input can have any TENSOR_* type.
+ 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 ||
+ type == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
+ return true;
+ }
+ } break;
+ case OperationType::QUANTIZE: {
+ if (operand == operation.inputs[0] &&
+ (type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32)) {
+ return true;
+ }
+ if (operand == operation.outputs[0] &&
+ (type == OperandType::TENSOR_QUANT8_ASYMM ||
+ type == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)) {
+ return true;
+ }
+ } break;
+ 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_ASYMM_SIGNED ||
+ 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;
+ case OperationType::AXIS_ALIGNED_BBOX_TRANSFORM: {
+ if (operand == operation.inputs[1] &&
+ (type == OperandType::TENSOR_QUANT8_ASYMM ||
+ type == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)) {
+ return true;
+ }
+ } break;
+ case OperationType::RANK: {
+ if (operand == operation.inputs[0] &&
+ (type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32 ||
+ type == OperandType::TENSOR_INT32 ||
+ type == OperandType::TENSOR_QUANT8_ASYMM ||
+ type == OperandType::TENSOR_QUANT16_SYMM ||
+ type == OperandType::TENSOR_BOOL8 ||
+ type == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL ||
+ type == OperandType::TENSOR_QUANT16_ASYMM ||
+ type == OperandType::TENSOR_QUANT8_SYMM ||
+ type == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)) {
+ return true;
+ }
+ } break;
+ default:
+ break;
+ }
+ }
+ return false;
+}
+
+static void mutateOperationOperandTypeTest(const std::shared_ptr<IDevice>& device,
+ const Model& model) {
+ for (size_t operand = 0; operand < model.main.operands.size(); ++operand) {
+ for (OperandType invalidOperandType : ndk::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, ExecutionPreference*, Priority*) {
+ mutateOperand(&model->main.operands[operand], invalidOperandType);
+ });
+ }
+ }
+}
+
+///////////////////////// VALIDATE MODEL OPERATION TYPE /////////////////////////
+
+static const int32_t invalidOperationTypes[] = {
+ -1,
+ static_cast<int32_t>(*(ndk::enum_range<OperationType>().end() - 1)) + 1,
+};
+
+static void mutateOperationTypeTest(const std::shared_ptr<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.main.operations.size(); ++operation) {
+ for (int32_t invalidOperationType : invalidOperationTypes) {
+ const std::string message = "mutateOperationTypeTest: operation " +
+ std::to_string(operation) + " set to value " +
+ std::to_string(invalidOperationType);
+ validate(device, message, model,
+ [operation, invalidOperationType](Model* model, ExecutionPreference*,
+ Priority*) {
+ model->main.operations[operation].type =
+ static_cast<OperationType>(invalidOperationType);
+ });
+ }
+ }
+}
+
+///////////////////////// VALIDATE MODEL OPERATION INPUT OPERAND INDEX /////////////////////////
+
+static void mutateOperationInputOperandIndexTest(const std::shared_ptr<IDevice>& device,
+ const Model& model) {
+ for (size_t operation = 0; operation < model.main.operations.size(); ++operation) {
+ const uint32_t invalidOperand = model.main.operands.size();
+ for (size_t input = 0; input < model.main.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, ExecutionPreference*,
+ Priority*) {
+ model->main.operations[operation].inputs[input] = invalidOperand;
+ });
+ }
+ }
+}
+
+///////////////////////// VALIDATE MODEL OPERATION OUTPUT OPERAND INDEX /////////////////////////
+
+static void mutateOperationOutputOperandIndexTest(const std::shared_ptr<IDevice>& device,
+ const Model& model) {
+ for (size_t operation = 0; operation < model.main.operations.size(); ++operation) {
+ const uint32_t invalidOperand = model.main.operands.size();
+ for (size_t output = 0; output < model.main.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, ExecutionPreference*,
+ Priority*) {
+ model->main.operations[operation].outputs[output] = invalidOperand;
+ });
+ }
+ }
+}
+
+///////////////////////// VALIDATE MODEL OPERANDS WRITTEN ///////////////////////////////////////
+
+static void mutateOperationRemoveWriteTest(const std::shared_ptr<IDevice>& device,
+ const Model& model,
+ const std::vector<uint32_t>& numberOfConsumers) {
+ for (size_t operation = 0; operation < model.main.operations.size(); ++operation) {
+ for (size_t outputNum = 0; outputNum < model.main.operations[operation].outputs.size();
+ ++outputNum) {
+ const uint32_t outputOperandIndex = model.main.operations[operation].outputs[outputNum];
+ if (numberOfConsumers[outputOperandIndex] > 0) {
+ const std::string message = "mutateOperationRemoveWriteTest: operation " +
+ std::to_string(operation) + " writes to " +
+ std::to_string(outputOperandIndex);
+ validate(device, message, model,
+ [operation, outputNum](Model* model, ExecutionPreference*, Priority*) {
+ int32_t& outputOperandIndex =
+ model->main.operations[operation].outputs[outputNum];
+ Operand operandValue = model->main.operands[outputOperandIndex];
+ if (operandValue.lifetime == OperandLifeTime::SUBGRAPH_OUTPUT) {
+ operandValue.lifetime = OperandLifeTime::TEMPORARY_VARIABLE;
+ } else {
+ ASSERT_EQ(operandValue.lifetime,
+ OperandLifeTime::TEMPORARY_VARIABLE);
+ }
+ outputOperandIndex = model->main.operands.size();
+ model->main.operands.push_back(operandValue);
+ });
+ }
+ }
+ }
+}
+
+///////////////////////// REMOVE OPERAND FROM EVERYTHING /////////////////////////
+
+static void removeValueAndDecrementGreaterValues(std::vector<int32_t>* vec, uint32_t value) {
+ if (vec) {
+ // remove elements matching "value"
+ vec->erase(std::remove(vec->begin(), vec->end(), value), vec->end());
+
+ // 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) {
+ model->main.operands.erase(model->main.operands.begin() + index);
+ for (Operation& operation : model->main.operations) {
+ removeValueAndDecrementGreaterValues(&operation.inputs, index);
+ removeValueAndDecrementGreaterValues(&operation.outputs, index);
+ }
+ removeValueAndDecrementGreaterValues(&model->main.inputIndexes, index);
+ removeValueAndDecrementGreaterValues(&model->main.outputIndexes, index);
+}
+
+static bool removeOperandSkip(size_t operandIndex, const Model& model,
+ const std::vector<uint32_t>& numberOfConsumers) {
+ if (numberOfConsumers[operandIndex] == 0) {
+ // Removing an unused operand has no effect.
+ return true;
+ }
+ for (const Operation& operation : model.main.operations) {
+ // Skip removeOperandTest for the following operations.
+ // - SPLIT's outputs are not checked during prepareModel.
+ if (operation.type == OperationType::SPLIT) {
+ for (const size_t index : operation.outputs) {
+ if (index == operandIndex) {
+ return true;
+ }
+ }
+ }
+ // BIDIRECTIONAL_SEQUENCE_LSTM and BIDIRECTIONAL_SEQUENCE_RNN can have
+ // either one, two, three or four outputs depending on their
+ // mergeOutputs parameter and if state outputs are provided.
+ // UNIDIRECTIONAL_SEQUENCE_LSTM and UNIDIRECTIONAL_SEQUENCE_RNN can have
+ // either one or three outputs depending on whether state outputs are
+ // provided.
+ if (operation.type == OperationType::UNIDIRECTIONAL_SEQUENCE_LSTM ||
+ operation.type == OperationType::UNIDIRECTIONAL_SEQUENCE_RNN ||
+ operation.type == OperationType::BIDIRECTIONAL_SEQUENCE_LSTM ||
+ operation.type == OperationType::BIDIRECTIONAL_SEQUENCE_RNN) {
+ for (const size_t index : operation.outputs) {
+ if (index == operandIndex) {
+ return true;
+ }
+ }
+ }
+ }
+ return false;
+}
+
+static void removeOperandTest(const std::shared_ptr<IDevice>& device, const Model& model,
+ const std::vector<uint32_t>& numberOfConsumers) {
+ for (size_t operand = 0; operand < model.main.operands.size(); ++operand) {
+ if (removeOperandSkip(operand, model, numberOfConsumers)) {
+ continue;
+ }
+ const std::string message = "removeOperandTest: operand " + std::to_string(operand);
+ validate(device, message, model, [operand](Model* model, ExecutionPreference*, Priority*) {
+ removeOperand(model, operand);
+ });
+ }
+}
+
+///////////////////////// REMOVE OPERATION /////////////////////////
+
+static void removeOperation(Model* model, uint32_t index) {
+ auto& operations = model->main.operations;
+ operations.erase(operations.begin() + index);
+}
+
+static void removeOperationTest(const std::shared_ptr<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.main.operations.size(); ++operation) {
+ const std::string message = "removeOperationTest: operation " + std::to_string(operation);
+ validate(device, message, model,
+ [operation](Model* model, ExecutionPreference*, Priority*) {
+ 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.
+ // RESIZE_BILINEAR and RESIZE_NEAREST_NEIGHBOR can have optional
+ // align_corners and half_pixel_centers parameters.
+ // - 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::RESIZE_NEAREST_NEIGHBOR: {
+ if (op.inputs.size() >= 5 && 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 std::shared_ptr<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.main.operations.size(); ++operation) {
+ for (size_t input = 0; input < model.main.operations[operation].inputs.size(); ++input) {
+ const Operation& op = model.main.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, ExecutionPreference*, Priority*) {
+ auto& inputs = model->main.operations[operation].inputs;
+ inputs.erase(inputs.begin() + input);
+ });
+ }
+ }
+}
+
+///////////////////////// REMOVE OPERATION OUTPUT /////////////////////////
+
+static void removeOperationOutputTest(const std::shared_ptr<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.main.operations.size(); ++operation) {
+ for (size_t output = 0; output < model.main.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, ExecutionPreference*, Priority*) {
+ auto& outputs = model->main.operations[operation].outputs;
+ outputs.erase(outputs.begin() + 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) ||
+ (op.type == OperationType::RESIZE_BILINEAR && op.inputs.size() < 6) ||
+ (op.type == OperationType::RESIZE_NEAREST_NEIGHBOR && op.inputs.size() < 6)) {
+ return true;
+ }
+ return false;
+}
+
+static void addOperationInputTest(const std::shared_ptr<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.main.operations.size(); ++operation) {
+ if (addOperationInputSkip(model.main.operations[operation])) {
+ continue;
+ }
+ const std::string message = "addOperationInputTest: operation " + std::to_string(operation);
+ validate(device, message, model,
+ [operation](Model* model, ExecutionPreference*, Priority*) {
+ uint32_t index = addOperand(model, OperandLifeTime::SUBGRAPH_INPUT);
+ model->main.operations[operation].inputs.push_back(index);
+ model->main.inputIndexes.push_back(index);
+ });
+ }
+}
+
+///////////////////////// ADD OPERATION OUTPUT /////////////////////////
+
+static void addOperationOutputTest(const std::shared_ptr<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.main.operations.size(); ++operation) {
+ const std::string message =
+ "addOperationOutputTest: operation " + std::to_string(operation);
+ validate(device, message, model,
+ [operation](Model* model, ExecutionPreference*, Priority*) {
+ uint32_t index = addOperand(model, OperandLifeTime::SUBGRAPH_OUTPUT);
+ model->main.operations[operation].outputs.push_back(index);
+ model->main.outputIndexes.push_back(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 std::shared_ptr<IDevice>& device,
+ const Model& model) {
+ for (int32_t invalidPreference : invalidExecutionPreferences) {
+ const std::string message =
+ "mutateExecutionPreferenceTest: preference " + std::to_string(invalidPreference);
+ validate(device, message, model,
+ [invalidPreference](Model*, ExecutionPreference* preference, Priority*) {
+ *preference = static_cast<ExecutionPreference>(invalidPreference);
+ });
+ }
+}
+
+///////////////////////// VALIDATE PRIORITY /////////////////////////
+
+static const int32_t invalidPriorities[] = {
+ static_cast<int32_t>(Priority::LOW) - 1, // lower bound
+ static_cast<int32_t>(Priority::HIGH) + 1, // upper bound
+};
+
+static void mutateExecutionPriorityTest(const std::shared_ptr<IDevice>& device,
+ const Model& model) {
+ for (int32_t invalidPriority : invalidPriorities) {
+ const std::string message =
+ "mutatePriorityTest: priority " + std::to_string(invalidPriority);
+ validate(device, message, model,
+ [invalidPriority](Model*, ExecutionPreference*, Priority* priority) {
+ *priority = static_cast<Priority>(invalidPriority);
+ });
+ }
+}
+
+////////////////////////// ENTRY POINT //////////////////////////////
+
+void validateModel(const std::shared_ptr<IDevice>& device, const Model& model) {
+ const auto numberOfConsumers = nn::countNumberOfConsumers(
+ model.main.operands.size(), nn::convert(model.main.operations).value());
+ mutateExecutionOrderTest(device, model, numberOfConsumers);
+ mutateOperandTypeTest(device, model);
+ mutateOperandRankTest(device, model);
+ mutateOperandScaleTest(device, model);
+ mutateOperandZeroPointTest(device, model);
+ mutateOperandLifeTimeTest(device, model);
+ mutateOperandInputOutputTest(device, model);
+ mutateOperandAddWriterTest(device, model);
+ mutateOperationOperandTypeTest(device, model);
+ mutateOperationTypeTest(device, model);
+ mutateOperationInputOperandIndexTest(device, model);
+ mutateOperationOutputOperandIndexTest(device, model);
+ mutateOperationRemoveWriteTest(device, model, numberOfConsumers);
+ removeOperandTest(device, model, numberOfConsumers);
+ removeOperationTest(device, model);
+ removeOperationInputTest(device, model);
+ removeOperationOutputTest(device, model);
+ addOperationInputTest(device, model);
+ addOperationOutputTest(device, model);
+ mutateExecutionPreferenceTest(device, model);
+ mutateExecutionPriorityTest(device, model);
+}
+
+} // namespace aidl::android::hardware::neuralnetworks::vts::functional
diff --git a/neuralnetworks/aidl/vts/functional/ValidateRequest.cpp b/neuralnetworks/aidl/vts/functional/ValidateRequest.cpp
new file mode 100644
index 0000000..db8f429
--- /dev/null
+++ b/neuralnetworks/aidl/vts/functional/ValidateRequest.cpp
@@ -0,0 +1,126 @@
+/*
+ * Copyright (C) 2021 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_aidl_hal_test"
+
+#include <android/binder_auto_utils.h>
+
+#include <chrono>
+
+#include <TestHarness.h>
+#include <nnapi/hal/aidl/Utils.h>
+
+#include "Callbacks.h"
+#include "GeneratedTestHarness.h"
+#include "Utils.h"
+#include "VtsHalNeuralnetworks.h"
+
+namespace aidl::android::hardware::neuralnetworks::vts::functional {
+
+using ExecutionMutation = std::function<void(Request*)>;
+
+///////////////////////// UTILITY FUNCTIONS /////////////////////////
+
+// 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.
+static void validate(const std::shared_ptr<IPreparedModel>& preparedModel,
+ const std::string& message, const Request& originalRequest,
+ const ExecutionMutation& mutate) {
+ Request request = utils::clone(originalRequest).value();
+ mutate(&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;
+ };
+ bool measure = (hash & 1);
+
+ // synchronous
+ {
+ SCOPED_TRACE(message + " [executeSynchronously]");
+ ExecutionResult executionResult;
+ const auto executeStatus = preparedModel->executeSynchronously(
+ request, measure, kNoDeadline, kOmittedTimeoutDuration, &executionResult);
+ ASSERT_FALSE(executeStatus.isOk());
+ ASSERT_EQ(executeStatus.getExceptionCode(), EX_SERVICE_SPECIFIC);
+ ASSERT_EQ(static_cast<ErrorStatus>(executeStatus.getServiceSpecificError()),
+ ErrorStatus::INVALID_ARGUMENT);
+ }
+
+ // fenced
+ {
+ SCOPED_TRACE(message + " [executeFenced]");
+ ndk::ScopedFileDescriptor syncFence;
+ std::shared_ptr<IFencedExecutionCallback> callback;
+ const auto executeStatus = preparedModel->executeFenced(request, {}, false, kNoDeadline,
+ kOmittedTimeoutDuration,
+ kNoDuration, &syncFence, &callback);
+ ASSERT_FALSE(executeStatus.isOk());
+ ASSERT_EQ(executeStatus.getExceptionCode(), EX_SERVICE_SPECIFIC);
+ ASSERT_EQ(static_cast<ErrorStatus>(executeStatus.getServiceSpecificError()),
+ ErrorStatus::INVALID_ARGUMENT);
+ }
+}
+
+///////////////////////// REMOVE INPUT ////////////////////////////////////
+
+static void removeInputTest(const std::shared_ptr<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) {
+ request->inputs.erase(request->inputs.begin() + input);
+ });
+ }
+}
+
+///////////////////////// REMOVE OUTPUT ////////////////////////////////////
+
+static void removeOutputTest(const std::shared_ptr<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) {
+ request->outputs.erase(request->outputs.begin() + output);
+ });
+ }
+}
+
+///////////////////////////// ENTRY POINT //////////////////////////////////
+
+void validateRequest(const std::shared_ptr<IPreparedModel>& preparedModel, const Request& request) {
+ removeInputTest(preparedModel, request);
+ removeOutputTest(preparedModel, request);
+}
+
+void validateRequestFailure(const std::shared_ptr<IPreparedModel>& preparedModel,
+ const Request& request) {
+ SCOPED_TRACE("Expecting request to fail [executeSynchronously]");
+ ExecutionResult executionResult;
+ const auto executeStatus = preparedModel->executeSynchronously(
+ request, false, kNoDeadline, kOmittedTimeoutDuration, &executionResult);
+
+ ASSERT_FALSE(executeStatus.isOk());
+ ASSERT_EQ(executeStatus.getExceptionCode(), EX_SERVICE_SPECIFIC);
+ ASSERT_NE(static_cast<ErrorStatus>(executeStatus.getServiceSpecificError()), ErrorStatus::NONE);
+}
+
+} // namespace aidl::android::hardware::neuralnetworks::vts::functional
diff --git a/neuralnetworks/aidl/vts/functional/VtsHalNeuralnetworks.cpp b/neuralnetworks/aidl/vts/functional/VtsHalNeuralnetworks.cpp
new file mode 100644
index 0000000..2d91b8e
--- /dev/null
+++ b/neuralnetworks/aidl/vts/functional/VtsHalNeuralnetworks.cpp
@@ -0,0 +1,194 @@
+/*
+ * Copyright (C) 2021 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_aidl_hal_test"
+#include "VtsHalNeuralnetworks.h"
+
+#include <android-base/logging.h>
+#include <android/binder_auto_utils.h>
+#include <android/binder_interface_utils.h>
+#include <android/binder_manager.h>
+#include <android/binder_status.h>
+#include <gtest/gtest.h>
+#include <memory>
+#include <string>
+#include <utility>
+
+#include <TestHarness.h>
+#include <aidl/Vintf.h>
+#include <nnapi/hal/aidl/Conversions.h>
+
+#include "Callbacks.h"
+#include "GeneratedTestHarness.h"
+#include "Utils.h"
+
+namespace aidl::android::hardware::neuralnetworks::vts::functional {
+
+using implementation::PreparedModelCallback;
+
+// internal helper function
+void createPreparedModel(const std::shared_ptr<IDevice>& device, const Model& model,
+ std::shared_ptr<IPreparedModel>* preparedModel, bool reportSkipping) {
+ ASSERT_NE(nullptr, preparedModel);
+ *preparedModel = nullptr;
+
+ // see if service can handle model
+ std::vector<bool> supportedOperations;
+ const auto supportedCallStatus = device->getSupportedOperations(model, &supportedOperations);
+ ASSERT_TRUE(supportedCallStatus.isOk());
+ ASSERT_NE(0ul, supportedOperations.size());
+ const bool fullySupportsModel = std::all_of(
+ supportedOperations.begin(), supportedOperations.end(), [](bool v) { return v; });
+
+ // launch prepare model
+ const std::shared_ptr<PreparedModelCallback> preparedModelCallback =
+ ndk::SharedRefBase::make<PreparedModelCallback>();
+ const auto prepareLaunchStatus =
+ device->prepareModel(model, ExecutionPreference::FAST_SINGLE_ANSWER, kDefaultPriority,
+ kNoDeadline, {}, {}, kEmptyCacheToken, preparedModelCallback);
+ ASSERT_TRUE(prepareLaunchStatus.isOk()) << prepareLaunchStatus.getDescription();
+
+ // retrieve prepared model
+ preparedModelCallback->wait();
+ const ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
+ *preparedModel = preparedModelCallback->getPreparedModel();
+
+ // The getSupportedOperations call returns a list of operations that are guaranteed not to fail
+ // if prepareModel is called, and 'fullySupportsModel' is true i.f.f. the entire model is
+ // guaranteed. If a driver has any doubt that it can prepare an operation, it must return false.
+ // So here, if a driver isn't sure if it can support an operation, but reports that it
+ // successfully prepared the model, the test can continue.
+ if (!fullySupportsModel && prepareReturnStatus != ErrorStatus::NONE) {
+ ASSERT_EQ(nullptr, preparedModel->get());
+ if (!reportSkipping) {
+ return;
+ }
+ 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 NeuralNetworksAidlTest::SetUp() {
+ testing::TestWithParam<NeuralNetworksAidlTestParam>::SetUp();
+ ASSERT_NE(kDevice, nullptr);
+}
+
+static NamedDevice makeNamedDevice(const std::string& name) {
+ ndk::SpAIBinder binder(AServiceManager_getService(name.c_str()));
+ return {name, IDevice::fromBinder(binder)};
+}
+
+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 = ::android::getAidlHalInstanceNames(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 printNeuralNetworksAidlTest(
+ const testing::TestParamInfo<NeuralNetworksAidlTestParam>& info) {
+ return gtestCompliantName(getName(info.param));
+}
+
+INSTANTIATE_DEVICE_TEST(NeuralNetworksAidlTest);
+
+// Forward declaration from ValidateModel.cpp
+void validateModel(const std::shared_ptr<IDevice>& device, const Model& model);
+// Forward declaration from ValidateRequest.cpp
+void validateRequest(const std::shared_ptr<IPreparedModel>& preparedModel, const Request& request);
+// Forward declaration from ValidateRequest.cpp
+void validateRequestFailure(const std::shared_ptr<IPreparedModel>& preparedModel,
+ const Request& request);
+
+void validateEverything(const std::shared_ptr<IDevice>& device, const Model& model,
+ const Request& request) {
+ validateModel(device, model);
+
+ // Create IPreparedModel.
+ std::shared_ptr<IPreparedModel> preparedModel;
+ createPreparedModel(device, model, &preparedModel);
+ if (preparedModel == nullptr) return;
+
+ validateRequest(preparedModel, request);
+ // HIDL also had test that expected executeFenced to fail on received null fd (-1). This is not
+ // allowed in AIDL and will result in EX_TRANSACTION_FAILED.
+}
+
+void validateFailure(const std::shared_ptr<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.
+ std::shared_ptr<IPreparedModel> preparedModel;
+ createPreparedModel(device, model, &preparedModel);
+ if (preparedModel == nullptr) return;
+
+ validateRequestFailure(preparedModel, request);
+}
+
+TEST_P(ValidationTest, Test) {
+ const Model model = createModel(kTestModel);
+ ExecutionContext context;
+ const Request request = context.createRequest(kTestModel);
+ if (kTestModel.expectFailure) {
+ validateFailure(kDevice, model, request);
+ } else {
+ validateEverything(kDevice, model, request);
+ }
+}
+
+INSTANTIATE_GENERATED_TEST(ValidationTest, [](const std::string& testName) {
+ // Skip validation for the "inputs_as_internal" and "all_tensors_as_inputs"
+ // generated tests.
+ return testName.find("inputs_as_internal") == std::string::npos &&
+ testName.find("all_tensors_as_inputs") == std::string::npos;
+});
+
+std::string toString(Executor executor) {
+ switch (executor) {
+ case Executor::ASYNC:
+ return "ASYNC";
+ case Executor::SYNC:
+ return "SYNC";
+ case Executor::BURST:
+ return "BURST";
+ case Executor::FENCED:
+ return "FENCED";
+ default:
+ CHECK(false);
+ }
+}
+
+} // namespace aidl::android::hardware::neuralnetworks::vts::functional
diff --git a/neuralnetworks/aidl/vts/functional/VtsHalNeuralnetworks.h b/neuralnetworks/aidl/vts/functional/VtsHalNeuralnetworks.h
new file mode 100644
index 0000000..9b81ee1
--- /dev/null
+++ b/neuralnetworks/aidl/vts/functional/VtsHalNeuralnetworks.h
@@ -0,0 +1,61 @@
+/*
+ * Copyright (C) 2021 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_AIDL_VTS_HAL_NEURALNETWORKS_H
+#define ANDROID_HARDWARE_NEURALNETWORKS_AIDL_VTS_HAL_NEURALNETWORKS_H
+
+#include <gtest/gtest.h>
+#include <vector>
+
+#include <aidl/android/hardware/neuralnetworks/IDevice.h>
+
+#include "Callbacks.h"
+#include "Utils.h"
+
+namespace aidl::android::hardware::neuralnetworks::vts::functional {
+
+using NamedDevice = Named<std::shared_ptr<IDevice>>;
+using NeuralNetworksAidlTestParam = NamedDevice;
+
+class NeuralNetworksAidlTest : public testing::TestWithParam<NeuralNetworksAidlTestParam> {
+ protected:
+ void SetUp() override;
+ const std::shared_ptr<IDevice> kDevice = getData(GetParam());
+};
+
+const std::vector<NamedDevice>& getNamedDevices();
+
+std::string printNeuralNetworksAidlTest(
+ const testing::TestParamInfo<NeuralNetworksAidlTestParam>& info);
+
+#define INSTANTIATE_DEVICE_TEST(TestSuite) \
+ GTEST_ALLOW_UNINSTANTIATED_PARAMETERIZED_TEST(TestSuite); \
+ INSTANTIATE_TEST_SUITE_P(PerInstance, TestSuite, testing::ValuesIn(getNamedDevices()), \
+ printNeuralNetworksAidlTest)
+
+// Create an IPreparedModel object. If the model cannot be prepared,
+// "preparedModel" will be nullptr instead.
+void createPreparedModel(const std::shared_ptr<IDevice>& device, const Model& model,
+ std::shared_ptr<IPreparedModel>* preparedModel,
+ bool reportSkipping = true);
+
+enum class Executor { ASYNC, SYNC, BURST, FENCED };
+
+std::string toString(Executor executor);
+
+} // namespace aidl::android::hardware::neuralnetworks::vts::functional
+
+#endif // ANDROID_HARDWARE_NEURALNETWORKS_AIDL_VTS_HAL_NEURALNETWORKS_H
diff --git a/neuralnetworks/utils/common/include/nnapi/hal/CommonUtils.h b/neuralnetworks/utils/common/include/nnapi/hal/CommonUtils.h
index b3989e5..fef9d9c 100644
--- a/neuralnetworks/utils/common/include/nnapi/hal/CommonUtils.h
+++ b/neuralnetworks/utils/common/include/nnapi/hal/CommonUtils.h
@@ -24,15 +24,21 @@
#include <functional>
#include <vector>
-// Shorthand
+// Shorthands
namespace android::hardware::neuralnetworks {
namespace hal = ::android::hardware::neuralnetworks;
} // namespace android::hardware::neuralnetworks
-// Shorthand
+// Shorthands
+namespace aidl::android::hardware::neuralnetworks {
+namespace aidl_hal = ::aidl::android::hardware::neuralnetworks;
+} // namespace aidl::android::hardware::neuralnetworks
+
+// Shorthands
namespace android::nn {
namespace hal = ::android::hardware::neuralnetworks;
-}
+namespace aidl_hal = ::aidl::android::hardware::neuralnetworks;
+} // namespace android::nn
namespace android::hardware::neuralnetworks::utils {
diff --git a/power/stats/aidl/Android.bp b/power/stats/aidl/Android.bp
new file mode 100644
index 0000000..1688b12
--- /dev/null
+++ b/power/stats/aidl/Android.bp
@@ -0,0 +1,35 @@
+// Copyright (C) 2020 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.
+
+aidl_interface {
+ name: "android.hardware.power.stats",
+ vendor_available: true,
+ srcs: [
+ "android/hardware/power/stats/*.aidl",
+ ],
+ stability: "vintf",
+ backend: {
+ java: {
+ platform_apis: true,
+ },
+ ndk: {
+ vndk: {
+ enabled: true,
+ },
+ },
+ cpp: {
+ enabled: false,
+ },
+ },
+}
diff --git a/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/Channel.aidl b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/Channel.aidl
new file mode 100644
index 0000000..e0c372b
--- /dev/null
+++ b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/Channel.aidl
@@ -0,0 +1,39 @@
+/*
+ * Copyright (C) 2020 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.
+ *////////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL file. Do not edit it manually. There are
+// two cases:
+// 1). this is a frozen version file - do not edit this in any case.
+// 2). this is a 'current' file. If you make a backwards compatible change to
+// the interface (from the latest frozen version), the build system will
+// prompt you to update this file with `m <name>-update-api`.
+//
+// You must not make a backward incompatible change to any AIDL file built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.power.stats;
+@VintfStability
+parcelable Channel {
+ int id;
+ @utf8InCpp String name;
+ @utf8InCpp String subsystem;
+}
diff --git a/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/EnergyConsumer.aidl b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/EnergyConsumer.aidl
new file mode 100644
index 0000000..c8d7645
--- /dev/null
+++ b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/EnergyConsumer.aidl
@@ -0,0 +1,40 @@
+/*
+ * Copyright (C) 2020 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.
+ *////////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL file. Do not edit it manually. There are
+// two cases:
+// 1). this is a frozen version file - do not edit this in any case.
+// 2). this is a 'current' file. If you make a backwards compatible change to
+// the interface (from the latest frozen version), the build system will
+// prompt you to update this file with `m <name>-update-api`.
+//
+// You must not make a backward incompatible change to any AIDL file built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.power.stats;
+@VintfStability
+parcelable EnergyConsumer {
+ int id;
+ int ordinal;
+ android.hardware.power.stats.EnergyConsumerType type;
+ @utf8InCpp String name;
+}
diff --git a/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/EnergyConsumerAttribution.aidl b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/EnergyConsumerAttribution.aidl
new file mode 100644
index 0000000..2fdc1a5
--- /dev/null
+++ b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/EnergyConsumerAttribution.aidl
@@ -0,0 +1,38 @@
+/*
+ * Copyright (C) 2020 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.
+ *////////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL file. Do not edit it manually. There are
+// two cases:
+// 1). this is a frozen version file - do not edit this in any case.
+// 2). this is a 'current' file. If you make a backwards compatible change to
+// the interface (from the latest frozen version), the build system will
+// prompt you to update this file with `m <name>-update-api`.
+//
+// You must not make a backward incompatible change to any AIDL file built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.power.stats;
+@VintfStability
+parcelable EnergyConsumerAttribution {
+ int uid;
+ long energyUWs;
+}
diff --git a/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/EnergyConsumerResult.aidl b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/EnergyConsumerResult.aidl
new file mode 100644
index 0000000..4937aef
--- /dev/null
+++ b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/EnergyConsumerResult.aidl
@@ -0,0 +1,40 @@
+/*
+ * Copyright (C) 2020 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.
+ *////////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL file. Do not edit it manually. There are
+// two cases:
+// 1). this is a frozen version file - do not edit this in any case.
+// 2). this is a 'current' file. If you make a backwards compatible change to
+// the interface (from the latest frozen version), the build system will
+// prompt you to update this file with `m <name>-update-api`.
+//
+// You must not make a backward incompatible change to any AIDL file built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.power.stats;
+@VintfStability
+parcelable EnergyConsumerResult {
+ int id;
+ long timestampMs;
+ long energyUWs;
+ android.hardware.power.stats.EnergyConsumerAttribution[] attribution;
+}
diff --git a/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/EnergyConsumerType.aidl b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/EnergyConsumerType.aidl
new file mode 100644
index 0000000..7b05d2f
--- /dev/null
+++ b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/EnergyConsumerType.aidl
@@ -0,0 +1,39 @@
+/*
+ * Copyright (C) 2020 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.
+ *////////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL file. Do not edit it manually. There are
+// two cases:
+// 1). this is a frozen version file - do not edit this in any case.
+// 2). this is a 'current' file. If you make a backwards compatible change to
+// the interface (from the latest frozen version), the build system will
+// prompt you to update this file with `m <name>-update-api`.
+//
+// You must not make a backward incompatible change to any AIDL file built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.power.stats;
+@VintfStability
+enum EnergyConsumerType {
+ OTHER = 0,
+ CPU_CLUSTER = 1,
+ DISPLAY = 2,
+}
diff --git a/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/EnergyMeasurement.aidl b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/EnergyMeasurement.aidl
new file mode 100644
index 0000000..2d6ae16
--- /dev/null
+++ b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/EnergyMeasurement.aidl
@@ -0,0 +1,40 @@
+/*
+ * Copyright (C) 2020 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.
+ *////////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL file. Do not edit it manually. There are
+// two cases:
+// 1). this is a frozen version file - do not edit this in any case.
+// 2). this is a 'current' file. If you make a backwards compatible change to
+// the interface (from the latest frozen version), the build system will
+// prompt you to update this file with `m <name>-update-api`.
+//
+// You must not make a backward incompatible change to any AIDL file built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.power.stats;
+@VintfStability
+parcelable EnergyMeasurement {
+ int id;
+ long timestampMs;
+ long durationMs;
+ long energyUWs;
+}
diff --git a/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/IPowerStats.aidl b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/IPowerStats.aidl
new file mode 100644
index 0000000..5b11695
--- /dev/null
+++ b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/IPowerStats.aidl
@@ -0,0 +1,42 @@
+/*
+ * Copyright (C) 2020 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.
+ *////////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL file. Do not edit it manually. There are
+// two cases:
+// 1). this is a frozen version file - do not edit this in any case.
+// 2). this is a 'current' file. If you make a backwards compatible change to
+// the interface (from the latest frozen version), the build system will
+// prompt you to update this file with `m <name>-update-api`.
+//
+// You must not make a backward incompatible change to any AIDL file built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.power.stats;
+@VintfStability
+interface IPowerStats {
+ android.hardware.power.stats.PowerEntity[] getPowerEntityInfo();
+ android.hardware.power.stats.StateResidencyResult[] getStateResidency(in int[] powerEntityIds);
+ android.hardware.power.stats.EnergyConsumer[] getEnergyConsumerInfo();
+ android.hardware.power.stats.EnergyConsumerResult[] getEnergyConsumed(in int[] energyConsumerIds);
+ android.hardware.power.stats.Channel[] getEnergyMeterInfo();
+ android.hardware.power.stats.EnergyMeasurement[] readEnergyMeter(in int[] channelIds);
+}
diff --git a/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/PowerEntity.aidl b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/PowerEntity.aidl
new file mode 100644
index 0000000..38a62ac
--- /dev/null
+++ b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/PowerEntity.aidl
@@ -0,0 +1,39 @@
+/*
+ * Copyright (C) 2020 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.
+ *////////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL file. Do not edit it manually. There are
+// two cases:
+// 1). this is a frozen version file - do not edit this in any case.
+// 2). this is a 'current' file. If you make a backwards compatible change to
+// the interface (from the latest frozen version), the build system will
+// prompt you to update this file with `m <name>-update-api`.
+//
+// You must not make a backward incompatible change to any AIDL file built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.power.stats;
+@VintfStability
+parcelable PowerEntity {
+ int id;
+ @utf8InCpp String name;
+ android.hardware.power.stats.State[] states;
+}
diff --git a/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/State.aidl b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/State.aidl
new file mode 100644
index 0000000..1c5d7c1
--- /dev/null
+++ b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/State.aidl
@@ -0,0 +1,38 @@
+/*
+ * Copyright (C) 2020 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.
+ *////////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL file. Do not edit it manually. There are
+// two cases:
+// 1). this is a frozen version file - do not edit this in any case.
+// 2). this is a 'current' file. If you make a backwards compatible change to
+// the interface (from the latest frozen version), the build system will
+// prompt you to update this file with `m <name>-update-api`.
+//
+// You must not make a backward incompatible change to any AIDL file built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.power.stats;
+@VintfStability
+parcelable State {
+ int id;
+ @utf8InCpp String name;
+}
diff --git a/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/StateResidency.aidl b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/StateResidency.aidl
new file mode 100644
index 0000000..7982f02
--- /dev/null
+++ b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/StateResidency.aidl
@@ -0,0 +1,40 @@
+/*
+ * Copyright (C) 2020 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.
+ *////////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL file. Do not edit it manually. There are
+// two cases:
+// 1). this is a frozen version file - do not edit this in any case.
+// 2). this is a 'current' file. If you make a backwards compatible change to
+// the interface (from the latest frozen version), the build system will
+// prompt you to update this file with `m <name>-update-api`.
+//
+// You must not make a backward incompatible change to any AIDL file built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.power.stats;
+@VintfStability
+parcelable StateResidency {
+ int id;
+ long totalTimeInStateMs;
+ long totalStateEntryCount;
+ long lastEntryTimestampMs;
+}
diff --git a/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/StateResidencyResult.aidl b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/StateResidencyResult.aidl
new file mode 100644
index 0000000..5bc548e
--- /dev/null
+++ b/power/stats/aidl/aidl_api/android.hardware.power.stats/current/android/hardware/power/stats/StateResidencyResult.aidl
@@ -0,0 +1,38 @@
+/*
+ * Copyright (C) 2020 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.
+ *////////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL file. Do not edit it manually. There are
+// two cases:
+// 1). this is a frozen version file - do not edit this in any case.
+// 2). this is a 'current' file. If you make a backwards compatible change to
+// the interface (from the latest frozen version), the build system will
+// prompt you to update this file with `m <name>-update-api`.
+//
+// You must not make a backward incompatible change to any AIDL file built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.power.stats;
+@VintfStability
+parcelable StateResidencyResult {
+ int id;
+ android.hardware.power.stats.StateResidency[] stateResidencyData;
+}
diff --git a/power/stats/aidl/android/hardware/power/stats/Channel.aidl b/power/stats/aidl/android/hardware/power/stats/Channel.aidl
new file mode 100644
index 0000000..5e8962e
--- /dev/null
+++ b/power/stats/aidl/android/hardware/power/stats/Channel.aidl
@@ -0,0 +1,35 @@
+/*
+ * Copyright (C) 2020 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.power.stats;
+
+@VintfStability
+parcelable Channel {
+ /**
+ * Unique ID of this Channel
+ */
+ int id;
+ /**
+ * Unique name of this Channel. Vendor/device specific. Opaque to framework
+ */
+ @utf8InCpp String name;
+
+ /**
+ * Name of the subsystem associated with this Channel. Opaque to framework
+ */
+ @utf8InCpp String subsystem;
+}
+
diff --git a/power/stats/aidl/android/hardware/power/stats/EnergyConsumer.aidl b/power/stats/aidl/android/hardware/power/stats/EnergyConsumer.aidl
new file mode 100644
index 0000000..2ff1279
--- /dev/null
+++ b/power/stats/aidl/android/hardware/power/stats/EnergyConsumer.aidl
@@ -0,0 +1,41 @@
+/*
+ * Copyright (C) 2020 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.power.stats;
+
+import android.hardware.power.stats.EnergyConsumerType;
+
+@VintfStability
+parcelable EnergyConsumer {
+ /**
+ * Unique ID of this EnergyConsumer
+ */
+ int id;
+
+ /**
+ * For a group of EnergyConsumers of the same logical type, sorting by ordinal
+ * gives their physical order. Ordinals must be consecutive integers starting from 0.
+ */
+ int ordinal;
+
+ /* Type of this EnergyConsumer */
+ EnergyConsumerType type;
+
+ /**
+ * Unique name of this EnergyConsumer. Vendor/device specific. Opaque to framework
+ */
+ @utf8InCpp String name;
+}
\ No newline at end of file
diff --git a/power/stats/aidl/android/hardware/power/stats/EnergyConsumerAttribution.aidl b/power/stats/aidl/android/hardware/power/stats/EnergyConsumerAttribution.aidl
new file mode 100644
index 0000000..5767de1
--- /dev/null
+++ b/power/stats/aidl/android/hardware/power/stats/EnergyConsumerAttribution.aidl
@@ -0,0 +1,29 @@
+/*
+ * Copyright (C) 2020 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.power.stats;
+
+@VintfStability
+parcelable EnergyConsumerAttribution {
+ /**
+ * Android ID / Linux UID, the accumulated energy is attributed to
+ */
+ int uid;
+ /**
+ * Accumulated energy since boot in microwatt-seconds (uWs) for this AID
+ */
+ long energyUWs;
+}
diff --git a/power/stats/aidl/android/hardware/power/stats/EnergyConsumerResult.aidl b/power/stats/aidl/android/hardware/power/stats/EnergyConsumerResult.aidl
new file mode 100644
index 0000000..12d2042
--- /dev/null
+++ b/power/stats/aidl/android/hardware/power/stats/EnergyConsumerResult.aidl
@@ -0,0 +1,41 @@
+/*
+ * Copyright (C) 2020 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.power.stats;
+
+import android.hardware.power.stats.EnergyConsumerAttribution;
+
+@VintfStability
+parcelable EnergyConsumerResult {
+ /**
+ * ID of the EnergyConsumer associated with this result
+ */
+ int id;
+ /**
+ * Time since boot in milliseconds
+ */
+ long timestampMs;
+ /**
+ * Total accumulated energy since boot in microwatt-seconds (uWs)
+ */
+ long energyUWs;
+ /**
+ * Optional attributed energy per Android ID / Linux UID for this EnergyConsumer.
+ * Sum total of attributed energy must be less than or equal to total accumulated energy.
+ */
+ EnergyConsumerAttribution[] attribution;
+}
+
diff --git a/power/stats/aidl/android/hardware/power/stats/EnergyConsumerType.aidl b/power/stats/aidl/android/hardware/power/stats/EnergyConsumerType.aidl
new file mode 100644
index 0000000..7fd2348
--- /dev/null
+++ b/power/stats/aidl/android/hardware/power/stats/EnergyConsumerType.aidl
@@ -0,0 +1,25 @@
+/*
+ * Copyright (C) 2020 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.power.stats;
+
+/* Indicates the type of an energy consumer reported by the Power Stats HAL */
+@VintfStability
+enum EnergyConsumerType {
+ OTHER,
+ CPU_CLUSTER,
+ DISPLAY,
+}
\ No newline at end of file
diff --git a/power/stats/aidl/android/hardware/power/stats/EnergyMeasurement.aidl b/power/stats/aidl/android/hardware/power/stats/EnergyMeasurement.aidl
new file mode 100644
index 0000000..d3e8f46
--- /dev/null
+++ b/power/stats/aidl/android/hardware/power/stats/EnergyMeasurement.aidl
@@ -0,0 +1,38 @@
+/*
+ * Copyright (C) 2020 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.power.stats;
+
+@VintfStability
+parcelable EnergyMeasurement {
+ /**
+ * ID of the Channel associated with this measurement
+ */
+ int id;
+ /**
+ * Approximate time of data capture in millseconds since boot
+ */
+ long timestampMs;
+ /**
+ * Duration in milliseconds that energy has been accumulated
+ */
+ long durationMs;
+ /**
+ * Accumulated energy in microwatt-seconds (uWs)
+ */
+ long energyUWs;
+}
+
diff --git a/power/stats/aidl/android/hardware/power/stats/IPowerStats.aidl b/power/stats/aidl/android/hardware/power/stats/IPowerStats.aidl
new file mode 100644
index 0000000..7a95f74
--- /dev/null
+++ b/power/stats/aidl/android/hardware/power/stats/IPowerStats.aidl
@@ -0,0 +1,111 @@
+/*
+ * Copyright (C) 2020 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.power.stats;
+
+import android.hardware.power.stats.Channel;
+import android.hardware.power.stats.EnergyConsumer;
+import android.hardware.power.stats.EnergyConsumerResult;
+import android.hardware.power.stats.EnergyMeasurement;
+import android.hardware.power.stats.PowerEntity;
+import android.hardware.power.stats.StateResidencyResult;
+
+@VintfStability
+interface IPowerStats {
+ /**
+ * Return information related to all supported PowerEntity(s) for which state residency data
+ * is available.
+ *
+ * A PowerEntity is defined as a platform subsystem, peripheral, or power domain that impacts
+ * the total device power consumption.
+ *
+ * @return List of information on each PowerEntity
+ */
+ PowerEntity[] getPowerEntityInfo();
+
+ /**
+ * Reports the accumulated state residency for each requested PowerEntity.
+ *
+ * Each PowerEntity may reside in one of multiple states. It may also
+ * transition from one state to another. StateResidency is defined as
+ * an accumulation of time that a PowerEntity resided in each
+ * of its possible states, the number of times that each state was
+ * entered, and a timestamp corresponding to the last time that state
+ * was entered.
+ *
+ * Data is accumulated starting at device boot.
+ *
+ * @param powerEntityIds List of IDs of PowerEntities for which data is requested.
+ * Passing an empty list will return state residency for all available PowerEntitys.
+ * ID of each PowerEntity is contained in PowerEntityInfo.
+ *
+ * @return StateResidency since boot for each requested PowerEntity
+ *
+ * Returns the following service-specific exceptions in order of highest priority:
+ * - STATUS_BAD_VALUE if an invalid powerEntityId is provided
+ * - STATUS_FAILED_TRANSACTION if any StateResidencyResult fails to be returned
+ */
+ StateResidencyResult[] getStateResidency(in int[] powerEntityIds);
+
+ /**
+ * Return the list EnergyConsumers for which energy consumption data is available.
+ *
+ * An EnergyConsumer is a device subsystem or peripheral that consumes energy. Energy
+ * consumption data may be used by framework for the purpose of power attribution.
+ *
+ * @return List of EnergyConsumers that are available.
+ */
+ EnergyConsumer[] getEnergyConsumerInfo();
+
+ /**
+ * Reports the energy consumed since boot by each requested EnergyConsumer.
+ *
+ * @param energyConsumerIds List of IDs of EnergyConsumers for which data is requested.
+ * Passing an empty list will return state residency for all available EnergyConsumers.
+ *
+ * @return Energy consumed since boot for each requested EnergyConsumer
+ *
+ * Returns the following service-specific exceptions in order of highest priority:
+ * - STATUS_BAD_VALUE if an invalid energyConsumerId is provided
+ * - STATUS_FAILED_TRANSACTION if any EnergyConsumerResult fails to be returned
+ */
+ EnergyConsumerResult[] getEnergyConsumed(in int[] energyConsumerIds);
+
+ /**
+ * Return information related to all channels monitored by Energy Meters.
+ *
+ * An Energy Meter is a device that monitors energy and may support monitoring multiple
+ * channels simultaneously. A channel may correspond a bus, sense resistor, or power rail.
+ *
+ * @return Channels monitored by Energy Meters.
+ */
+ Channel[] getEnergyMeterInfo();
+
+ /**
+ * Reports accumulated energy for each specified channel.
+ *
+ * @param channelIds IDs of channels for which data is requested.
+ * Passing an empty list will return energy measurements for all available channels.
+ * ID of each channel is contained in ChannelInfo.
+ *
+ * @return Energy measured since boot for each requested channel
+ *
+ * Returns the following service-specific exceptions in order of highest priority:
+ * - STATUS_BAD_VALUE if an invalid channelId is provided
+ * - STATUS_FAILED_TRANSACTION if any EnergyMeasurement fails to be returned
+ */
+ EnergyMeasurement[] readEnergyMeter(in int[] channelIds);
+}
diff --git a/power/stats/aidl/android/hardware/power/stats/PowerEntity.aidl b/power/stats/aidl/android/hardware/power/stats/PowerEntity.aidl
new file mode 100644
index 0000000..6844a4c
--- /dev/null
+++ b/power/stats/aidl/android/hardware/power/stats/PowerEntity.aidl
@@ -0,0 +1,35 @@
+/*
+ * Copyright (C) 2020 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.power.stats;
+
+import android.hardware.power.stats.State;
+
+@VintfStability
+parcelable PowerEntity {
+ /**
+ * Unique ID of this PowerEntity
+ */
+ int id;
+ /**
+ * Unique name of this PowerEntity. Vendor/device specific. Opaque to framework
+ */
+ @utf8InCpp String name;
+ /**
+ * List of states that this PowerEntity may reside in
+ */
+ State[] states;
+}
\ No newline at end of file
diff --git a/power/stats/aidl/android/hardware/power/stats/State.aidl b/power/stats/aidl/android/hardware/power/stats/State.aidl
new file mode 100644
index 0000000..33a9f70
--- /dev/null
+++ b/power/stats/aidl/android/hardware/power/stats/State.aidl
@@ -0,0 +1,31 @@
+/*
+ * Copyright (C) 2020 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.power.stats;
+
+@VintfStability
+parcelable State {
+ /**
+ * Unique (for a given PowerEntity) ID of this State
+ */
+ int id;
+ /**
+ * Unique (for a given PowerEntity) name of the state. Vendor/device specific.
+ * Opaque to framework
+ */
+ @utf8InCpp String name;
+}
+
diff --git a/power/stats/aidl/android/hardware/power/stats/StateResidency.aidl b/power/stats/aidl/android/hardware/power/stats/StateResidency.aidl
new file mode 100644
index 0000000..4162517
--- /dev/null
+++ b/power/stats/aidl/android/hardware/power/stats/StateResidency.aidl
@@ -0,0 +1,41 @@
+/*
+ * Copyright (C) 2020 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.power.stats;
+
+/**
+ * Contains residency data for a single state
+ */
+@VintfStability
+parcelable StateResidency {
+ /**
+ * ID of the state associated with this residency
+ */
+ int id;
+ /**
+ * Total time in milliseconds that the corresponding PowerEntity resided
+ * in this state since boot
+ */
+ long totalTimeInStateMs;
+ /**
+ * Total number of times that the state was entered since boot
+ */
+ long totalStateEntryCount;
+ /**
+ * Last time this state was entered. Time in milliseconds since boot
+ */
+ long lastEntryTimestampMs;
+}
\ No newline at end of file
diff --git a/power/stats/aidl/android/hardware/power/stats/StateResidencyResult.aidl b/power/stats/aidl/android/hardware/power/stats/StateResidencyResult.aidl
new file mode 100644
index 0000000..949879c
--- /dev/null
+++ b/power/stats/aidl/android/hardware/power/stats/StateResidencyResult.aidl
@@ -0,0 +1,32 @@
+/*
+ * Copyright (C) 2020 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.power.stats;
+
+import android.hardware.power.stats.StateResidency;
+
+@VintfStability
+parcelable StateResidencyResult {
+ /**
+ * ID of the PowerEntity associated with this result
+ */
+ int id;
+ /**
+ * Residency for each state in the PowerEntity's state space
+ */
+ StateResidency[] stateResidencyData;
+}
+
diff --git a/power/stats/aidl/default/Android.bp b/power/stats/aidl/default/Android.bp
new file mode 100644
index 0000000..595ecd6
--- /dev/null
+++ b/power/stats/aidl/default/Android.bp
@@ -0,0 +1,30 @@
+// Copyright (C) 2020 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_binary {
+ name: "android.hardware.power.stats-service.example",
+ relative_install_path: "hw",
+ init_rc: ["power.stats-default.rc"],
+ vintf_fragments: ["power.stats-default.xml"],
+ vendor: true,
+ shared_libs: [
+ "libbase",
+ "libbinder_ndk",
+ "android.hardware.power.stats-V1-ndk_platform",
+ ],
+ srcs: [
+ "main.cpp",
+ "PowerStats.cpp",
+ ],
+}
diff --git a/power/stats/aidl/default/PowerStats.cpp b/power/stats/aidl/default/PowerStats.cpp
new file mode 100644
index 0000000..0ffbd08
--- /dev/null
+++ b/power/stats/aidl/default/PowerStats.cpp
@@ -0,0 +1,67 @@
+/*
+ * Copyright (C) 2020 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 "PowerStats.h"
+
+#include <android-base/logging.h>
+
+namespace aidl {
+namespace android {
+namespace hardware {
+namespace power {
+namespace stats {
+
+ndk::ScopedAStatus PowerStats::getPowerEntityInfo(std::vector<PowerEntity>* _aidl_return) {
+ (void)_aidl_return;
+ return ndk::ScopedAStatus::ok();
+}
+
+ndk::ScopedAStatus PowerStats::getStateResidency(const std::vector<int32_t>& in_powerEntityIds,
+ std::vector<StateResidencyResult>* _aidl_return) {
+ (void)in_powerEntityIds;
+ (void)_aidl_return;
+ return ndk::ScopedAStatus::ok();
+}
+
+ndk::ScopedAStatus PowerStats::getEnergyConsumerInfo(std::vector<EnergyConsumer>* _aidl_return) {
+ (void)_aidl_return;
+ return ndk::ScopedAStatus::ok();
+}
+
+ndk::ScopedAStatus PowerStats::getEnergyConsumed(const std::vector<int32_t>& in_energyConsumerIds,
+ std::vector<EnergyConsumerResult>* _aidl_return) {
+ (void)in_energyConsumerIds;
+ (void)_aidl_return;
+ return ndk::ScopedAStatus::ok();
+}
+
+ndk::ScopedAStatus PowerStats::getEnergyMeterInfo(std::vector<Channel>* _aidl_return) {
+ (void)_aidl_return;
+ return ndk::ScopedAStatus::ok();
+}
+
+ndk::ScopedAStatus PowerStats::readEnergyMeter(const std::vector<int32_t>& in_channelIds,
+ std::vector<EnergyMeasurement>* _aidl_return) {
+ (void)in_channelIds;
+ (void)_aidl_return;
+ return ndk::ScopedAStatus::ok();
+}
+
+} // namespace stats
+} // namespace power
+} // namespace hardware
+} // namespace android
+} // namespace aidl
diff --git a/power/stats/aidl/default/PowerStats.h b/power/stats/aidl/default/PowerStats.h
new file mode 100644
index 0000000..cb98e55
--- /dev/null
+++ b/power/stats/aidl/default/PowerStats.h
@@ -0,0 +1,46 @@
+/*
+ * Copyright (C) 2020 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.
+ */
+
+#pragma once
+
+#include <aidl/android/hardware/power/stats/BnPowerStats.h>
+
+namespace aidl {
+namespace android {
+namespace hardware {
+namespace power {
+namespace stats {
+
+class PowerStats : public BnPowerStats {
+ public:
+ PowerStats() = default;
+ // Methods from aidl::android::hardware::power::stats::IPowerStats
+ ndk::ScopedAStatus getPowerEntityInfo(std::vector<PowerEntity>* _aidl_return) override;
+ ndk::ScopedAStatus getStateResidency(const std::vector<int32_t>& in_powerEntityIds,
+ std::vector<StateResidencyResult>* _aidl_return) override;
+ ndk::ScopedAStatus getEnergyConsumerInfo(std::vector<EnergyConsumer>* _aidl_return) override;
+ ndk::ScopedAStatus getEnergyConsumed(const std::vector<int32_t>& in_energyConsumerIds,
+ std::vector<EnergyConsumerResult>* _aidl_return) override;
+ ndk::ScopedAStatus getEnergyMeterInfo(std::vector<Channel>* _aidl_return) override;
+ ndk::ScopedAStatus readEnergyMeter(const std::vector<int32_t>& in_channelIds,
+ std::vector<EnergyMeasurement>* _aidl_return) override;
+};
+
+} // namespace stats
+} // namespace power
+} // namespace hardware
+} // namespace android
+} // namespace aidl
diff --git a/power/stats/aidl/default/main.cpp b/power/stats/aidl/default/main.cpp
new file mode 100644
index 0000000..0469b4c
--- /dev/null
+++ b/power/stats/aidl/default/main.cpp
@@ -0,0 +1,35 @@
+/*
+ * Copyright (C) 2020 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 "PowerStats.h"
+
+#include <android-base/logging.h>
+#include <android/binder_manager.h>
+#include <android/binder_process.h>
+
+using aidl::android::hardware::power::stats::PowerStats;
+
+int main() {
+ ABinderProcess_setThreadPoolMaxThreadCount(0);
+ std::shared_ptr<PowerStats> p = ndk::SharedRefBase::make<PowerStats>();
+
+ const std::string instance = std::string() + PowerStats::descriptor + "/default";
+ binder_status_t status = AServiceManager_addService(p->asBinder().get(), instance.c_str());
+ CHECK(status == STATUS_OK);
+
+ ABinderProcess_joinThreadPool();
+ return EXIT_FAILURE; // should not reach
+}
diff --git a/power/stats/aidl/default/power.stats-default.rc b/power/stats/aidl/default/power.stats-default.rc
new file mode 100644
index 0000000..6ff6754
--- /dev/null
+++ b/power/stats/aidl/default/power.stats-default.rc
@@ -0,0 +1,4 @@
+service vendor.power.stats-default /vendor/bin/hw/android.hardware.power.stats-service.example
+ class hal
+ user system
+ group system
diff --git a/power/stats/aidl/default/power.stats-default.xml b/power/stats/aidl/default/power.stats-default.xml
new file mode 100644
index 0000000..3b1a216
--- /dev/null
+++ b/power/stats/aidl/default/power.stats-default.xml
@@ -0,0 +1,6 @@
+<manifest version="1.0" type="device">
+ <hal format="aidl">
+ <name>android.hardware.power.stats</name>
+ <fqname>IPowerStats/default</fqname>
+ </hal>
+</manifest>
diff --git a/power/stats/aidl/vts/Android.bp b/power/stats/aidl/vts/Android.bp
new file mode 100644
index 0000000..31fb990
--- /dev/null
+++ b/power/stats/aidl/vts/Android.bp
@@ -0,0 +1,32 @@
+// Copyright (C) 2020 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: "VtsHalPowerStatsTargetTest",
+ defaults: [
+ "VtsHalTargetTestDefaults",
+ "use_libaidlvintf_gtest_helper_static",
+ ],
+ srcs: ["VtsHalPowerStatsTargetTest.cpp"],
+ shared_libs: [
+ "libbinder_ndk",
+ ],
+ static_libs: [
+ "android.hardware.power.stats-V1-ndk_platform",
+ ],
+ test_suites: [
+ "general-tests",
+ "vts",
+ ],
+}
diff --git a/power/stats/aidl/vts/VtsHalPowerStatsTargetTest.cpp b/power/stats/aidl/vts/VtsHalPowerStatsTargetTest.cpp
new file mode 100644
index 0000000..bed3fdf
--- /dev/null
+++ b/power/stats/aidl/vts/VtsHalPowerStatsTargetTest.cpp
@@ -0,0 +1,423 @@
+/*
+ * Copyright (C) 2020 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 <aidl/Gtest.h>
+#include <aidl/Vintf.h>
+
+#include <aidl/android/hardware/power/stats/IPowerStats.h>
+#include <android-base/properties.h>
+#include <android/binder_manager.h>
+#include <android/binder_process.h>
+
+#include <algorithm>
+#include <iterator>
+#include <random>
+#include <unordered_map>
+
+using aidl::android::hardware::power::stats::Channel;
+using aidl::android::hardware::power::stats::EnergyConsumer;
+using aidl::android::hardware::power::stats::EnergyConsumerAttribution;
+using aidl::android::hardware::power::stats::EnergyConsumerResult;
+using aidl::android::hardware::power::stats::EnergyConsumerType;
+using aidl::android::hardware::power::stats::EnergyMeasurement;
+using aidl::android::hardware::power::stats::IPowerStats;
+using aidl::android::hardware::power::stats::PowerEntity;
+using aidl::android::hardware::power::stats::State;
+using aidl::android::hardware::power::stats::StateResidency;
+using aidl::android::hardware::power::stats::StateResidencyResult;
+
+using ndk::SpAIBinder;
+
+class PowerStatsAidl : public testing::TestWithParam<std::string> {
+ public:
+ virtual void SetUp() override {
+ powerstats = IPowerStats::fromBinder(
+ SpAIBinder(AServiceManager_waitForService(GetParam().c_str())));
+ ASSERT_NE(nullptr, powerstats.get());
+ }
+
+ template <typename T>
+ std::vector<T> getRandomSubset(std::vector<T> const& collection);
+
+ void testNameValid(const std::string& name);
+
+ template <typename T, typename S>
+ void testUnique(std::vector<T> const& collection, S T::*field);
+
+ template <typename T, typename S, typename R>
+ void testMatching(std::vector<T> const& c1, R T::*f1, std::vector<S> const& c2, R S::*f2);
+
+ std::shared_ptr<IPowerStats> powerstats;
+};
+
+// Returns a random subset from a collection
+template <typename T>
+std::vector<T> PowerStatsAidl::getRandomSubset(std::vector<T> const& collection) {
+ if (collection.empty()) {
+ return {};
+ }
+
+ std::vector<T> selected;
+ std::sample(collection.begin(), collection.end(), std::back_inserter(selected),
+ rand() % collection.size() + 1, std::mt19937{std::random_device{}()});
+
+ return selected;
+}
+
+// Tests whether a name is valid
+void PowerStatsAidl::testNameValid(const std::string& name) {
+ EXPECT_NE(name, "");
+}
+
+// Tests whether the fields in a given collection are unique
+template <typename T, typename S>
+void PowerStatsAidl::testUnique(std::vector<T> const& collection, S T::*field) {
+ std::set<S> cSet;
+ for (auto const& elem : collection) {
+ EXPECT_TRUE(cSet.insert(elem.*field).second);
+ }
+}
+
+template <typename T, typename S, typename R>
+void PowerStatsAidl::testMatching(std::vector<T> const& c1, R T::*f1, std::vector<S> const& c2,
+ R S::*f2) {
+ std::set<R> c1fields, c2fields;
+ for (auto elem : c1) {
+ c1fields.insert(elem.*f1);
+ }
+
+ for (auto elem : c2) {
+ c2fields.insert(elem.*f2);
+ }
+
+ EXPECT_EQ(c1fields, c2fields);
+}
+
+// Each PowerEntity must have a valid name
+TEST_P(PowerStatsAidl, ValidatePowerEntityNames) {
+ std::vector<PowerEntity> infos;
+ ASSERT_TRUE(powerstats->getPowerEntityInfo(&infos).isOk());
+
+ for (auto info : infos) {
+ testNameValid(info.name);
+ }
+}
+
+// Each power entity must have a unique name
+TEST_P(PowerStatsAidl, ValidatePowerEntityUniqueNames) {
+ std::vector<PowerEntity> entities;
+ ASSERT_TRUE(powerstats->getPowerEntityInfo(&entities).isOk());
+
+ testUnique(entities, &PowerEntity::name);
+}
+
+// Each PowerEntity must have a unique ID
+TEST_P(PowerStatsAidl, ValidatePowerEntityIds) {
+ std::vector<PowerEntity> entities;
+ ASSERT_TRUE(powerstats->getPowerEntityInfo(&entities).isOk());
+
+ testUnique(entities, &PowerEntity::id);
+}
+
+// Each power entity must have at least one state
+TEST_P(PowerStatsAidl, ValidateStateSize) {
+ std::vector<PowerEntity> entities;
+ ASSERT_TRUE(powerstats->getPowerEntityInfo(&entities).isOk());
+
+ for (auto entity : entities) {
+ EXPECT_GT(entity.states.size(), 0);
+ }
+}
+
+// Each state must have a valid name
+TEST_P(PowerStatsAidl, ValidateStateNames) {
+ std::vector<PowerEntity> entities;
+ ASSERT_TRUE(powerstats->getPowerEntityInfo(&entities).isOk());
+
+ for (auto entity : entities) {
+ for (auto state : entity.states) {
+ testNameValid(state.name);
+ }
+ }
+}
+
+// Each state must have a name that is unique to the given PowerEntity
+TEST_P(PowerStatsAidl, ValidateStateUniqueNames) {
+ std::vector<PowerEntity> entities;
+ ASSERT_TRUE(powerstats->getPowerEntityInfo(&entities).isOk());
+
+ for (auto entity : entities) {
+ testUnique(entity.states, &State::name);
+ }
+}
+
+// Each state must have an ID that is unique to the given PowerEntity
+TEST_P(PowerStatsAidl, ValidateStateUniqueIds) {
+ std::vector<PowerEntity> entities;
+ ASSERT_TRUE(powerstats->getPowerEntityInfo(&entities).isOk());
+
+ for (auto entity : entities) {
+ testUnique(entity.states, &State::id);
+ }
+}
+
+// State residency must return a valid status
+TEST_P(PowerStatsAidl, TestGetStateResidency) {
+ std::vector<StateResidencyResult> results;
+ ASSERT_TRUE(powerstats->getStateResidency({}, &results).isOk());
+}
+
+// State residency must return all results
+TEST_P(PowerStatsAidl, TestGetStateResidencyAllResults) {
+ std::vector<PowerEntity> entities;
+ ASSERT_TRUE(powerstats->getPowerEntityInfo(&entities).isOk());
+
+ std::vector<StateResidencyResult> results;
+ ASSERT_TRUE(powerstats->getStateResidency({}, &results).isOk());
+
+ testMatching(entities, &PowerEntity::id, results, &StateResidencyResult::id);
+}
+
+// Each result must contain all state residencies
+TEST_P(PowerStatsAidl, TestGetStateResidencyAllStateResidencies) {
+ std::vector<PowerEntity> entities;
+ ASSERT_TRUE(powerstats->getPowerEntityInfo(&entities).isOk());
+
+ std::vector<StateResidencyResult> results;
+ ASSERT_TRUE(powerstats->getStateResidency({}, &results).isOk());
+
+ for (auto entity : entities) {
+ auto it = std::find_if(results.begin(), results.end(),
+ [&entity](const auto& x) { return x.id == entity.id; });
+ ASSERT_NE(it, results.end());
+
+ testMatching(entity.states, &State::id, it->stateResidencyData, &StateResidency::id);
+ }
+}
+
+// State residency must return results for each requested power entity
+TEST_P(PowerStatsAidl, TestGetStateResidencySelectedResults) {
+ std::vector<PowerEntity> entities;
+ ASSERT_TRUE(powerstats->getPowerEntityInfo(&entities).isOk());
+ if (entities.empty()) {
+ return;
+ }
+
+ std::vector<PowerEntity> selectedEntities = getRandomSubset(entities);
+ std::vector<int32_t> selectedIds;
+ for (auto const& entity : selectedEntities) {
+ selectedIds.push_back(entity.id);
+ }
+
+ std::vector<StateResidencyResult> selectedResults;
+ ASSERT_TRUE(powerstats->getStateResidency(selectedIds, &selectedResults).isOk());
+
+ testMatching(selectedEntities, &PowerEntity::id, selectedResults, &StateResidencyResult::id);
+}
+
+// Energy meter info must return a valid status
+TEST_P(PowerStatsAidl, TestGetEnergyMeterInfo) {
+ std::vector<Channel> info;
+ ASSERT_TRUE(powerstats->getEnergyMeterInfo(&info).isOk());
+}
+
+// Each channel must have a valid name and subsystem
+TEST_P(PowerStatsAidl, ValidateChannelNames) {
+ std::vector<Channel> channels;
+ ASSERT_TRUE(powerstats->getEnergyMeterInfo(&channels).isOk());
+ for (auto channel : channels) {
+ testNameValid(channel.name);
+ testNameValid(channel.subsystem);
+ }
+}
+
+// Each channel must have a unique name
+TEST_P(PowerStatsAidl, ValidateChannelUniqueNames) {
+ std::vector<Channel> channels;
+ ASSERT_TRUE(powerstats->getEnergyMeterInfo(&channels).isOk());
+
+ testUnique(channels, &Channel::name);
+}
+
+// Each channel must have a unique ID
+TEST_P(PowerStatsAidl, ValidateChannelUniqueIds) {
+ std::vector<Channel> channels;
+ ASSERT_TRUE(powerstats->getEnergyMeterInfo(&channels).isOk());
+
+ testUnique(channels, &Channel::id);
+}
+
+// Reading energy meter must return a valid status
+TEST_P(PowerStatsAidl, TestReadEnergyMeter) {
+ std::vector<EnergyMeasurement> data;
+ ASSERT_TRUE(powerstats->readEnergyMeter({}, &data).isOk());
+}
+
+// Reading energy meter must return results for all available channels
+TEST_P(PowerStatsAidl, TestGetAllEnergyMeasurements) {
+ std::vector<Channel> channels;
+ ASSERT_TRUE(powerstats->getEnergyMeterInfo(&channels).isOk());
+
+ std::vector<EnergyMeasurement> measurements;
+ ASSERT_TRUE(powerstats->readEnergyMeter({}, &measurements).isOk());
+
+ testMatching(channels, &Channel::id, measurements, &EnergyMeasurement::id);
+}
+
+// Reading energy must must return results for each selected channel
+TEST_P(PowerStatsAidl, TestGetSelectedEnergyMeasurements) {
+ std::vector<Channel> channels;
+ ASSERT_TRUE(powerstats->getEnergyMeterInfo(&channels).isOk());
+ if (channels.empty()) {
+ return;
+ }
+
+ std::vector<Channel> selectedChannels = getRandomSubset(channels);
+ std::vector<int32_t> selectedIds;
+ for (auto const& channel : selectedChannels) {
+ selectedIds.push_back(channel.id);
+ }
+
+ std::vector<EnergyMeasurement> selectedMeasurements;
+ ASSERT_TRUE(powerstats->readEnergyMeter(selectedIds, &selectedMeasurements).isOk());
+
+ testMatching(selectedChannels, &Channel::id, selectedMeasurements, &EnergyMeasurement::id);
+}
+
+// Energy consumer info must return a valid status
+TEST_P(PowerStatsAidl, TestGetEnergyConsumerInfo) {
+ std::vector<EnergyConsumer> consumers;
+ ASSERT_TRUE(powerstats->getEnergyConsumerInfo(&consumers).isOk());
+}
+
+// Each energy consumer must have a unique id
+TEST_P(PowerStatsAidl, TestGetEnergyConsumerUniqueId) {
+ std::vector<EnergyConsumer> consumers;
+ ASSERT_TRUE(powerstats->getEnergyConsumerInfo(&consumers).isOk());
+
+ testUnique(consumers, &EnergyConsumer::id);
+}
+
+// Each energy consumer must have a valid name
+TEST_P(PowerStatsAidl, ValidateEnergyConsumerNames) {
+ std::vector<EnergyConsumer> consumers;
+ ASSERT_TRUE(powerstats->getEnergyConsumerInfo(&consumers).isOk());
+
+ for (auto consumer : consumers) {
+ testNameValid(consumer.name);
+ }
+}
+
+// Each energy consumer must have a unique name
+TEST_P(PowerStatsAidl, ValidateEnergyConsumerUniqueNames) {
+ std::vector<EnergyConsumer> consumers;
+ ASSERT_TRUE(powerstats->getEnergyConsumerInfo(&consumers).isOk());
+
+ testUnique(consumers, &EnergyConsumer::name);
+}
+
+// Energy consumers of the same type must have ordinals that are 0,1,2,..., N - 1
+TEST_P(PowerStatsAidl, ValidateEnergyConsumerOrdinals) {
+ std::vector<EnergyConsumer> consumers;
+ ASSERT_TRUE(powerstats->getEnergyConsumerInfo(&consumers).isOk());
+
+ std::unordered_map<EnergyConsumerType, std::set<int32_t>> ordinalMap;
+
+ // Ordinals must be unique for each type
+ for (auto consumer : consumers) {
+ EXPECT_TRUE(ordinalMap[consumer.type].insert(consumer.ordinal).second);
+ }
+
+ // Min ordinal must be 0, max ordinal must be N - 1
+ for (const auto& [unused, ordinals] : ordinalMap) {
+ EXPECT_EQ(0, *std::min_element(ordinals.begin(), ordinals.end()));
+ EXPECT_EQ(ordinals.size() - 1, *std::max_element(ordinals.begin(), ordinals.end()));
+ }
+}
+
+// Energy consumed must return a valid status
+TEST_P(PowerStatsAidl, TestGetEnergyConsumed) {
+ std::vector<EnergyConsumerResult> results;
+ ASSERT_TRUE(powerstats->getEnergyConsumed({}, &results).isOk());
+}
+
+// Energy consumed must return data for all energy consumers
+TEST_P(PowerStatsAidl, TestGetAllEnergyConsumed) {
+ std::vector<EnergyConsumer> consumers;
+ ASSERT_TRUE(powerstats->getEnergyConsumerInfo(&consumers).isOk());
+
+ std::vector<EnergyConsumerResult> results;
+ ASSERT_TRUE(powerstats->getEnergyConsumed({}, &results).isOk());
+
+ testMatching(consumers, &EnergyConsumer::id, results, &EnergyConsumerResult::id);
+}
+
+// Energy consumed must return data for each selected energy consumer
+TEST_P(PowerStatsAidl, TestGetSelectedEnergyConsumed) {
+ std::vector<EnergyConsumer> consumers;
+ ASSERT_TRUE(powerstats->getEnergyConsumerInfo(&consumers).isOk());
+ if (consumers.empty()) {
+ return;
+ }
+
+ std::vector<EnergyConsumer> selectedConsumers = getRandomSubset(consumers);
+ std::vector<int32_t> selectedIds;
+ for (auto const& consumer : selectedConsumers) {
+ selectedIds.push_back(consumer.id);
+ }
+
+ std::vector<EnergyConsumerResult> selectedResults;
+ ASSERT_TRUE(powerstats->getEnergyConsumed(selectedIds, &selectedResults).isOk());
+
+ testMatching(selectedConsumers, &EnergyConsumer::id, selectedResults,
+ &EnergyConsumerResult::id);
+}
+
+// Energy consumed attribution uids must be unique for a given energy consumer
+TEST_P(PowerStatsAidl, ValidateEnergyConsumerAttributionUniqueUids) {
+ std::vector<EnergyConsumerResult> results;
+ ASSERT_TRUE(powerstats->getEnergyConsumed({}, &results).isOk());
+
+ for (auto result : results) {
+ testUnique(result.attribution, &EnergyConsumerAttribution::uid);
+ }
+}
+
+// Energy consumed total energy >= sum total of uid-attributed energy
+TEST_P(PowerStatsAidl, TestGetEnergyConsumedAttributedEnergy) {
+ std::vector<EnergyConsumerResult> results;
+ ASSERT_TRUE(powerstats->getEnergyConsumed({}, &results).isOk());
+
+ for (auto result : results) {
+ int64_t totalAttributedEnergyUWs = 0;
+ for (auto attribution : result.attribution) {
+ totalAttributedEnergyUWs += attribution.energyUWs;
+ }
+ EXPECT_TRUE(result.energyUWs >= totalAttributedEnergyUWs);
+ }
+}
+
+GTEST_ALLOW_UNINSTANTIATED_PARAMETERIZED_TEST(PowerStatsAidl);
+INSTANTIATE_TEST_SUITE_P(
+ PowerStats, PowerStatsAidl,
+ testing::ValuesIn(android::getAidlHalInstanceNames(IPowerStats::descriptor)),
+ android::PrintInstanceNameToString);
+
+int main(int argc, char** argv) {
+ ::testing::InitGoogleTest(&argc, argv);
+ ABinderProcess_setThreadPoolMaxThreadCount(1);
+ ABinderProcess_startThreadPool();
+ return RUN_ALL_TESTS();
+}
diff --git a/radio/1.6/IRadio.hal b/radio/1.6/IRadio.hal
index a398e7d..df28940 100644
--- a/radio/1.6/IRadio.hal
+++ b/radio/1.6/IRadio.hal
@@ -18,9 +18,12 @@
import @1.0::CdmaSmsMessage;
+import @1.0::Dial;
import @1.0::GsmSmsMessage;
import @1.1::CardPowerState;
import @1.2::DataRequestReason;
+import @1.4::EmergencyCallRouting;
+import @1.4::EmergencyServiceCategory;
import @1.4::RadioAccessFamily;
import @1.5::IRadio;
import @1.5::AccessNetwork;
@@ -327,6 +330,9 @@
* Instruct the radio to *only* accept the types of network provided. This
* is stronger than setPreferredNetworkType which is a suggestion.
*
+ * In case of an emergency call, the modem is authorized to bypass this
+ * restriction.
+ *
* @param serial Serial number of request.
* @param networkTypeBitmap a 32-bit bearer bitmap of RadioAccessFamily
*
@@ -375,6 +381,64 @@
int64_t completionDurationMillis);
/**
+ * Initiate emergency voice call, with zero or more emergency service category(s), zero or
+ * more emergency Uniform Resource Names (URN), and routing information for handling the call.
+ * Android uses this request to make its emergency call instead of using @1.0::IRadio.dial
+ * if the 'address' in the 'dialInfo' field is identified as an emergency number by Android.
+ *
+ * In multi-sim scenario, if the emergency number is from a specific subscription, this radio
+ * request can still be sent out on the other subscription as long as routing is set to
+ * @1.4::EmergencyNumberRouting#EMERGENCY. This radio request will not be sent on an inactive
+ * (PIN/PUK locked) subscription unless both subscriptions are PIN/PUK locked. In this case,
+ * the request will be sent on the primary subscription.
+ *
+ * Some countries or carriers require some emergency numbers that must be handled with normal
+ * call routing if possible or emergency routing. 1) if the 'routing' field is specified as
+ * @1.4::EmergencyNumberRouting#NORMAL, the implementation must try the full radio service to
+ * use normal call routing to handle the call; if service cannot support normal routing, the
+ * implementation must use emergency routing to handle the call. 2) if 'routing' is specified
+ * as @1.4::EmergencyNumberRouting#EMERGENCY, the implementation must use emergency routing to
+ * handle the call. 3) if 'routing' is specified as @1.4::EmergencyNumberRouting#UNKNOWN,
+ * Android does not know how to handle the call.
+ *
+ * If the dialed emergency number does not have a specified emergency service category, the
+ * 'categories' field is set to @1.4::EmergencyServiceCategory#UNSPECIFIED; if the dialed
+ * emergency number does not have specified emergency Uniform Resource Names, the 'urns' field
+ * is set to an empty list. If the underlying technology used to request emergency services
+ * does not support the emergency service category or emergency uniform resource names, the
+ * field 'categories' or 'urns' may be ignored.
+ *
+ * In the scenarios that the 'address' in the 'dialInfo' field has other functions besides the
+ * emergency number function, if the 'hasKnownUserIntentEmergency' field is true, the user's
+ * intent for this dial request is emergency call, and the modem must treat this as an actual
+ * emergency dial; if the 'hasKnownUserIntentEmergency' field is false, Android does not know
+ * user's intent for this call.
+ *
+ * If 'isTesting' is true, this request is for testing purpose, and must not be sent to a real
+ * emergency service; otherwise it's for a real emergency call request.
+ *
+ * Reference: 3gpp 22.101, Section 10 - Emergency Calls;
+ * 3gpp 23.167, Section 6 - Functional description;
+ * 3gpp 24.503, Section 5.1.6.8.1 - General;
+ * RFC 5031
+ *
+ * @param serial Serial number of request.
+ * @param dialInfo the same @1.0::Dial information used by @1.0::IRadio.dial.
+ * @param categories bitfield<@1.4::EmergencyServiceCategory> the Emergency Service Category(s)
+ * of the call.
+ * @param urns the emergency Uniform Resource Names (URN)
+ * @param routing @1.4::EmergencyCallRouting the emergency call routing information.
+ * @param hasKnownUserIntentEmergency Flag indicating if user's intent for the emergency call
+ * is known.
+ * @param isTesting Flag indicating if this request is for testing purpose.
+ *
+ * Response function is IRadioResponse.emergencyDialResponse()
+ */
+ oneway emergencyDial_1_6(int32_t serial, Dial dialInfo,
+ bitfield<EmergencyServiceCategory> categories, vec<string> urns,
+ EmergencyCallRouting routing, bool hasKnownUserIntentEmergency, bool isTesting);
+
+ /**
* Get which bands the modem's background scan is acting on.
*
* @param serial Serial number of request.
diff --git a/radio/1.6/types.hal b/radio/1.6/types.hal
index 8e1033b..8560b3d 100644
--- a/radio/1.6/types.hal
+++ b/radio/1.6/types.hal
@@ -732,6 +732,14 @@
*/
NrVopsInfo nrVopsInfo;
} ngranInfo;
+
+ struct GeranRegistrationInfo {
+ /**
+ * True if the dual transfer mode is supported.
+ * Refer to 3GPP TS 44.108 section 3.4.25.3
+ */
+ bool dtmSupported;
+ } geranInfo;
} accessTechnologySpecificInfo;
};
diff --git a/radio/1.6/vts/functional/radio_hidl_hal_api.cpp b/radio/1.6/vts/functional/radio_hidl_hal_api.cpp
index 8b87292..44900b8 100644
--- a/radio/1.6/vts/functional/radio_hidl_hal_api.cpp
+++ b/radio/1.6/vts/functional/radio_hidl_hal_api.cpp
@@ -412,6 +412,167 @@
}
/*
+ * Test IRadio.emergencyDial() for the response returned.
+ */
+TEST_P(RadioHidlTest_v1_6, emergencyDial_1_6) {
+ if (!deviceSupportsFeature(FEATURE_VOICE_CALL)) {
+ ALOGI("Skipping emergencyDial because voice call is not supported in device");
+ return;
+ } else {
+ ALOGI("Running emergencyDial because voice call is supported in device");
+ }
+
+ serial = GetRandomSerialNumber();
+
+ ::android::hardware::radio::V1_0::Dial dialInfo;
+ dialInfo.address = hidl_string("911");
+ int categories = static_cast<int>(
+ ::android::hardware::radio::V1_4::EmergencyServiceCategory::UNSPECIFIED);
+ std::vector<hidl_string> urns = {""};
+ ::android::hardware::radio::V1_4::EmergencyCallRouting routing =
+ ::android::hardware::radio::V1_4::EmergencyCallRouting::UNKNOWN;
+
+ Return<void> res =
+ radio_v1_6->emergencyDial_1_6(serial, dialInfo, categories, urns, routing, true, true);
+ ASSERT_OK(res);
+ EXPECT_EQ(std::cv_status::no_timeout, wait());
+ EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_6->rspInfo_v1_0.type);
+ EXPECT_EQ(serial, radioRsp_v1_6->rspInfo_v1_0.serial);
+
+ ALOGI("emergencyDial, rspInfo_v1_0.error = %s\n",
+ toString(radioRsp_v1_6->rspInfo_v1_0.error).c_str());
+
+ ::android::hardware::radio::V1_0::RadioError rspEmergencyDial =
+ radioRsp_v1_6->rspInfo_v1_0.error;
+ // In DSDS or TSTS, we only check the result if the current slot is IN_SERVICE
+ // or Emergency_Only.
+ if (isDsDsEnabled() || isTsTsEnabled()) {
+ serial = GetRandomSerialNumber();
+ radio_v1_6->getVoiceRegistrationState(serial);
+ EXPECT_EQ(std::cv_status::no_timeout, wait());
+ if (isVoiceEmergencyOnly(radioRsp_v1_6->voiceRegResp.regState) ||
+ isVoiceInService(radioRsp_v1_6->voiceRegResp.regState)) {
+ EXPECT_EQ(::android::hardware::radio::V1_0::RadioError::NONE, rspEmergencyDial);
+ }
+ } else {
+ EXPECT_EQ(::android::hardware::radio::V1_0::RadioError::NONE, rspEmergencyDial);
+ }
+
+ // Give some time for modem to establish the emergency call channel.
+ sleep(MODEM_EMERGENCY_CALL_ESTABLISH_TIME);
+
+ // Disconnect all the potential established calls to prevent them affecting other tests.
+ clearPotentialEstablishedCalls();
+}
+
+/*
+ * Test IRadio.emergencyDial() with specified service and its response returned.
+ */
+TEST_P(RadioHidlTest_v1_6, emergencyDial_1_6_withServices) {
+ if (!deviceSupportsFeature(FEATURE_VOICE_CALL)) {
+ ALOGI("Skipping emergencyDial because voice call is not supported in device");
+ return;
+ } else {
+ ALOGI("Running emergencyDial because voice call is supported in device");
+ }
+
+ serial = GetRandomSerialNumber();
+
+ ::android::hardware::radio::V1_0::Dial dialInfo;
+ dialInfo.address = hidl_string("911");
+ int categories =
+ static_cast<int>(::android::hardware::radio::V1_4::EmergencyServiceCategory::AMBULANCE);
+ std::vector<hidl_string> urns = {"urn:service:sos.ambulance"};
+ ::android::hardware::radio::V1_4::EmergencyCallRouting routing =
+ ::android::hardware::radio::V1_4::EmergencyCallRouting::UNKNOWN;
+
+ Return<void> res =
+ radio_v1_6->emergencyDial_1_6(serial, dialInfo, categories, urns, routing, true, true);
+ ASSERT_OK(res);
+ EXPECT_EQ(std::cv_status::no_timeout, wait());
+ EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_6->rspInfo_v1_0.type);
+ EXPECT_EQ(serial, radioRsp_v1_6->rspInfo_v1_0.serial);
+
+ ALOGI("emergencyDial_withServices, rspInfo_v1_0.error = %s\n",
+ toString(radioRsp_v1_6->rspInfo_v1_0.error).c_str());
+ ::android::hardware::radio::V1_0::RadioError rspEmergencyDial =
+ radioRsp_v1_6->rspInfo_v1_0.error;
+
+ // In DSDS or TSTS, we only check the result if the current slot is IN_SERVICE
+ // or Emergency_Only.
+ if (isDsDsEnabled() || isTsTsEnabled()) {
+ serial = GetRandomSerialNumber();
+ radio_v1_6->getVoiceRegistrationState(serial);
+ EXPECT_EQ(std::cv_status::no_timeout, wait());
+ if (isVoiceEmergencyOnly(radioRsp_v1_6->voiceRegResp.regState) ||
+ isVoiceInService(radioRsp_v1_6->voiceRegResp.regState)) {
+ EXPECT_EQ(::android::hardware::radio::V1_0::RadioError::NONE, rspEmergencyDial);
+ }
+ } else {
+ EXPECT_EQ(::android::hardware::radio::V1_0::RadioError::NONE, rspEmergencyDial);
+ }
+ // Give some time for modem to establish the emergency call channel.
+ sleep(MODEM_EMERGENCY_CALL_ESTABLISH_TIME);
+
+ // Disconnect all the potential established calls to prevent them affecting other tests.
+ clearPotentialEstablishedCalls();
+}
+
+/*
+ * Test IRadio.emergencyDial() with known emergency call routing and its response returned.
+ */
+TEST_P(RadioHidlTest_v1_6, emergencyDial_1_6_withEmergencyRouting) {
+ if (!deviceSupportsFeature(FEATURE_VOICE_CALL)) {
+ ALOGI("Skipping emergencyDial because voice call is not supported in device");
+ return;
+ } else {
+ ALOGI("Running emergencyDial because voice call is supported in device");
+ }
+
+ serial = GetRandomSerialNumber();
+
+ ::android::hardware::radio::V1_0::Dial dialInfo;
+ dialInfo.address = hidl_string("911");
+ int categories = static_cast<int>(
+ ::android::hardware::radio::V1_4::EmergencyServiceCategory::UNSPECIFIED);
+ std::vector<hidl_string> urns = {""};
+ ::android::hardware::radio::V1_4::EmergencyCallRouting routing =
+ ::android::hardware::radio::V1_4::EmergencyCallRouting::EMERGENCY;
+
+ Return<void> res =
+ radio_v1_6->emergencyDial_1_6(serial, dialInfo, categories, urns, routing, true, true);
+ ASSERT_OK(res);
+ EXPECT_EQ(std::cv_status::no_timeout, wait());
+ EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_6->rspInfo_v1_0.type);
+ EXPECT_EQ(serial, radioRsp_v1_6->rspInfo_v1_0.serial);
+
+ ALOGI("emergencyDial_withEmergencyRouting, rspInfo_v1_0.error = %s\n",
+ toString(radioRsp_v1_6->rspInfo_v1_0.error).c_str());
+ ::android::hardware::radio::V1_0::RadioError rspEmergencyDial =
+ radioRsp_v1_6->rspInfo_v1_0.error;
+
+ // In DSDS or TSTS, we only check the result if the current slot is IN_SERVICE
+ // or Emergency_Only.
+ if (isDsDsEnabled() || isTsTsEnabled()) {
+ serial = GetRandomSerialNumber();
+ radio_v1_6->getVoiceRegistrationState(serial);
+ EXPECT_EQ(std::cv_status::no_timeout, wait());
+ if (isVoiceEmergencyOnly(radioRsp_v1_6->voiceRegResp.regState) ||
+ isVoiceInService(radioRsp_v1_6->voiceRegResp.regState)) {
+ EXPECT_EQ(::android::hardware::radio::V1_0::RadioError::NONE, rspEmergencyDial);
+ }
+ } else {
+ EXPECT_EQ(::android::hardware::radio::V1_0::RadioError::NONE, rspEmergencyDial);
+ }
+
+ // Give some time for modem to establish the emergency call channel.
+ sleep(MODEM_EMERGENCY_CALL_ESTABLISH_TIME);
+
+ // Disconnect all the potential established calls to prevent them affecting other tests.
+ clearPotentialEstablishedCalls();
+}
+
+/*
* Test IRadio.getCurrentCalls_1_6() for the response returned.
*/
TEST_P(RadioHidlTest_v1_6, getCurrentCalls_1_6) {
diff --git a/radio/1.6/vts/functional/radio_hidl_hal_test.cpp b/radio/1.6/vts/functional/radio_hidl_hal_test.cpp
index 79c3cde..59f7682 100644
--- a/radio/1.6/vts/functional/radio_hidl_hal_test.cpp
+++ b/radio/1.6/vts/functional/radio_hidl_hal_test.cpp
@@ -74,6 +74,29 @@
return status;
}
+void RadioHidlTest_v1_6::clearPotentialEstablishedCalls() {
+ // Get the current call Id to hangup the established emergency call.
+ serial = GetRandomSerialNumber();
+ radio_v1_6->getCurrentCalls_1_6(serial);
+ EXPECT_EQ(std::cv_status::no_timeout, wait());
+
+ // Hang up to disconnect the established call channels.
+ for (const ::android::hardware::radio::V1_6::Call& call : radioRsp_v1_6->currentCalls) {
+ serial = GetRandomSerialNumber();
+ radio_v1_6->hangup(serial, call.base.base.index);
+ ALOGI("Hang up to disconnect the established call channel: %d", call.base.base.index);
+ EXPECT_EQ(std::cv_status::no_timeout, wait());
+ // Give some time for modem to disconnect the established call channel.
+ sleep(MODEM_EMERGENCY_CALL_DISCONNECT_TIME);
+ }
+
+ // Verify there are no more current calls.
+ serial = GetRandomSerialNumber();
+ radio_v1_6->getCurrentCalls_1_6(serial);
+ EXPECT_EQ(std::cv_status::no_timeout, wait());
+ EXPECT_EQ(0, radioRsp_v1_6->currentCalls.size());
+}
+
void RadioHidlTest_v1_6::updateSimCardStatus() {
serial = GetRandomSerialNumber();
radio_v1_6->getIccCardStatus(serial);
diff --git a/radio/1.6/vts/functional/radio_hidl_hal_utils_v1_6.h b/radio/1.6/vts/functional/radio_hidl_hal_utils_v1_6.h
index 5fcfa3b..db067d7 100644
--- a/radio/1.6/vts/functional/radio_hidl_hal_utils_v1_6.h
+++ b/radio/1.6/vts/functional/radio_hidl_hal_utils_v1_6.h
@@ -70,7 +70,8 @@
::android::hardware::radio::V1_6::RadioResponseInfo rspInfo;
// Call
- hidl_vec<::android::hardware::radio::V1_2::Call> currentCalls;
+ hidl_vec<::android::hardware::radio::V1_6::Call> currentCalls;
+ ::android::hardware::radio::V1_2::VoiceRegStateResult voiceRegResp;
// Sms
SendSmsResult sendSmsResult;
diff --git a/radio/1.6/vts/functional/radio_response.cpp b/radio/1.6/vts/functional/radio_response.cpp
index 7c5cf6d..ba84fd4 100644
--- a/radio/1.6/vts/functional/radio_response.cpp
+++ b/radio/1.6/vts/functional/radio_response.cpp
@@ -816,8 +816,11 @@
}
Return<void> RadioResponse_v1_6::getVoiceRegistrationStateResponse_1_2(
- const ::android::hardware::radio::V1_0::RadioResponseInfo& /*info*/,
- const ::android::hardware::radio::V1_2::VoiceRegStateResult& /*voiceRegResponse*/) {
+ const ::android::hardware::radio::V1_0::RadioResponseInfo& info,
+ const ::android::hardware::radio::V1_2::VoiceRegStateResult& voiceRegResponse) {
+ rspInfo_v1_0 = info;
+ voiceRegResp = voiceRegResponse;
+ parent_v1_6.notify(info.serial);
return Void();
}
@@ -1210,8 +1213,9 @@
Return<void> RadioResponse_v1_6::getCurrentCallsResponse_1_6(
const ::android::hardware::radio::V1_6::RadioResponseInfo& info,
- const ::android::hardware::hidl_vec<::android::hardware::radio::V1_6::Call>& /*calls*/) {
+ const ::android::hardware::hidl_vec<::android::hardware::radio::V1_6::Call>& calls) {
rspInfo = info;
+ currentCalls = calls;
parent_v1_6.notify(info.serial);
return Void();
}
diff --git a/security/keymint/aidl/aidl_api/android.hardware.security.keymint/current/android/hardware/security/keymint/ErrorCode.aidl b/security/keymint/aidl/aidl_api/android.hardware.security.keymint/current/android/hardware/security/keymint/ErrorCode.aidl
index 594844a..a35b46c 100644
--- a/security/keymint/aidl/aidl_api/android.hardware.security.keymint/current/android/hardware/security/keymint/ErrorCode.aidl
+++ b/security/keymint/aidl/aidl_api/android.hardware.security.keymint/current/android/hardware/security/keymint/ErrorCode.aidl
@@ -111,6 +111,8 @@
STORAGE_KEY_UNSUPPORTED = -77,
INCOMPATIBLE_MGF_DIGEST = -78,
UNSUPPORTED_MGF_DIGEST = -79,
+ MISSING_NOT_BEFORE = -80,
+ MISSING_NOT_AFTER = -81,
UNIMPLEMENTED = -100,
VERSION_MISMATCH = -101,
UNKNOWN_ERROR = -1000,
diff --git a/security/keymint/aidl/aidl_api/android.hardware.security.keymint/current/android/hardware/security/keymint/Tag.aidl b/security/keymint/aidl/aidl_api/android.hardware.security.keymint/current/android/hardware/security/keymint/Tag.aidl
index b924a13..03982e3 100644
--- a/security/keymint/aidl/aidl_api/android.hardware.security.keymint/current/android/hardware/security/keymint/Tag.aidl
+++ b/security/keymint/aidl/aidl_api/android.hardware.security.keymint/current/android/hardware/security/keymint/Tag.aidl
@@ -94,4 +94,8 @@
MAC_LENGTH = 805307371,
RESET_SINCE_ID_ROTATION = 1879049196,
CONFIRMATION_TOKEN = -1879047187,
+ CERTIFICATE_SERIAL = -2147482642,
+ CERTIFICATE_SUBJECT = -1879047185,
+ CERTIFICATE_NOT_BEFORE = 1610613744,
+ CERTIFICATE_NOT_AFTER = 1610613745,
}
diff --git a/security/keymint/aidl/android/hardware/security/keymint/ErrorCode.aidl b/security/keymint/aidl/android/hardware/security/keymint/ErrorCode.aidl
index b20601d..35e3827 100644
--- a/security/keymint/aidl/android/hardware/security/keymint/ErrorCode.aidl
+++ b/security/keymint/aidl/android/hardware/security/keymint/ErrorCode.aidl
@@ -42,7 +42,7 @@
INVALID_AUTHORIZATION_TIMEOUT = -16,
UNSUPPORTED_KEY_FORMAT = -17,
INCOMPATIBLE_KEY_FORMAT = -18,
- UNSUPPORTED_KEY_ENCRYPTION_ALGORITHM = -19, /** For PKCS8 & PKCS12 */
+ UNSUPPORTED_KEY_ENCRYPTION_ALGORITHM = -19, /** For PKCS8 & PKCS12 */
UNSUPPORTED_KEY_VERIFICATION_ALGORITHM = -20, /** For PKCS8 & PKCS12 */
INVALID_INPUT_LENGTH = -21,
KEY_EXPORT_OPTIONS_INVALID = -22,
@@ -101,6 +101,8 @@
STORAGE_KEY_UNSUPPORTED = -77,
INCOMPATIBLE_MGF_DIGEST = -78,
UNSUPPORTED_MGF_DIGEST = -79,
+ MISSING_NOT_BEFORE = -80,
+ MISSING_NOT_AFTER = -81,
UNIMPLEMENTED = -100,
VERSION_MISMATCH = -101,
diff --git a/security/keymint/aidl/android/hardware/security/keymint/Tag.aidl b/security/keymint/aidl/android/hardware/security/keymint/Tag.aidl
index bc07235..4f58cbe 100644
--- a/security/keymint/aidl/android/hardware/security/keymint/Tag.aidl
+++ b/security/keymint/aidl/android/hardware/security/keymint/Tag.aidl
@@ -27,7 +27,7 @@
* data are stored in KeyParameter.
*/
@VintfStability
-@Backing(type = "int")
+@Backing(type="int")
enum Tag {
/**
* Tag::INVALID should never be set. It means you hit an error.
@@ -82,7 +82,6 @@
*/
BLOCK_MODE = (2 << 28) /* TagType:ENUM_REP */ | 4,
-
/**
* Tag::DIGEST specifies the digest algorithms that may be used with the key to perform signing
* and verification operations. This tag is relevant to RSA, ECDSA and HMAC keys. Possible
@@ -187,21 +186,21 @@
*/
INCLUDE_UNIQUE_ID = (7 << 28) /* TagType:BOOL */ | 202,
- /**
- * Tag::RSA_OAEP_MGF_DIGEST specifies the MGF1 digest algorithms that may be used with
- * RSA encryption/decryption with OAEP padding. If the key characteristics supports OAEP
- * and this tag is absent then SHA1 digest is selected by default for MGF1.
- *
- * This tag is repeatable for key generation/import. If this tag is present in the key
- * characteristics with one or more values from @4.0::Digest, then for RSA cipher
- * operations with OAEP Padding, the caller must specify a digest in the additionalParams
- * argument of begin operation. If this tag is missing or the specified digest is not in
- * the digests associated with the key then begin operation must fail with
- * ErrorCode::INCOMPATIBLE_MGF_DIGEST.
- *
- * Must be hardware-enforced.
- */
- RSA_OAEP_MGF_DIGEST = (2 << 28) /* TagType:ENUM_REP */ | 203,
+ /**
+ * Tag::RSA_OAEP_MGF_DIGEST specifies the MGF1 digest algorithms that may be used with
+ * RSA encryption/decryption with OAEP padding. If the key characteristics supports OAEP
+ * and this tag is absent then SHA1 digest is selected by default for MGF1.
+ *
+ * This tag is repeatable for key generation/import. If this tag is present in the key
+ * characteristics with one or more values from @4.0::Digest, then for RSA cipher
+ * operations with OAEP Padding, the caller must specify a digest in the additionalParams
+ * argument of begin operation. If this tag is missing or the specified digest is not in
+ * the digests associated with the key then begin operation must fail with
+ * ErrorCode::INCOMPATIBLE_MGF_DIGEST.
+ *
+ * Must be hardware-enforced.
+ */
+ RSA_OAEP_MGF_DIGEST = (2 << 28) /* TagType:ENUM_REP */ | 203,
/**
* TODO(seleneh) this tag needs to be deleted from all codes.
@@ -346,14 +345,14 @@
* At this point, if the caller specifies count > 1, it is not expected that any TEE will be
* able to enforce this feature in the hardware due to limited resources of secure
* storage. In this case, the tag with the value of maximum usage must be added to the key
- * characteristics with SecurityLevel::SOFTWARE by the IKeyMintDevice.
+ * characteristics with SecurityLevel::KEYSTORE by the IKeyMintDevice.
*
* On the other hand, if the caller specifies count = 1, some TEEs may have the ability
* to enforce this feature in the hardware with its secure storage. If the IKeyMintDevice
* implementation can enforce this feature, the tag with value = 1 must be added to the key
* characteristics with the SecurityLevel of the IKeyMintDevice. If the IKeyMintDevice can't
* enforce this feature even when the count = 1, the tag must be added to the key
- * characteristics with the SecurityLevel::SOFTWARE.
+ * characteristics with the SecurityLevel::KEYSTORE.
*
* When the key is attested, this tag with the same value must also be added to the attestation
* record. This tag must have the same SecurityLevel as the tag that is added to the key
@@ -497,7 +496,8 @@
*/
TRUSTED_USER_PRESENCE_REQUIRED = (7 << 28) /* TagType:BOOL */ | 507,
- /** Tag::TRUSTED_CONFIRMATION_REQUIRED is only applicable to keys with KeyPurpose SIGN, and
+ /**
+ * Tag::TRUSTED_CONFIRMATION_REQUIRED is only applicable to keys with KeyPurpose SIGN, and
* specifies that this key must not be usable unless the user provides confirmation of the data
* to be signed. Confirmation is proven to keyMint via an approval token. See
* CONFIRMATION_TOKEN, as well as the ConfirmatinUI HAL.
@@ -933,4 +933,35 @@
* Must never appear in KeyCharacteristics.
*/
CONFIRMATION_TOKEN = (9 << 28) /* TagType:BYTES */ | 1005,
+
+ /**
+ * Tag::CERTIFICATE_SERIAL specifies the serial number to be assigned to the
+ * attestation certificate to be generated for the given key. This parameter should only
+ * be passed to keyMint in the attestation parameters during generateKey() and importKey().
+ */
+ CERTIFICATE_SERIAL = (8 << 28) /* TagType:BIGNUM */ | 1006,
+
+ /**
+ * Tag::CERTIFICATE_SUBJECT the certificate subject. The value is a DER encoded X509 NAME.
+ * This value is used when generating a self signed certificates. This tag may be specified
+ * during generateKey and importKey. If not provided the subject name shall default to
+ * <TODO default subject here>.
+ */
+ CERTIFICATE_SUBJECT = (9 << 28) /* TagType:BYTES */ | 1007,
+
+ /**
+ * Tag::CERTIFICATE_NOT_BEFORE the beginning of the validity of the certificate in UNIX epoch
+ * time in seconds. This value is used when generating attestation or self signed certificates.
+ * ErrorCode::MISSING_NOT_BEFORE must be returned if this tag is not provided if this tag is
+ * not provided to generateKey or importKey.
+ */
+ CERTIFICATE_NOT_BEFORE = (6 << 28) /* TagType:DATE */ | 1008,
+
+ /**
+ * Tag::CERTIFICATE_NOT_AFTER the end of the validity of the certificate in UNIX epoch
+ * time in seconds. This value is used when generating attestation or self signed certificates.
+ * ErrorCode::MISSING_NOT_AFTER must be returned if this tag is not provided to generateKey
+ * or importKey.
+ */
+ CERTIFICATE_NOT_AFTER = (6 << 28) /* TagType:DATE */ | 1009,
}
diff --git a/security/keymint/aidl/vts/functional/KeyMintAidlTestBase.cpp b/security/keymint/aidl/vts/functional/KeyMintAidlTestBase.cpp
index 766c02d..6555157 100644
--- a/security/keymint/aidl/vts/functional/KeyMintAidlTestBase.cpp
+++ b/security/keymint/aidl/vts/functional/KeyMintAidlTestBase.cpp
@@ -55,6 +55,9 @@
for (auto& entry : key_characteristics) {
if (entry.authorizations.empty()) return false;
+ // Just ignore the SecurityLevel::KEYSTORE as the KM won't do any enforcement on this.
+ if (entry.securityLevel == SecurityLevel::KEYSTORE) continue;
+
if (levels_seen.find(entry.securityLevel) != levels_seen.end()) return false;
levels_seen.insert(entry.securityLevel);
@@ -824,22 +827,36 @@
return (found == key_characteristics.end()) ? kEmptyAuthList : found->authorizations;
}
-const vector<KeyParameter>& KeyMintAidlTestBase::HwEnforcedAuthorizations(
- const vector<KeyCharacteristics>& key_characteristics) {
- auto found =
- std::find_if(key_characteristics.begin(), key_characteristics.end(), [](auto& entry) {
- return entry.securityLevel == SecurityLevel::STRONGBOX ||
- entry.securityLevel == SecurityLevel::TRUSTED_ENVIRONMENT;
- });
+const vector<KeyParameter>& KeyMintAidlTestBase::SecLevelAuthorizations(
+ const vector<KeyCharacteristics>& key_characteristics, SecurityLevel securityLevel) {
+ auto found = std::find_if(
+ key_characteristics.begin(), key_characteristics.end(),
+ [securityLevel](auto& entry) { return entry.securityLevel == securityLevel; });
return (found == key_characteristics.end()) ? kEmptyAuthList : found->authorizations;
}
-const vector<KeyParameter>& KeyMintAidlTestBase::SwEnforcedAuthorizations(
+AuthorizationSet KeyMintAidlTestBase::HwEnforcedAuthorizations(
const vector<KeyCharacteristics>& key_characteristics) {
- auto found = std::find_if(
- key_characteristics.begin(), key_characteristics.end(),
- [](auto& entry) { return entry.securityLevel == SecurityLevel::SOFTWARE; });
- return (found == key_characteristics.end()) ? kEmptyAuthList : found->authorizations;
+ AuthorizationSet authList;
+ for (auto& entry : key_characteristics) {
+ if (entry.securityLevel == SecurityLevel::STRONGBOX ||
+ entry.securityLevel == SecurityLevel::TRUSTED_ENVIRONMENT) {
+ authList.push_back(AuthorizationSet(entry.authorizations));
+ }
+ }
+ return authList;
+}
+
+AuthorizationSet KeyMintAidlTestBase::SwEnforcedAuthorizations(
+ const vector<KeyCharacteristics>& key_characteristics) {
+ AuthorizationSet authList;
+ for (auto& entry : key_characteristics) {
+ if (entry.securityLevel == SecurityLevel::SOFTWARE ||
+ entry.securityLevel == SecurityLevel::KEYSTORE) {
+ authList.push_back(AuthorizationSet(entry.authorizations));
+ }
+ }
+ return authList;
}
} // namespace test
diff --git a/security/keymint/aidl/vts/functional/KeyMintAidlTestBase.h b/security/keymint/aidl/vts/functional/KeyMintAidlTestBase.h
index c1a1dd9..780971d 100644
--- a/security/keymint/aidl/vts/functional/KeyMintAidlTestBase.h
+++ b/security/keymint/aidl/vts/functional/KeyMintAidlTestBase.h
@@ -175,9 +175,12 @@
inline const vector<KeyParameter>& SecLevelAuthorizations() {
return SecLevelAuthorizations(key_characteristics_);
}
- const vector<KeyParameter>& HwEnforcedAuthorizations(
+ const vector<KeyParameter>& SecLevelAuthorizations(
+ const vector<KeyCharacteristics>& key_characteristics, SecurityLevel securityLevel);
+
+ AuthorizationSet HwEnforcedAuthorizations(
const vector<KeyCharacteristics>& key_characteristics);
- const vector<KeyParameter>& SwEnforcedAuthorizations(
+ AuthorizationSet SwEnforcedAuthorizations(
const vector<KeyCharacteristics>& key_characteristics);
private:
diff --git a/security/keymint/aidl/vts/functional/KeyMintTest.cpp b/security/keymint/aidl/vts/functional/KeyMintTest.cpp
index c876440..88122ce 100644
--- a/security/keymint/aidl/vts/functional/KeyMintTest.cpp
+++ b/security/keymint/aidl/vts/functional/KeyMintTest.cpp
@@ -544,7 +544,8 @@
ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
.RsaSigningKey(key_size, 65537)
.Digest(Digest::NONE)
- .Padding(PaddingMode::NONE),
+ .Padding(PaddingMode::NONE)
+ .SetDefaultValidity(),
&key_blob, &key_characteristics));
ASSERT_GT(key_blob.size(), 0U);
@@ -580,7 +581,8 @@
.Padding(PaddingMode::NONE)
.AttestationChallenge(challenge)
.AttestationApplicationId(app_id)
- .Authorization(TAG_NO_AUTH_REQUIRED),
+ .Authorization(TAG_NO_AUTH_REQUIRED)
+ .SetDefaultValidity(),
&key_blob, &key_characteristics));
ASSERT_GT(key_blob.size(), 0U);
@@ -620,7 +622,8 @@
.RsaSigningKey(key_size, 65537)
.Digest(Digest::NONE)
.Padding(PaddingMode::NONE)
- .Authorization(TAG_USAGE_COUNT_LIMIT, 1),
+ .Authorization(TAG_USAGE_COUNT_LIMIT, 1)
+ .SetDefaultValidity(),
&key_blob, &key_characteristics));
ASSERT_GT(key_blob.size(), 0U);
@@ -646,6 +649,62 @@
}
/*
+ * NewKeyGenerationTest.LimitedUsageRsaWithAttestation
+ *
+ * Verifies that KeyMint can generate all required RSA key sizes with limited usage, and that the
+ * resulting keys have correct characteristics and attestation.
+ */
+TEST_P(NewKeyGenerationTest, LimitedUsageRsaWithAttestation) {
+ for (auto key_size : ValidKeySizes(Algorithm::RSA)) {
+ auto challenge = "hello";
+ auto app_id = "foo";
+
+ vector<uint8_t> key_blob;
+ vector<KeyCharacteristics> key_characteristics;
+ ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
+ .RsaSigningKey(key_size, 65537)
+ .Digest(Digest::NONE)
+ .Padding(PaddingMode::NONE)
+ .AttestationChallenge(challenge)
+ .AttestationApplicationId(app_id)
+ .Authorization(TAG_NO_AUTH_REQUIRED)
+ .Authorization(TAG_USAGE_COUNT_LIMIT, 1)
+ .SetDefaultValidity(),
+ &key_blob, &key_characteristics));
+
+ ASSERT_GT(key_blob.size(), 0U);
+ CheckBaseParams(key_characteristics);
+
+ AuthorizationSet crypto_params = SecLevelAuthorizations(key_characteristics);
+
+ EXPECT_TRUE(crypto_params.Contains(TAG_ALGORITHM, Algorithm::RSA));
+ EXPECT_TRUE(crypto_params.Contains(TAG_KEY_SIZE, key_size))
+ << "Key size " << key_size << "missing";
+ EXPECT_TRUE(crypto_params.Contains(TAG_RSA_PUBLIC_EXPONENT, 65537U));
+
+ // Check the usage count limit tag appears in the authorizations.
+ AuthorizationSet auths;
+ for (auto& entry : key_characteristics) {
+ auths.push_back(AuthorizationSet(entry.authorizations));
+ }
+ EXPECT_TRUE(auths.Contains(TAG_USAGE_COUNT_LIMIT, 1U))
+ << "key usage count limit " << 1U << " missing";
+
+ // Check the usage count limit tag also appears in the attestation.
+ EXPECT_TRUE(verify_chain(cert_chain_));
+ ASSERT_GT(cert_chain_.size(), 0);
+
+ AuthorizationSet hw_enforced = HwEnforcedAuthorizations(key_characteristics);
+ AuthorizationSet sw_enforced = SwEnforcedAuthorizations(key_characteristics);
+ EXPECT_TRUE(verify_attestation_record(challenge, app_id, //
+ sw_enforced, hw_enforced, SecLevel(),
+ cert_chain_[0].encodedCertificate));
+
+ CheckedDeleteKey(&key_blob);
+ }
+}
+
+/*
* NewKeyGenerationTest.NoInvalidRsaSizes
*
* Verifies that keymint cannot generate any RSA key sizes that are designated as invalid.
@@ -658,7 +717,8 @@
GenerateKey(AuthorizationSetBuilder()
.RsaSigningKey(key_size, 65537)
.Digest(Digest::NONE)
- .Padding(PaddingMode::NONE),
+ .Padding(PaddingMode::NONE)
+ .SetDefaultValidity(),
&key_blob, &key_characteristics));
}
}
@@ -674,7 +734,8 @@
GenerateKey(AuthorizationSetBuilder()
.Authorization(TAG_ALGORITHM, Algorithm::RSA)
.Authorization(TAG_RSA_PUBLIC_EXPONENT, 3U)
- .SigningKey()));
+ .SigningKey()
+ .SetDefaultValidity()));
}
/*
@@ -687,10 +748,11 @@
for (auto key_size : ValidKeySizes(Algorithm::EC)) {
vector<uint8_t> key_blob;
vector<KeyCharacteristics> key_characteristics;
- ASSERT_EQ(ErrorCode::OK,
- GenerateKey(
- AuthorizationSetBuilder().EcdsaSigningKey(key_size).Digest(Digest::NONE),
- &key_blob, &key_characteristics));
+ ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
+ .EcdsaSigningKey(key_size)
+ .Digest(Digest::NONE)
+ .SetDefaultValidity(),
+ &key_blob, &key_characteristics));
ASSERT_GT(key_blob.size(), 0U);
CheckBaseParams(key_characteristics);
@@ -717,7 +779,8 @@
ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
.EcdsaSigningKey(key_size)
.Digest(Digest::NONE)
- .Authorization(TAG_USAGE_COUNT_LIMIT, 1),
+ .Authorization(TAG_USAGE_COUNT_LIMIT, 1)
+ .SetDefaultValidity(),
&key_blob, &key_characteristics));
ASSERT_GT(key_blob.size(), 0U);
@@ -752,7 +815,8 @@
GenerateKey(AuthorizationSetBuilder()
.Authorization(TAG_ALGORITHM, Algorithm::EC)
.SigningKey()
- .Digest(Digest::NONE)));
+ .Digest(Digest::NONE)
+ .SetDefaultValidity()));
}
/*
@@ -765,14 +829,17 @@
for (auto key_size : InvalidKeySizes(Algorithm::EC)) {
vector<uint8_t> key_blob;
vector<KeyCharacteristics> key_characteristics;
- ASSERT_EQ(ErrorCode::UNSUPPORTED_KEY_SIZE,
- GenerateKey(
- AuthorizationSetBuilder().EcdsaSigningKey(key_size).Digest(Digest::NONE),
- &key_blob, &key_characteristics));
+ ASSERT_EQ(ErrorCode::UNSUPPORTED_KEY_SIZE, GenerateKey(AuthorizationSetBuilder()
+ .EcdsaSigningKey(key_size)
+ .Digest(Digest::NONE)
+ .SetDefaultValidity(),
+ &key_blob, &key_characteristics));
}
- ASSERT_EQ(ErrorCode::UNSUPPORTED_KEY_SIZE,
- GenerateKey(AuthorizationSetBuilder().EcdsaSigningKey(190).Digest(Digest::NONE)));
+ ASSERT_EQ(ErrorCode::UNSUPPORTED_KEY_SIZE, GenerateKey(AuthorizationSetBuilder()
+ .EcdsaSigningKey(190)
+ .Digest(Digest::NONE)
+ .SetDefaultValidity()));
}
/*
@@ -788,7 +855,8 @@
GenerateKey(AuthorizationSetBuilder()
.EcdsaSigningKey(224)
.Authorization(TAG_EC_CURVE, EcCurve::P_256)
- .Digest(Digest::NONE)));
+ .Digest(Digest::NONE)
+ .SetDefaultValidity()));
}
/*
@@ -799,8 +867,10 @@
TEST_P(NewKeyGenerationTest, EcdsaAllValidSizes) {
auto valid_sizes = ValidKeySizes(Algorithm::EC);
for (size_t size : valid_sizes) {
- EXPECT_EQ(ErrorCode::OK,
- GenerateKey(AuthorizationSetBuilder().EcdsaSigningKey(size).Digest(Digest::NONE)))
+ EXPECT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
+ .EcdsaSigningKey(size)
+ .Digest(Digest::NONE)
+ .SetDefaultValidity()))
<< "Failed to generate size: " << size;
CheckedDeleteKey();
}
@@ -819,8 +889,10 @@
digest = Digest::SHA_2_512;
}
for (auto curve : ValidCurves()) {
- EXPECT_EQ(ErrorCode::OK,
- GenerateKey(AuthorizationSetBuilder().EcdsaSigningKey(curve).Digest(digest)))
+ EXPECT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
+ .EcdsaSigningKey(curve)
+ .Digest(digest)
+ .SetDefaultValidity()))
<< "Failed to generate key on curve: " << curve;
CheckedDeleteKey();
}
@@ -1003,7 +1075,8 @@
.RsaSigningKey(2048, 65537)
.Digest(Digest::NONE)
.Padding(PaddingMode::NONE)
- .Authorization(TAG_NO_AUTH_REQUIRED)));
+ .Authorization(TAG_NO_AUTH_REQUIRED)
+ .SetDefaultValidity()));
string message = "12345678901234567890123456789012";
string signature = SignMessage(
message, AuthorizationSetBuilder().Digest(Digest::NONE).Padding(PaddingMode::NONE));
@@ -1021,7 +1094,8 @@
.Digest(Digest::NONE)
.Padding(PaddingMode::NONE)
.Authorization(TAG_APPLICATION_ID, "clientid")
- .Authorization(TAG_APPLICATION_DATA, "appdata")));
+ .Authorization(TAG_APPLICATION_DATA, "appdata")
+ .SetDefaultValidity()));
EXPECT_EQ(ErrorCode::INVALID_KEY_BLOB,
Begin(KeyPurpose::SIGN,
AuthorizationSetBuilder().Digest(Digest::NONE).Padding(PaddingMode::NONE)));
@@ -1057,7 +1131,8 @@
.RsaSigningKey(2048, 65537)
.Digest(Digest::SHA_2_256)
.Padding(PaddingMode::RSA_PSS)
- .Authorization(TAG_NO_AUTH_REQUIRED)));
+ .Authorization(TAG_NO_AUTH_REQUIRED)
+ .SetDefaultValidity()));
// Use large message, which won't work without digesting.
string message(1024, 'a');
string signature = SignMessage(
@@ -1076,7 +1151,8 @@
.RsaSigningKey(2048, 65537)
.Digest(Digest::NONE)
.Authorization(TAG_NO_AUTH_REQUIRED)
- .Padding(PaddingMode::NONE)));
+ .Padding(PaddingMode::NONE)
+ .SetDefaultValidity()));
string message = "12345678901234567890123456789012";
string signature;
@@ -1095,13 +1171,13 @@
*/
TEST_P(SigningOperationsTest, NoUserConfirmation) {
if (SecLevel() == SecurityLevel::STRONGBOX) return;
- ASSERT_EQ(ErrorCode::OK,
- GenerateKey(AuthorizationSetBuilder()
- .RsaSigningKey(1024, 65537)
- .Digest(Digest::NONE)
- .Padding(PaddingMode::NONE)
- .Authorization(TAG_NO_AUTH_REQUIRED)
- .Authorization(TAG_TRUSTED_CONFIRMATION_REQUIRED)));
+ ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
+ .RsaSigningKey(1024, 65537)
+ .Digest(Digest::NONE)
+ .Padding(PaddingMode::NONE)
+ .Authorization(TAG_NO_AUTH_REQUIRED)
+ .Authorization(TAG_TRUSTED_CONFIRMATION_REQUIRED)
+ .SetDefaultValidity()));
const string message = "12345678901234567890123456789012";
EXPECT_EQ(ErrorCode::OK,
@@ -1121,7 +1197,8 @@
.RsaSigningKey(2048, 65537)
.Digest(Digest::SHA_2_256)
.Authorization(TAG_NO_AUTH_REQUIRED)
- .Padding(PaddingMode::RSA_PKCS1_1_5_SIGN)));
+ .Padding(PaddingMode::RSA_PKCS1_1_5_SIGN)
+ .SetDefaultValidity()));
string message(1024, 'a');
string signature = SignMessage(message, AuthorizationSetBuilder()
.Digest(Digest::SHA_2_256)
@@ -1138,7 +1215,8 @@
.RsaSigningKey(2048, 65537)
.Digest(Digest::NONE)
.Authorization(TAG_NO_AUTH_REQUIRED)
- .Padding(PaddingMode::RSA_PKCS1_1_5_SIGN)));
+ .Padding(PaddingMode::RSA_PKCS1_1_5_SIGN)
+ .SetDefaultValidity()));
string message(53, 'a');
string signature = SignMessage(message, AuthorizationSetBuilder()
.Digest(Digest::NONE)
@@ -1156,7 +1234,8 @@
.RsaSigningKey(2048, 65537)
.Digest(Digest::NONE)
.Authorization(TAG_NO_AUTH_REQUIRED)
- .Padding(PaddingMode::RSA_PKCS1_1_5_SIGN)));
+ .Padding(PaddingMode::RSA_PKCS1_1_5_SIGN)
+ .SetDefaultValidity()));
string message(257, 'a');
EXPECT_EQ(ErrorCode::OK,
@@ -1186,7 +1265,8 @@
.RsaSigningKey(1024, 65537)
.Digest(Digest::SHA_2_512)
.Authorization(TAG_NO_AUTH_REQUIRED)
- .Padding(PaddingMode::RSA_PSS)));
+ .Padding(PaddingMode::RSA_PSS)
+ .SetDefaultValidity()));
EXPECT_EQ(ErrorCode::INCOMPATIBLE_DIGEST,
Begin(KeyPurpose::SIGN, AuthorizationSetBuilder()
.Digest(Digest::SHA_2_512)
@@ -1204,7 +1284,8 @@
.RsaSigningKey(2048, 65537)
.Digest(Digest::NONE)
.Authorization(TAG_NO_AUTH_REQUIRED)
- .Padding(PaddingMode::RSA_PKCS1_1_5_SIGN)));
+ .Padding(PaddingMode::RSA_PKCS1_1_5_SIGN)
+ .SetDefaultValidity()));
// One byte too long
string message(2048 / 8 + 1, 'a');
ASSERT_EQ(ErrorCode::OK,
@@ -1238,7 +1319,8 @@
.RsaSigningKey(2048, 65537)
.Digest(Digest::NONE)
.Authorization(TAG_NO_AUTH_REQUIRED)
- .Padding(PaddingMode::NONE)));
+ .Padding(PaddingMode::NONE)
+ .SetDefaultValidity()));
ASSERT_EQ(ErrorCode::OK,
Begin(KeyPurpose::SIGN,
@@ -1263,7 +1345,8 @@
.RsaSigningKey(2048, 65537)
.Authorization(TAG_NO_AUTH_REQUIRED)
.Digest(Digest::SHA_2_256 /* supported digest */)
- .Padding(PaddingMode::PKCS7)));
+ .Padding(PaddingMode::PKCS7)
+ .SetDefaultValidity()));
ASSERT_EQ(
ErrorCode::UNSUPPORTED_PADDING_MODE,
Begin(KeyPurpose::SIGN,
@@ -1280,7 +1363,8 @@
.RsaSigningKey(2048, 65537)
.Authorization(TAG_NO_AUTH_REQUIRED)
.Digest(Digest::NONE)
- .Padding(PaddingMode::RSA_PSS)));
+ .Padding(PaddingMode::RSA_PSS)
+ .SetDefaultValidity()));
ASSERT_EQ(ErrorCode::INCOMPATIBLE_DIGEST,
Begin(KeyPurpose::SIGN,
AuthorizationSetBuilder().Digest(Digest::NONE).Padding(PaddingMode::RSA_PSS)));
@@ -1301,7 +1385,8 @@
.RsaKey(2048, 65537)
.Authorization(TAG_NO_AUTH_REQUIRED)
.SigningKey()
- .Digest(Digest::NONE)));
+ .Digest(Digest::NONE)
+ .SetDefaultValidity()));
ASSERT_EQ(ErrorCode::UNSUPPORTED_PADDING_MODE,
Begin(KeyPurpose::SIGN, AuthorizationSetBuilder().Digest(Digest::NONE)));
}
@@ -1316,7 +1401,8 @@
.Authorization(TAG_NO_AUTH_REQUIRED)
.RsaSigningKey(2048, 65537)
.Digest(Digest::NONE)
- .Padding(PaddingMode::NONE)));
+ .Padding(PaddingMode::NONE)
+ .SetDefaultValidity()));
// Barely shorter
string message(2048 / 8 - 1, 'a');
@@ -1337,7 +1423,8 @@
.Authorization(TAG_NO_AUTH_REQUIRED)
.RsaEncryptionKey(2048, 65537)
.Digest(Digest::NONE)
- .Padding(PaddingMode::NONE)));
+ .Padding(PaddingMode::NONE)
+ .SetDefaultValidity()));
ASSERT_EQ(ErrorCode::INCOMPATIBLE_PURPOSE,
Begin(KeyPurpose::SIGN,
AuthorizationSetBuilder().Digest(Digest::NONE).Padding(PaddingMode::NONE)));
@@ -1354,7 +1441,8 @@
.Authorization(TAG_NO_AUTH_REQUIRED)
.RsaSigningKey(2048, 65537)
.Digest(Digest::NONE)
- .Padding(PaddingMode::NONE)));
+ .Padding(PaddingMode::NONE)
+ .SetDefaultValidity()));
// Largest possible message will always be larger than the public modulus.
string message(2048 / 8, static_cast<char>(0xff));
@@ -1377,7 +1465,8 @@
ErrorCode error = GenerateKey(AuthorizationSetBuilder()
.Authorization(TAG_NO_AUTH_REQUIRED)
.EcdsaSigningKey(key_size)
- .Digest(digest));
+ .Digest(digest)
+ .SetDefaultValidity());
EXPECT_EQ(ErrorCode::OK, error) << "Failed to generate ECDSA key with size " << key_size
<< " and digest " << digest;
if (error != ErrorCode::OK) continue;
@@ -1400,7 +1489,8 @@
ErrorCode error = GenerateKey(AuthorizationSetBuilder()
.Authorization(TAG_NO_AUTH_REQUIRED)
.EcdsaSigningKey(curve)
- .Digest(Digest::SHA_2_256));
+ .Digest(Digest::SHA_2_256)
+ .SetDefaultValidity());
EXPECT_EQ(ErrorCode::OK, error) << "Failed to generate ECDSA key with curve " << curve;
if (error != ErrorCode::OK) continue;
@@ -1422,7 +1512,8 @@
ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
.Authorization(TAG_NO_AUTH_REQUIRED)
.EcdsaSigningKey(256)
- .Digest(Digest::NONE)));
+ .Digest(Digest::NONE)
+ .SetDefaultValidity()));
string message(1 * 1024, 'a');
SignMessage(message, AuthorizationSetBuilder().Digest(Digest::NONE));
}
@@ -1438,7 +1529,8 @@
.EcdsaSigningKey(256)
.Digest(Digest::NONE)
.Authorization(TAG_APPLICATION_ID, "clientid")
- .Authorization(TAG_APPLICATION_DATA, "appdata")));
+ .Authorization(TAG_APPLICATION_DATA, "appdata")
+ .SetDefaultValidity()));
EXPECT_EQ(ErrorCode::INVALID_KEY_BLOB,
Begin(KeyPurpose::SIGN, AuthorizationSetBuilder().Digest(Digest::NONE)));
AbortIfNeeded();
@@ -1627,7 +1719,8 @@
.Authorization(TAG_NO_AUTH_REQUIRED)
.RsaSigningKey(2048, 65537)
.Digest(Digest::NONE)
- .Padding(PaddingMode::NONE)));
+ .Padding(PaddingMode::NONE)
+ .SetDefaultValidity()));
string message = "12345678901234567890123456789012";
string signature = SignMessage(
message, AuthorizationSetBuilder().Digest(Digest::NONE).Padding(PaddingMode::NONE));
@@ -1647,7 +1740,8 @@
.Digest(ValidDigests(true /* withNone */, true /* withMD5 */))
.Padding(PaddingMode::NONE)
.Padding(PaddingMode::RSA_PSS)
- .Padding(PaddingMode::RSA_PKCS1_1_5_SIGN);
+ .Padding(PaddingMode::RSA_PKCS1_1_5_SIGN)
+ .SetDefaultValidity();
ASSERT_EQ(ErrorCode::OK, GenerateKey(authorizations));
@@ -1744,7 +1838,8 @@
ErrorCode error = GenerateKey(AuthorizationSetBuilder()
.Authorization(TAG_NO_AUTH_REQUIRED)
.EcdsaSigningKey(curve)
- .Digest(digests));
+ .Digest(digests)
+ .SetDefaultValidity());
EXPECT_EQ(ErrorCode::OK, error) << "Failed to generate key for EC curve " << curve;
if (error != ErrorCode::OK) {
continue;
@@ -1907,7 +2002,8 @@
.Authorization(TAG_NO_AUTH_REQUIRED)
.RsaSigningKey(1024, 65537)
.Digest(Digest::SHA_2_256)
- .Padding(PaddingMode::RSA_PSS),
+ .Padding(PaddingMode::RSA_PSS)
+ .SetDefaultValidity(),
KeyFormat::PKCS8, rsa_key));
CheckCryptoParam(TAG_ALGORITHM, Algorithm::RSA);
@@ -1934,7 +2030,8 @@
ImportKey(AuthorizationSetBuilder()
.RsaSigningKey(2048 /* Doesn't match key */, 65537)
.Digest(Digest::NONE)
- .Padding(PaddingMode::NONE),
+ .Padding(PaddingMode::NONE)
+ .SetDefaultValidity(),
KeyFormat::PKCS8, rsa_key));
}
@@ -1949,7 +2046,8 @@
ImportKey(AuthorizationSetBuilder()
.RsaSigningKey(1024, 3 /* Doesn't match key */)
.Digest(Digest::NONE)
- .Padding(PaddingMode::NONE),
+ .Padding(PaddingMode::NONE)
+ .SetDefaultValidity(),
KeyFormat::PKCS8, rsa_key));
}
@@ -1962,7 +2060,8 @@
ASSERT_EQ(ErrorCode::OK, ImportKey(AuthorizationSetBuilder()
.Authorization(TAG_NO_AUTH_REQUIRED)
.EcdsaSigningKey(256)
- .Digest(Digest::SHA_2_256),
+ .Digest(Digest::SHA_2_256)
+ .SetDefaultValidity(),
KeyFormat::PKCS8, ec_256_key));
CheckCryptoParam(TAG_ALGORITHM, Algorithm::EC);
@@ -1988,7 +2087,8 @@
ASSERT_EQ(ErrorCode::OK, ImportKey(AuthorizationSetBuilder()
.Authorization(TAG_NO_AUTH_REQUIRED)
.EcdsaSigningKey(256)
- .Digest(Digest::SHA_2_256),
+ .Digest(Digest::SHA_2_256)
+ .SetDefaultValidity(),
KeyFormat::PKCS8, ec_256_key_rfc5915));
CheckCryptoParam(TAG_ALGORITHM, Algorithm::EC);
@@ -2013,7 +2113,8 @@
ASSERT_EQ(ErrorCode::OK, ImportKey(AuthorizationSetBuilder()
.Authorization(TAG_NO_AUTH_REQUIRED)
.EcdsaSigningKey(256)
- .Digest(Digest::SHA_2_256),
+ .Digest(Digest::SHA_2_256)
+ .SetDefaultValidity(),
KeyFormat::PKCS8, ec_256_key_sec1));
CheckCryptoParam(TAG_ALGORITHM, Algorithm::EC);
@@ -2039,7 +2140,8 @@
ASSERT_EQ(ErrorCode::OK, ImportKey(AuthorizationSetBuilder()
.Authorization(TAG_NO_AUTH_REQUIRED)
.EcdsaSigningKey(521)
- .Digest(Digest::SHA_2_256),
+ .Digest(Digest::SHA_2_256)
+ .SetDefaultValidity(),
KeyFormat::PKCS8, ec_521_key));
CheckCryptoParam(TAG_ALGORITHM, Algorithm::EC);
@@ -2064,7 +2166,8 @@
ASSERT_EQ(ErrorCode::IMPORT_PARAMETER_MISMATCH,
ImportKey(AuthorizationSetBuilder()
.EcdsaSigningKey(224 /* Doesn't match key */)
- .Digest(Digest::NONE),
+ .Digest(Digest::NONE)
+ .SetDefaultValidity(),
KeyFormat::PKCS8, ec_256_key));
}
@@ -2078,7 +2181,8 @@
ASSERT_EQ(ErrorCode::IMPORT_PARAMETER_MISMATCH,
ImportKey(AuthorizationSetBuilder()
.EcdsaSigningKey(EcCurve::P_224 /* Doesn't match key */)
- .Digest(Digest::NONE),
+ .Digest(Digest::NONE)
+ .SetDefaultValidity(),
KeyFormat::PKCS8, ec_256_key));
}
@@ -2199,7 +2303,8 @@
.RsaEncryptionKey(2048, 65537)
.Digest(Digest::SHA_2_256)
.Padding(PaddingMode::RSA_OAEP)
- .Authorization(TAG_PURPOSE, KeyPurpose::WRAP_KEY);
+ .Authorization(TAG_PURPOSE, KeyPurpose::WRAP_KEY)
+ .SetDefaultValidity();
ASSERT_EQ(ErrorCode::OK,
ImportWrappedKey(wrapped_key, wrapping_key, wrapping_key_desc, zero_masking_key,
@@ -2219,7 +2324,8 @@
.RsaEncryptionKey(2048, 65537)
.Digest(Digest::SHA_2_256)
.Padding(PaddingMode::RSA_OAEP)
- .Authorization(TAG_PURPOSE, KeyPurpose::WRAP_KEY);
+ .Authorization(TAG_PURPOSE, KeyPurpose::WRAP_KEY)
+ .SetDefaultValidity();
ASSERT_EQ(ErrorCode::OK,
ImportWrappedKey(wrapped_key_masked, wrapping_key, wrapping_key_desc, masking_key,
@@ -2233,7 +2339,8 @@
.RsaEncryptionKey(2048, 65537)
.Digest(Digest::SHA_2_256)
.Padding(PaddingMode::RSA_OAEP)
- .Authorization(TAG_PURPOSE, KeyPurpose::WRAP_KEY);
+ .Authorization(TAG_PURPOSE, KeyPurpose::WRAP_KEY)
+ .SetDefaultValidity();
ASSERT_EQ(
ErrorCode::VERIFICATION_FAILED,
@@ -2247,7 +2354,8 @@
auto wrapping_key_desc = AuthorizationSetBuilder()
.RsaEncryptionKey(2048, 65537)
.Digest(Digest::SHA_2_256)
- .Padding(PaddingMode::RSA_OAEP);
+ .Padding(PaddingMode::RSA_OAEP)
+ .SetDefaultValidity();
ASSERT_EQ(
ErrorCode::INCOMPATIBLE_PURPOSE,
@@ -2270,7 +2378,8 @@
ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
.Authorization(TAG_NO_AUTH_REQUIRED)
.RsaEncryptionKey(2048, 65537)
- .Padding(PaddingMode::NONE)));
+ .Padding(PaddingMode::NONE)
+ .SetDefaultValidity()));
string message = string(2048 / 8, 'a');
auto params = AuthorizationSetBuilder().Padding(PaddingMode::NONE);
@@ -2293,7 +2402,8 @@
ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
.Authorization(TAG_NO_AUTH_REQUIRED)
.RsaEncryptionKey(2048, 65537)
- .Padding(PaddingMode::NONE)));
+ .Padding(PaddingMode::NONE)
+ .SetDefaultValidity()));
string message = "1";
auto params = AuthorizationSetBuilder().Padding(PaddingMode::NONE);
@@ -2322,7 +2432,8 @@
ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
.Authorization(TAG_NO_AUTH_REQUIRED)
.RsaEncryptionKey(2048, 65537)
- .Padding(PaddingMode::NONE)));
+ .Padding(PaddingMode::NONE)
+ .SetDefaultValidity()));
string message(2048 / 8 + 1, 'a');
@@ -2355,7 +2466,8 @@
.Authorization(TAG_NO_AUTH_REQUIRED)
.RsaEncryptionKey(key_size, 65537)
.Padding(PaddingMode::RSA_OAEP)
- .Digest(digests)));
+ .Digest(digests)
+ .SetDefaultValidity()));
string message = "Hello";
@@ -2403,7 +2515,8 @@
.Authorization(TAG_NO_AUTH_REQUIRED)
.RsaEncryptionKey(2048, 65537)
.Padding(PaddingMode::RSA_OAEP)
- .Digest(Digest::NONE)));
+ .Digest(Digest::NONE)
+ .SetDefaultValidity()));
string message = "Hello World!";
auto params = AuthorizationSetBuilder().Padding(PaddingMode::RSA_OAEP).Digest(Digest::NONE);
@@ -2423,7 +2536,8 @@
.Authorization(TAG_NO_AUTH_REQUIRED)
.RsaEncryptionKey(1024, 65537)
.Padding(PaddingMode::RSA_OAEP)
- .Digest(Digest::SHA_2_224, Digest::SHA_2_256)));
+ .Digest(Digest::SHA_2_224, Digest::SHA_2_256)
+ .SetDefaultValidity()));
string message = "Hello World!";
string ciphertext = EncryptMessage(
message,
@@ -2448,7 +2562,8 @@
.Authorization(TAG_NO_AUTH_REQUIRED)
.RsaEncryptionKey(2048, 65537)
.Padding(PaddingMode::RSA_OAEP)
- .Digest(Digest::SHA_2_256)));
+ .Digest(Digest::SHA_2_256)
+ .SetDefaultValidity()));
constexpr size_t digest_size = 256 /* SHA_2_256 */ / 8;
constexpr size_t oaep_overhead = 2 * digest_size + 2;
string message(2048 / 8 - oaep_overhead + 1, 'a');
@@ -2476,7 +2591,8 @@
.Authorization(TAG_NO_AUTH_REQUIRED)
.RsaEncryptionKey(key_size, 65537)
.Padding(PaddingMode::RSA_OAEP)
- .Digest(Digest::SHA_2_256)));
+ .Digest(Digest::SHA_2_256)
+ .SetDefaultValidity()));
string message = "Hello";
@@ -2529,7 +2645,8 @@
.Authorization(TAG_NO_AUTH_REQUIRED)
.RsaEncryptionKey(2048, 65537)
.Padding(PaddingMode::RSA_OAEP)
- .Digest(Digest::SHA_2_256)));
+ .Digest(Digest::SHA_2_256)
+ .SetDefaultValidity()));
string message = "Hello World!";
auto params = AuthorizationSetBuilder()
@@ -2552,7 +2669,8 @@
.Authorization(TAG_NO_AUTH_REQUIRED)
.RsaEncryptionKey(2048, 65537)
.Padding(PaddingMode::RSA_OAEP)
- .Digest(Digest::SHA_2_256)));
+ .Digest(Digest::SHA_2_256)
+ .SetDefaultValidity()));
string message = "Hello World!";
auto params = AuthorizationSetBuilder()
@@ -2571,7 +2689,8 @@
ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
.Authorization(TAG_NO_AUTH_REQUIRED)
.RsaEncryptionKey(2048, 65537)
- .Padding(PaddingMode::RSA_PKCS1_1_5_ENCRYPT)));
+ .Padding(PaddingMode::RSA_PKCS1_1_5_ENCRYPT)
+ .SetDefaultValidity()));
string message = "Hello World!";
auto params = AuthorizationSetBuilder().Padding(PaddingMode::RSA_PKCS1_1_5_ENCRYPT);
@@ -2610,7 +2729,8 @@
ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
.Authorization(TAG_NO_AUTH_REQUIRED)
.RsaEncryptionKey(2048, 65537)
- .Padding(PaddingMode::RSA_PKCS1_1_5_ENCRYPT)));
+ .Padding(PaddingMode::RSA_PKCS1_1_5_ENCRYPT)
+ .SetDefaultValidity()));
string message(2048 / 8 - 10, 'a');
auto params = AuthorizationSetBuilder().Padding(PaddingMode::RSA_PKCS1_1_5_ENCRYPT);
@@ -2630,7 +2750,8 @@
ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
.Authorization(TAG_NO_AUTH_REQUIRED)
.EcdsaSigningKey(256)
- .Digest(Digest::NONE)));
+ .Digest(Digest::NONE)
+ .SetDefaultValidity()));
auto params = AuthorizationSetBuilder().Digest(Digest::NONE);
ASSERT_EQ(ErrorCode::UNSUPPORTED_PURPOSE, Begin(KeyPurpose::ENCRYPT, params));
ASSERT_EQ(ErrorCode::UNSUPPORTED_PURPOSE, Begin(KeyPurpose::DECRYPT, params));
@@ -4278,7 +4399,8 @@
.Authorization(TAG_NO_AUTH_REQUIRED)
.RsaSigningKey(1024, 65537)
.NoDigestOrPadding()
- .Authorization(TAG_MAX_USES_PER_BOOT, 3)));
+ .Authorization(TAG_MAX_USES_PER_BOOT, 3)
+ .SetDefaultValidity()));
string message = "1234567890123456";
@@ -4297,11 +4419,11 @@
typedef KeyMintAidlTestBase UsageCountLimitTest;
/*
- * UsageCountLimitTest.TestLimitAes
+ * UsageCountLimitTest.TestSingleUseAes
*
- * Verifies that the usage count limit tag works correctly with AES keys.
+ * Verifies that the usage count limit tag = 1 works correctly with AES keys.
*/
-TEST_P(UsageCountLimitTest, TestLimitAes) {
+TEST_P(UsageCountLimitTest, TestSingleUseAes) {
if (SecLevel() == SecurityLevel::STRONGBOX) return;
ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
@@ -4322,38 +4444,83 @@
string message = "1234567890123456";
auto params = AuthorizationSetBuilder().EcbMode().Padding(PaddingMode::NONE);
+ AuthorizationSet hardware_auths = HwEnforcedAuthorizations(key_characteristics_);
+ AuthorizationSet keystore_auths =
+ SecLevelAuthorizations(key_characteristics_, SecurityLevel::KEYSTORE);
+
// First usage of AES key should work.
EncryptMessage(message, params);
- AuthorizationSet hardware_auths;
- for (auto& entry : key_characteristics_) {
- if (entry.securityLevel != SecurityLevel::SOFTWARE) {
- auths.push_back(AuthorizationSet(entry.authorizations));
- }
- }
if (hardware_auths.Contains(TAG_USAGE_COUNT_LIMIT, 1U)) {
// Usage count limit tag is enforced by hardware. After using the key, the key blob
// must be invalidated from secure storage (such as RPMB partition).
EXPECT_EQ(ErrorCode::INVALID_KEY_BLOB, Begin(KeyPurpose::ENCRYPT, params));
} else {
- // Usage count limit tag is enforced by software, keymint does nothing.
+ // Usage count limit tag is enforced by keystore, keymint does nothing.
+ EXPECT_TRUE(keystore_auths.Contains(TAG_USAGE_COUNT_LIMIT, 1U));
EXPECT_EQ(ErrorCode::OK, Begin(KeyPurpose::ENCRYPT, params));
}
}
/*
- * UsageCountLimitTest.TestLimitRsa
+ * UsageCountLimitTest.TestLimitedUseAes
*
- * Verifies that the usage count limit tag works correctly with RSA keys.
+ * Verifies that the usage count limit tag > 1 works correctly with AES keys.
*/
-TEST_P(UsageCountLimitTest, TestLimitRsa) {
+TEST_P(UsageCountLimitTest, TestLimitedUseAes) {
+ if (SecLevel() == SecurityLevel::STRONGBOX) return;
+
+ ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
+ .Authorization(TAG_NO_AUTH_REQUIRED)
+ .AesEncryptionKey(128)
+ .EcbMode()
+ .Padding(PaddingMode::NONE)
+ .Authorization(TAG_USAGE_COUNT_LIMIT, 3)));
+
+ // Check the usage count limit tag appears in the authorizations.
+ AuthorizationSet auths;
+ for (auto& entry : key_characteristics_) {
+ auths.push_back(AuthorizationSet(entry.authorizations));
+ }
+ EXPECT_TRUE(auths.Contains(TAG_USAGE_COUNT_LIMIT, 3U))
+ << "key usage count limit " << 3U << " missing";
+
+ string message = "1234567890123456";
+ auto params = AuthorizationSetBuilder().EcbMode().Padding(PaddingMode::NONE);
+
+ AuthorizationSet hardware_auths = HwEnforcedAuthorizations(key_characteristics_);
+ AuthorizationSet keystore_auths =
+ SecLevelAuthorizations(key_characteristics_, SecurityLevel::KEYSTORE);
+
+ EncryptMessage(message, params);
+ EncryptMessage(message, params);
+ EncryptMessage(message, params);
+
+ if (hardware_auths.Contains(TAG_USAGE_COUNT_LIMIT, 3U)) {
+ // Usage count limit tag is enforced by hardware. After using the key, the key blob
+ // must be invalidated from secure storage (such as RPMB partition).
+ EXPECT_EQ(ErrorCode::INVALID_KEY_BLOB, Begin(KeyPurpose::ENCRYPT, params));
+ } else {
+ // Usage count limit tag is enforced by keystore, keymint does nothing.
+ EXPECT_TRUE(keystore_auths.Contains(TAG_USAGE_COUNT_LIMIT, 3U));
+ EXPECT_EQ(ErrorCode::OK, Begin(KeyPurpose::ENCRYPT, params));
+ }
+}
+
+/*
+ * UsageCountLimitTest.TestSingleUseRsa
+ *
+ * Verifies that the usage count limit tag = 1 works correctly with RSA keys.
+ */
+TEST_P(UsageCountLimitTest, TestSingleUseRsa) {
if (SecLevel() == SecurityLevel::STRONGBOX) return;
ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
.Authorization(TAG_NO_AUTH_REQUIRED)
.RsaSigningKey(1024, 65537)
.NoDigestOrPadding()
- .Authorization(TAG_USAGE_COUNT_LIMIT, 1)));
+ .Authorization(TAG_USAGE_COUNT_LIMIT, 1)
+ .SetDefaultValidity()));
// Check the usage count limit tag appears in the authorizations.
AuthorizationSet auths;
@@ -4366,22 +4533,65 @@
string message = "1234567890123456";
auto params = AuthorizationSetBuilder().NoDigestOrPadding();
+ AuthorizationSet hardware_auths = HwEnforcedAuthorizations(key_characteristics_);
+ AuthorizationSet keystore_auths =
+ SecLevelAuthorizations(key_characteristics_, SecurityLevel::KEYSTORE);
+
// First usage of RSA key should work.
SignMessage(message, params);
- AuthorizationSet hardware_auths;
- for (auto& entry : key_characteristics_) {
- if (entry.securityLevel != SecurityLevel::SOFTWARE) {
- auths.push_back(AuthorizationSet(entry.authorizations));
- }
- }
-
if (hardware_auths.Contains(TAG_USAGE_COUNT_LIMIT, 1U)) {
// Usage count limit tag is enforced by hardware. After using the key, the key blob
// must be invalidated from secure storage (such as RPMB partition).
EXPECT_EQ(ErrorCode::INVALID_KEY_BLOB, Begin(KeyPurpose::SIGN, params));
} else {
- // Usage count limit tag is enforced by software, keymint does nothing.
+ // Usage count limit tag is enforced by keystore, keymint does nothing.
+ EXPECT_TRUE(keystore_auths.Contains(TAG_USAGE_COUNT_LIMIT, 1U));
+ EXPECT_EQ(ErrorCode::OK, Begin(KeyPurpose::SIGN, params));
+ }
+}
+
+/*
+ * UsageCountLimitTest.TestLimitUseRsa
+ *
+ * Verifies that the usage count limit tag > 1 works correctly with RSA keys.
+ */
+TEST_P(UsageCountLimitTest, TestLimitUseRsa) {
+ if (SecLevel() == SecurityLevel::STRONGBOX) return;
+
+ ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
+ .Authorization(TAG_NO_AUTH_REQUIRED)
+ .RsaSigningKey(1024, 65537)
+ .NoDigestOrPadding()
+ .Authorization(TAG_USAGE_COUNT_LIMIT, 3)
+ .SetDefaultValidity()));
+
+ // Check the usage count limit tag appears in the authorizations.
+ AuthorizationSet auths;
+ for (auto& entry : key_characteristics_) {
+ auths.push_back(AuthorizationSet(entry.authorizations));
+ }
+ EXPECT_TRUE(auths.Contains(TAG_USAGE_COUNT_LIMIT, 3U))
+ << "key usage count limit " << 3U << " missing";
+
+ string message = "1234567890123456";
+ auto params = AuthorizationSetBuilder().NoDigestOrPadding();
+
+ AuthorizationSet hardware_auths = HwEnforcedAuthorizations(key_characteristics_);
+ AuthorizationSet keystore_auths =
+ SecLevelAuthorizations(key_characteristics_, SecurityLevel::KEYSTORE);
+
+ SignMessage(message, params);
+ SignMessage(message, params);
+ SignMessage(message, params);
+
+ if (hardware_auths.Contains(TAG_USAGE_COUNT_LIMIT, 3U)) {
+ // Usage count limit tag is enforced by hardware. After using the key, the key blob
+ // must be invalidated from secure storage (such as RPMB partition).
+ EXPECT_EQ(ErrorCode::INVALID_KEY_BLOB, Begin(KeyPurpose::SIGN, params));
+ } else {
+ // Usage count limit tag is enforced by keystore, keymint does nothing.
+ EXPECT_TRUE(keystore_auths.Contains(TAG_USAGE_COUNT_LIMIT, 3U));
EXPECT_EQ(ErrorCode::OK, Begin(KeyPurpose::SIGN, params));
}
}
@@ -4563,7 +4773,8 @@
ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
.Authorization(TAG_NO_AUTH_REQUIRED)
.RsaEncryptionKey(2048, 65537)
- .Padding(PaddingMode::NONE)));
+ .Padding(PaddingMode::NONE)
+ .SetDefaultValidity()));
auto params = AuthorizationSetBuilder().Padding(PaddingMode::NONE);
constexpr size_t max_operations = 100; // set to arbituary large number
@@ -4694,7 +4905,8 @@
.Authorization(TAG_PURPOSE, KeyPurpose::AGREE_KEY)
.Authorization(TAG_ALGORITHM, Algorithm::EC)
.Authorization(TAG_ATTESTATION_APPLICATION_ID, {0x61, 0x62})
- .Authorization(TAG_ATTESTATION_CHALLENGE, challenge)))
+ .Authorization(TAG_ATTESTATION_CHALLENGE, challenge)
+ .SetDefaultValidity()))
<< "Failed to generate key";
ASSERT_GT(cert_chain_.size(), 0);
X509_Ptr kmKeyCert(parse_cert_blob(cert_chain_[0].encodedCertificate));
diff --git a/security/keymint/support/attestation_record.cpp b/security/keymint/support/attestation_record.cpp
index 596b097..a48f770 100644
--- a/security/keymint/support/attestation_record.cpp
+++ b/security/keymint/support/attestation_record.cpp
@@ -97,6 +97,7 @@
ASN1_NULL* device_unique_attestation;
ASN1_NULL* storage_key;
ASN1_NULL* identity_credential;
+ ASN1_INTEGER* usage_count_limit;
} KM_AUTH_LIST;
ASN1_SEQUENCE(KM_AUTH_LIST) = {
@@ -143,7 +144,8 @@
ASN1_EXP_OPT(KM_AUTH_LIST, storage_key, ASN1_NULL, TAG_STORAGE_KEY.maskedTag()),
ASN1_EXP_OPT(KM_AUTH_LIST, identity_credential, ASN1_NULL,
TAG_IDENTITY_CREDENTIAL_KEY.maskedTag()),
-
+ ASN1_EXP_OPT(KM_AUTH_LIST, usage_count_limit, ASN1_INTEGER,
+ TAG_USAGE_COUNT_LIMIT.maskedTag()),
} ASN1_SEQUENCE_END(KM_AUTH_LIST);
IMPLEMENT_ASN1_FUNCTIONS(KM_AUTH_LIST);
@@ -285,6 +287,7 @@
copyAuthTag(record->device_unique_attestation, TAG_DEVICE_UNIQUE_ATTESTATION, auth_list);
copyAuthTag(record->storage_key, TAG_STORAGE_KEY, auth_list);
copyAuthTag(record->identity_credential, TAG_IDENTITY_CREDENTIAL_KEY, auth_list);
+ copyAuthTag(record->usage_count_limit, TAG_USAGE_COUNT_LIMIT, auth_list);
return ErrorCode::OK;
}
diff --git a/security/keymint/support/authorization_set.cpp b/security/keymint/support/authorization_set.cpp
index 3d44dff..8d42571 100644
--- a/security/keymint/support/authorization_set.cpp
+++ b/security/keymint/support/authorization_set.cpp
@@ -243,4 +243,12 @@
return *this;
}
+AuthorizationSetBuilder& AuthorizationSetBuilder::SetDefaultValidity() {
+ // Per RFC 5280 4.1.2.5, an undefined expiration (not-after) field should be set to
+ // GeneralizedTime 999912312359559, which is 253402300799000 ms from Jan 1, 1970.
+ constexpr uint64_t kUndefinedExpirationDateTime = 253402300799000;
+ Authorization(TAG_CERTIFICATE_NOT_BEFORE, 0);
+ return Authorization(TAG_CERTIFICATE_NOT_AFTER, kUndefinedExpirationDateTime);
+}
+
} // namespace aidl::android::hardware::security::keymint
diff --git a/security/keymint/support/include/keymint_support/authorization_set.h b/security/keymint/support/include/keymint_support/authorization_set.h
index 1407c5f..6d36794 100644
--- a/security/keymint/support/include/keymint_support/authorization_set.h
+++ b/security/keymint/support/include/keymint_support/authorization_set.h
@@ -300,6 +300,8 @@
AuthorizationSetBuilder& Digest(std::vector<Digest> digests);
AuthorizationSetBuilder& Padding(std::initializer_list<PaddingMode> paddings);
+ AuthorizationSetBuilder& SetDefaultValidity();
+
AuthorizationSetBuilder& AttestationChallenge(const std::string& challenge) {
return Authorization(TAG_ATTESTATION_CHALLENGE, challenge);
}
diff --git a/security/keymint/support/include/keymint_support/keymint_tags.h b/security/keymint/support/include/keymint_support/keymint_tags.h
index 43cfb63..479a11d 100644
--- a/security/keymint/support/include/keymint_support/keymint_tags.h
+++ b/security/keymint/support/include/keymint_support/keymint_tags.h
@@ -126,6 +126,10 @@
DECLARE_TYPED_TAG(USER_SECURE_ID);
DECLARE_TYPED_TAG(VENDOR_PATCHLEVEL);
DECLARE_TYPED_TAG(RSA_OAEP_MGF_DIGEST);
+DECLARE_TYPED_TAG(CERTIFICATE_SERIAL);
+DECLARE_TYPED_TAG(CERTIFICATE_SUBJECT);
+DECLARE_TYPED_TAG(CERTIFICATE_NOT_BEFORE);
+DECLARE_TYPED_TAG(CERTIFICATE_NOT_AFTER);
#undef DECLARE_TYPED_TAG
diff --git a/sensors/common/default/2.X/Sensor.cpp b/sensors/common/default/2.X/Sensor.cpp
index 642fc89..1a7c628 100644
--- a/sensors/common/default/2.X/Sensor.cpp
+++ b/sensors/common/default/2.X/Sensor.cpp
@@ -313,7 +313,7 @@
mSensorInfo.maxRange = 1000.0f * M_PI / 180.0f;
mSensorInfo.resolution = 1000.0f * M_PI / (180.0f * 32768.0f);
mSensorInfo.power = 0.001f;
- mSensorInfo.minDelay = 2.5f * 1000; // microseconds
+ mSensorInfo.minDelay = 10 * 1000; // microseconds
mSensorInfo.maxDelay = kDefaultMaxDelayUs;
mSensorInfo.fifoReservedEventCount = 0;
mSensorInfo.fifoMaxEventCount = 0;
diff --git a/tv/cec/2.0/Android.bp b/tv/cec/2.0/Android.bp
deleted file mode 100644
index 5463b6d..0000000
--- a/tv/cec/2.0/Android.bp
+++ /dev/null
@@ -1,16 +0,0 @@
-// This file is autogenerated by hidl-gen -Landroidbp.
-
-hidl_interface {
- name: "android.hardware.tv.cec@2.0",
- root: "android.hardware",
- srcs: [
- "types.hal",
- "IHdmiCec.hal",
- "IHdmiCecCallback.hal",
- ],
- interfaces: [
- "android.hidl.base@1.0",
- "android.hidl.safe_union@1.0",
- ],
- gen_java: true,
-}
diff --git a/tv/cec/2.0/IHdmiCec.hal b/tv/cec/2.0/IHdmiCec.hal
deleted file mode 100644
index 0723bad..0000000
--- a/tv/cec/2.0/IHdmiCec.hal
+++ /dev/null
@@ -1,209 +0,0 @@
-/*
- * 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.cec@2.0;
-
-import IHdmiCecCallback;
-
-/**
- * HDMI-CEC HAL interface definition.
- */
-interface IHdmiCec {
- /**
- * Passes Primary Device Type that must be used in this system.
- *
- * HAL must use it to allocate logical address as specified in CEC section
- * 11.3.2 of the CEC spec 2.0b. Then CEC commands addressed the given
- * logical address can be filtered in.
- * This method shall be able to be called up to twice to support two Primary
- * Device Type as specified in CEC Table 11-8 of the CEC spec 2.0b.
- *
- * @param deviceType that must be used in this system. It must be a valid
- * value in CecDeviceType for the call to succeed.
- * @return result Result status of the operation. SUCCESS if successful,
- * FAILURE_INVALID_ARGS if the given device type is invalid,
- * FAILURE_BUSY if device or resource is busy
- */
- @callflow(next={"*"})
- addDeviceType(CecDeviceType deviceType) generates (Result result);
-
- /**
- * Clears all Primary Device Types.
- *
- * It is used when the system plan to reconfigure Primary Device Type,
- * hence to tell HAL to release all logical address associated to them,
- * and change the state back to the beginning.
- */
- @callflow(next="addDeviceType")
- @exit
- clearDeviceTypes();
-
- /**
- * Set All Device Types for a Primary Device Type.
- *
- * This value must be used in REPORT_FEATURES message to response
- * GIVE_FEATURES message in HAL.
- *
- * @param allDeviceTypes device all device types for a Primary Device Type.
- */
- @callflow(next="addDeviceType")
- setAllDeviceTypes(CecAllDeviceTypes allDeviceTypes);
-
- /**
- * Set Device Features for a Primary Device Type.
- *
- * This value must be used in REPORT_FEATURES message to response
- * GIVE_FEATURES message in HAL.
- *
- * @param deviceType The device Primary Device Type.
- * @param deviceFeatures device features for a Primary Device Type.
- */
- @callflow(next="addDeviceType")
- setDeviceFeatures(CecDeviceType deviceType,
- CecDeviceFeatures deviceFeatures);
-
- /**
- * Set Remote Control Profile for a Primary Device Type.
- *
- * This value must be used in REPORT_FEATURES message to response
- * GIVE_FEATURES message in HAL.
- *
- * @param deviceType The device Primary Device Type.
- * @param rcProliles remote control profiles for a Primary Device Type.
- */
- @callflow(next="addDeviceType")
- setRcProfile(CecDeviceType deviceType, CecRcProfile rcProfile);
-
- /**
- * Retrieve CEC device information.
- *
- * CEC section 11.3 of the CEC spec 2.0b specify that a device should not
- * ask for static information that another device has already supplied.
- * Therefore, CEC 2.0 software stack need a map to store all cec
- * devices’ information of current CEC network.
- * The device information is broadcasted by a device after it allocates a
- * logical address. Messages used to send out these information are
- * REPORT_FEATURES, REPORT_PHYSICAL_ADDRESS, DEVICE_VENDOR_ID.
- * The spec also requires less than 1 second between REPORT_FEATURES and
- * REPORT_PHYSICAL_ADDRESS message, and less than 2 second between
- * REPORT_PHYSICAL_ADDRESS and DEVICE_VENDOR_ID. An Implementation of
- * device information map in hal can help to meet the timing constraints.
- * Logical addressing is part of the process to build this map, so the
- * implementation shall include allocating logical address too.
- * Whenever a device plug/unplug, the topology of CEC network changes.
- * The hal implementation shall update devices’ information map, and
- * send out onTopologyEvent to Android system. Then Android system
- * will use readDeviceInfo to retreive latest devices’ information of CEC
- * network.
- * If SYSTEM_CEC_CONTROL is false, the hal implementation need continue to
- * maintain and update device information map, and send out pending
- * onTopologyEvent to Android system when SYSTEM_CEC_CONTROL is
- * changed to true.
- *
- * @param logicalAddress logical address of CEC device.
- * @param physicalAddress physical address of CEC device.
- * @return CecDeviceInfo from device information map.
- * @return result Result status of the operation. SUCCESS if successful,
- * FAILURE_INVALID_ARGS if logical or physical address is invalid.
- * FAILURE_INVALID_STATE if device information isn't available yet.
- */
- @callflow(next="onTopologyChangeEvent")
- readDeviceInfo(CecLogicalAddress logicalAddress,
- CecPhysicalAddress physicalAddress)
- generates (Result result, CecDeviceInfo deviceInfo);
-
- /**
- * Transmits HDMI-CEC message to other HDMI device.
- *
- * The method must be designed to return in a certain amount of time and not
- * hanging forever. This method MUST complete with in 1 second.
- *
- * It must try retransmission at least once as specified in the section '7.1
- * Frame Re-transmissions' of the CEC Spec 1.4b.
- *
- * @param message CEC message to be sent to other HDMI device.
- * @return result Result status of the operation. SUCCESS if successful,
- * NACK if the sent message is not acknowledged,
- * BUSY if the CEC bus is busy.
- */
- @callflow(next="*")
- sendMessage(CecMessage message) generates (SendMessageResult result);
-
- /**
- * Set the callback
- *
- * It is used by the framework to receive CecMessages, HDMI hotplug event
- * and topology update event. Only one callback client is supported.
- *
- * @param callback Callback object to pass hdmi events to the system. The
- * previously registered callback must be replaced with this one.
- */
- @callflow(next={"*"})
- @entry
- setCallback(IHdmiCecCallback callback);
-
- /**
- * Gets the hdmi port information of underlying hardware.
- *
- * @return infos The list of HDMI port information
- */
- @callflow(next={"*"})
- getPortInfo() generates (vec<HdmiPortInfo> infos);
-
- /**
- * Sets flags controlling the way HDMI-CEC service works down to HAL
- * implementation. Those flags must be used in case the feature needs update
- * in HAL itself, firmware or microcontroller.
- *
- * @param key The key of the option to be updated with a new value.
- * @param value Value to be set.
- */
- @callflow(next="*")
- setOption(OptionKey key, bool value);
-
- /**
- * Passes the updated language information of Android system. Contains
- * three-letter code as defined in ISO/FDIS 639-2. Must be used for HAL to
- * respond to <Get Menu Language> while in standby mode.
- *
- * @param language Three-letter code defined in ISO/FDIS 639-2. Must be
- * lowercase letters. (e.g., eng for English)
- */
- @callflow(next="*")
- setLanguage(string language);
-
- /**
- * Configures ARC circuit in the hardware logic to start or stop the
- * feature.
- *
- * @param portId Port id to be configured.
- * @param enable Flag must be either true to start the feature or false to
- * stop it.
- */
- @callflow(next="*")
- enableAudioReturnChannel(HdmiPortId portId, bool enable);
-
- /**
- * Gets the connection status of the specified port.
- *
- * It's specified in CEC section 10.8 of the CEC spec 2.0b
- *
- * @param portId Port id to be inspected for the connection status.
- * @return status True if a device is connected, otherwise false.
- */
- @callflow(next="*")
- isConnected(HdmiPortId portId) generates (bool connected);
-};
diff --git a/tv/cec/2.0/IHdmiCecCallback.hal b/tv/cec/2.0/IHdmiCecCallback.hal
deleted file mode 100644
index 1a8a489..0000000
--- a/tv/cec/2.0/IHdmiCecCallback.hal
+++ /dev/null
@@ -1,41 +0,0 @@
-/*
- * 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.cec@2.0;
-
-interface IHdmiCecCallback {
- /**
- * The callback function that must be called by HAL implementation to notify
- * the system of new CEC message arrival.
- */
- oneway onCecMessage(CecMessage message);
-
- /**
- * The callback function that must be called by HAL implementation to notify
- * the system of new hotplug event.
- */
- oneway onHotplugEvent(HotplugEvent event);
-
- /**
- * The callback function must be called by HAL implementation to notify the
- * system whenever CEC device information of CEC network change.
- * HAL shall be ready for readDeviceInfo call before invoke this callback.
- * This event is triggered by topology change of whole CEC network. It's
- * different from HotplugEvent which is triggered between devices which are
- * connected directly through HDMI cable.
- */
- oneway onTopologyEvent(CecTopologyEvent event);
-};
diff --git a/tv/cec/2.0/default/Android.bp b/tv/cec/2.0/default/Android.bp
deleted file mode 100644
index d3d5342..0000000
--- a/tv/cec/2.0/default/Android.bp
+++ /dev/null
@@ -1,40 +0,0 @@
-cc_library_shared {
- name: "android.hardware.tv.cec@2.0-impl",
- defaults: ["hidl_defaults"],
- vendor: true,
- relative_install_path: "hw",
- srcs: ["HdmiCec.cpp"],
-
- shared_libs: [
- "libhidlbase",
- "liblog",
- "libbase",
- "libutils",
- "libhardware",
- "android.hardware.tv.cec@2.0",
- ],
-
-}
-
-cc_binary {
- name: "android.hardware.tv.cec@2.0-service",
- vintf_fragments: ["android.hardware.tv.cec@2.0-service.xml"],
- defaults: ["hidl_defaults"],
- relative_install_path: "hw",
- vendor: true,
- init_rc: ["android.hardware.tv.cec@2.0-service.rc"],
- srcs: ["service.cpp"],
-
- shared_libs: [
- "liblog",
- "libcutils",
- "libdl",
- "libbase",
- "libutils",
- "libhardware_legacy",
- "libhardware",
- "libhidlbase",
- "android.hardware.tv.cec@2.0",
- ],
-
-}
diff --git a/tv/cec/2.0/default/HdmiCec.cpp b/tv/cec/2.0/default/HdmiCec.cpp
deleted file mode 100644
index f451719..0000000
--- a/tv/cec/2.0/default/HdmiCec.cpp
+++ /dev/null
@@ -1,421 +0,0 @@
-/*
- * 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 "android.hardware.tv.cec@2.0-impl"
-#include <android-base/logging.h>
-
-#include <hardware/hardware.h>
-#include <hardware/hdmi_cec.h>
-#include "HdmiCec.h"
-
-namespace android {
-namespace hardware {
-namespace tv {
-namespace cec {
-namespace V2_0 {
-namespace implementation {
-
-static_assert(CEC_DEVICE_INACTIVE == static_cast<int>(CecDeviceType::INACTIVE),
- "CecDeviceType::INACTIVE must match legacy value.");
-static_assert(CEC_DEVICE_TV == static_cast<int>(CecDeviceType::TV),
- "CecDeviceType::TV must match legacy value.");
-static_assert(CEC_DEVICE_RECORDER == static_cast<int>(CecDeviceType::RECORDER),
- "CecDeviceType::RECORDER must match legacy value.");
-static_assert(CEC_DEVICE_TUNER == static_cast<int>(CecDeviceType::TUNER),
- "CecDeviceType::TUNER must match legacy value.");
-static_assert(CEC_DEVICE_PLAYBACK == static_cast<int>(CecDeviceType::PLAYBACK),
- "CecDeviceType::PLAYBACK must match legacy value.");
-static_assert(CEC_DEVICE_AUDIO_SYSTEM == static_cast<int>(CecDeviceType::AUDIO_SYSTEM),
- "CecDeviceType::AUDIO_SYSTEM must match legacy value.");
-/* TODO: Adjust for cec@2.0
-static_assert(CEC_DEVICE_MAX == static_cast<int>(CecDeviceType::MAX),
- "CecDeviceType::MAX must match legacy value.");
-*/
-static_assert(CEC_ADDR_TV == static_cast<int>(CecLogicalAddress::TV),
- "CecLogicalAddress::TV must match legacy value.");
-static_assert(CEC_ADDR_RECORDER_1 == static_cast<int>(CecLogicalAddress::RECORDER_1),
- "CecLogicalAddress::RECORDER_1 must match legacy value.");
-static_assert(CEC_ADDR_RECORDER_2 == static_cast<int>(CecLogicalAddress::RECORDER_2),
- "CecLogicalAddress::RECORDER_2 must match legacy value.");
-static_assert(CEC_ADDR_TUNER_1 == static_cast<int>(CecLogicalAddress::TUNER_1),
- "CecLogicalAddress::TUNER_1 must match legacy value.");
-static_assert(CEC_ADDR_PLAYBACK_1 == static_cast<int>(CecLogicalAddress::PLAYBACK_1),
- "CecLogicalAddress::PLAYBACK_1 must match legacy value.");
-static_assert(CEC_ADDR_AUDIO_SYSTEM == static_cast<int>(CecLogicalAddress::AUDIO_SYSTEM),
- "CecLogicalAddress::AUDIO_SYSTEM must match legacy value.");
-static_assert(CEC_ADDR_TUNER_2 == static_cast<int>(CecLogicalAddress::TUNER_2),
- "CecLogicalAddress::TUNER_2 must match legacy value.");
-static_assert(CEC_ADDR_TUNER_3 == static_cast<int>(CecLogicalAddress::TUNER_3),
- "CecLogicalAddress::TUNER_3 must match legacy value.");
-static_assert(CEC_ADDR_PLAYBACK_2 == static_cast<int>(CecLogicalAddress::PLAYBACK_2),
- "CecLogicalAddress::PLAYBACK_2 must match legacy value.");
-static_assert(CEC_ADDR_RECORDER_3 == static_cast<int>(CecLogicalAddress::RECORDER_3),
- "CecLogicalAddress::RECORDER_3 must match legacy value.");
-static_assert(CEC_ADDR_TUNER_4 == static_cast<int>(CecLogicalAddress::TUNER_4),
- "CecLogicalAddress::TUNER_4 must match legacy value.");
-static_assert(CEC_ADDR_PLAYBACK_3 == static_cast<int>(CecLogicalAddress::PLAYBACK_3),
- "CecLogicalAddress::PLAYBACK_3 must match legacy value.");
-/* TODO: Adjust for cec@2.0
-static_assert(CEC_ADDR_FREE_USE == static_cast<int>(CecLogicalAddress::FREE_USE),
- "CecLogicalAddress::FREE_USE must match legacy value.");
-*/
-static_assert(CEC_ADDR_UNREGISTERED == static_cast<int>(CecLogicalAddress::UNREGISTERED),
- "CecLogicalAddress::UNREGISTERED must match legacy value.");
-static_assert(CEC_ADDR_BROADCAST == static_cast<int>(CecLogicalAddress::BROADCAST),
- "CecLogicalAddress::BROADCAST must match legacy value.");
-
-static_assert(CEC_MESSAGE_FEATURE_ABORT == static_cast<int>(CecMessageType::FEATURE_ABORT),
- "CecMessageType::FEATURE_ABORT must match legacy value.");
-static_assert(CEC_MESSAGE_IMAGE_VIEW_ON == static_cast<int>(CecMessageType::IMAGE_VIEW_ON),
- "CecMessageType::IMAGE_VIEW_ON must match legacy value.");
-static_assert(CEC_MESSAGE_TUNER_STEP_INCREMENT ==
- static_cast<int>(CecMessageType::TUNER_STEP_INCREMENT),
- "CecMessageType::TUNER_STEP_INCREMENT must match legacy value.");
-static_assert(CEC_MESSAGE_TUNER_STEP_DECREMENT ==
- static_cast<int>(CecMessageType::TUNER_STEP_DECREMENT),
- "CecMessageType::TUNER_STEP_DECREMENT must match legacy value.");
-static_assert(CEC_MESSAGE_TUNER_DEVICE_STATUS ==
- static_cast<int>(CecMessageType::TUNER_DEVICE_STATUS),
- "CecMessageType::TUNER_DEVICE_STATUS must match legacy value.");
-static_assert(CEC_MESSAGE_GIVE_TUNER_DEVICE_STATUS ==
- static_cast<int>(CecMessageType::GIVE_TUNER_DEVICE_STATUS),
- "CecMessageType::GIVE_TUNER_DEVICE_STATUS must match legacy value.");
-static_assert(CEC_MESSAGE_RECORD_ON == static_cast<int>(CecMessageType::RECORD_ON),
- "CecMessageType::RECORD_ON must match legacy value.");
-static_assert(CEC_MESSAGE_RECORD_STATUS == static_cast<int>(CecMessageType::RECORD_STATUS),
- "CecMessageType::RECORD_STATUS must match legacy value.");
-static_assert(CEC_MESSAGE_RECORD_OFF == static_cast<int>(CecMessageType::RECORD_OFF),
- "CecMessageType::RECORD_OFF must match legacy value.");
-static_assert(CEC_MESSAGE_TEXT_VIEW_ON == static_cast<int>(CecMessageType::TEXT_VIEW_ON),
- "CecMessageType::TEXT_VIEW_ON must match legacy value.");
-static_assert(CEC_MESSAGE_RECORD_TV_SCREEN == static_cast<int>(CecMessageType::RECORD_TV_SCREEN),
- "CecMessageType::RECORD_TV_SCREEN must match legacy value.");
-static_assert(CEC_MESSAGE_GIVE_DECK_STATUS == static_cast<int>(CecMessageType::GIVE_DECK_STATUS),
- "CecMessageType::GIVE_DECK_STATUS must match legacy value.");
-static_assert(CEC_MESSAGE_STANDBY == static_cast<int>(CecMessageType::STANDBY),
- "CecMessageType::STANDBY must match legacy value.");
-static_assert(CEC_MESSAGE_PLAY == static_cast<int>(CecMessageType::PLAY),
- "CecMessageType::PLAY must match legacy value.");
-static_assert(CEC_MESSAGE_DECK_CONTROL == static_cast<int>(CecMessageType::DECK_CONTROL),
- "CecMessageType::DECK_CONTROL must match legacy value.");
-static_assert(CEC_MESSAGE_TIMER_CLEARED_STATUS ==
- static_cast<int>(CecMessageType::TIMER_CLEARED_STATUS),
- "CecMessageType::TIMER_CLEARED_STATUS must match legacy value.");
-static_assert(CEC_MESSAGE_USER_CONTROL_PRESSED ==
- static_cast<int>(CecMessageType::USER_CONTROL_PRESSED),
- "CecMessageType::USER_CONTROL_PRESSED must match legacy value.");
-static_assert(CEC_MESSAGE_USER_CONTROL_RELEASED ==
- static_cast<int>(CecMessageType::USER_CONTROL_RELEASED),
- "CecMessageType::USER_CONTROL_RELEASED must match legacy value.");
-static_assert(CEC_MESSAGE_GIVE_OSD_NAME == static_cast<int>(CecMessageType::GIVE_OSD_NAME),
- "CecMessageType::GIVE_OSD_NAME must match legacy value.");
-static_assert(CEC_MESSAGE_SET_OSD_NAME == static_cast<int>(CecMessageType::SET_OSD_NAME),
- "CecMessageType::SET_OSD_NAME must match legacy value.");
-static_assert(CEC_MESSAGE_SYSTEM_AUDIO_MODE_REQUEST ==
- static_cast<int>(CecMessageType::SYSTEM_AUDIO_MODE_REQUEST),
- "CecMessageType::SYSTEM_AUDIO_MODE_REQUEST must match legacy value.");
-static_assert(CEC_MESSAGE_GIVE_AUDIO_STATUS == static_cast<int>(CecMessageType::GIVE_AUDIO_STATUS),
- "CecMessageType::GIVE_AUDIO_STATUS must match legacy value.");
-static_assert(CEC_MESSAGE_SET_SYSTEM_AUDIO_MODE ==
- static_cast<int>(CecMessageType::SET_SYSTEM_AUDIO_MODE),
- "CecMessageType::SET_SYSTEM_AUDIO_MODE must match legacy value.");
-static_assert(CEC_MESSAGE_REPORT_AUDIO_STATUS ==
- static_cast<int>(CecMessageType::REPORT_AUDIO_STATUS),
- "CecMessageType::REPORT_AUDIO_STATUS must match legacy value.");
-static_assert(CEC_MESSAGE_GIVE_SYSTEM_AUDIO_MODE_STATUS ==
- static_cast<int>(CecMessageType::GIVE_SYSTEM_AUDIO_MODE_STATUS),
- "CecMessageType::GIVE_SYSTEM_AUDIO_MODE_STATUS must match legacy value.");
-static_assert(CEC_MESSAGE_SYSTEM_AUDIO_MODE_STATUS ==
- static_cast<int>(CecMessageType::SYSTEM_AUDIO_MODE_STATUS),
- "CecMessageType::SYSTEM_AUDIO_MODE_STATUS must match legacy value.");
-static_assert(CEC_MESSAGE_ROUTING_CHANGE == static_cast<int>(CecMessageType::ROUTING_CHANGE),
- "CecMessageType::ROUTING_CHANGE must match legacy value.");
-static_assert(CEC_MESSAGE_ROUTING_INFORMATION ==
- static_cast<int>(CecMessageType::ROUTING_INFORMATION),
- "CecMessageType::ROUTING_INFORMATION must match legacy value.");
-static_assert(CEC_MESSAGE_ACTIVE_SOURCE == static_cast<int>(CecMessageType::ACTIVE_SOURCE),
- "CecMessageType::ACTIVE_SOURCE must match legacy value.");
-static_assert(CEC_MESSAGE_GIVE_PHYSICAL_ADDRESS ==
- static_cast<int>(CecMessageType::GIVE_PHYSICAL_ADDRESS),
- "CecMessageType::GIVE_PHYSICAL_ADDRESS must match legacy value.");
-static_assert(CEC_MESSAGE_REPORT_PHYSICAL_ADDRESS ==
- static_cast<int>(CecMessageType::REPORT_PHYSICAL_ADDRESS),
- "CecMessageType::REPORT_PHYSICAL_ADDRESS must match legacy value.");
-static_assert(CEC_MESSAGE_REQUEST_ACTIVE_SOURCE ==
- static_cast<int>(CecMessageType::REQUEST_ACTIVE_SOURCE),
- "CecMessageType::REQUEST_ACTIVE_SOURCE must match legacy value.");
-static_assert(CEC_MESSAGE_SET_STREAM_PATH == static_cast<int>(CecMessageType::SET_STREAM_PATH),
- "CecMessageType::SET_STREAM_PATH must match legacy value.");
-static_assert(CEC_MESSAGE_DEVICE_VENDOR_ID == static_cast<int>(CecMessageType::DEVICE_VENDOR_ID),
- "CecMessageType::DEVICE_VENDOR_ID must match legacy value.");
-static_assert(CEC_MESSAGE_VENDOR_COMMAND == static_cast<int>(CecMessageType::VENDOR_COMMAND),
- "CecMessageType::VENDOR_COMMAND must match legacy value.");
-static_assert(CEC_MESSAGE_VENDOR_REMOTE_BUTTON_DOWN ==
- static_cast<int>(CecMessageType::VENDOR_REMOTE_BUTTON_DOWN),
- "CecMessageType::VENDOR_REMOTE_BUTTON_DOWN must match legacy value.");
-static_assert(CEC_MESSAGE_VENDOR_REMOTE_BUTTON_UP ==
- static_cast<int>(CecMessageType::VENDOR_REMOTE_BUTTON_UP),
- "CecMessageType::VENDOR_REMOTE_BUTTON_UP must match legacy value.");
-static_assert(CEC_MESSAGE_GIVE_DEVICE_VENDOR_ID ==
- static_cast<int>(CecMessageType::GIVE_DEVICE_VENDOR_ID),
- "CecMessageType::GIVE_DEVICE_VENDOR_ID must match legacy value.");
-static_assert(CEC_MESSAGE_MENU_REQUEST == static_cast<int>(CecMessageType::MENU_REQUEST),
- "CecMessageType::MENU_REQUEST must match legacy value.");
-static_assert(CEC_MESSAGE_MENU_STATUS == static_cast<int>(CecMessageType::MENU_STATUS),
- "CecMessageType::MENU_STATUS must match legacy value.");
-static_assert(CEC_MESSAGE_GIVE_DEVICE_POWER_STATUS ==
- static_cast<int>(CecMessageType::GIVE_DEVICE_POWER_STATUS),
- "CecMessageType::GIVE_DEVICE_POWER_STATUS must match legacy value.");
-static_assert(CEC_MESSAGE_REPORT_POWER_STATUS ==
- static_cast<int>(CecMessageType::REPORT_POWER_STATUS),
- "CecMessageType::REPORT_POWER_STATUS must match legacy value.");
-static_assert(CEC_MESSAGE_GET_MENU_LANGUAGE == static_cast<int>(CecMessageType::GET_MENU_LANGUAGE),
- "CecMessageType::GET_MENU_LANGUAGE must match legacy value.");
-static_assert(CEC_MESSAGE_SELECT_ANALOG_SERVICE ==
- static_cast<int>(CecMessageType::SELECT_ANALOG_SERVICE),
- "CecMessageType::SELECT_ANALOG_SERVICE must match legacy value.");
-static_assert(CEC_MESSAGE_SELECT_DIGITAL_SERVICE ==
- static_cast<int>(CecMessageType::SELECT_DIGITAL_SERVICE),
- "CecMessageType::SELECT_DIGITAL_SERVICE must match legacy value.");
-static_assert(CEC_MESSAGE_SET_DIGITAL_TIMER == static_cast<int>(CecMessageType::SET_DIGITAL_TIMER),
- "CecMessageType::SET_DIGITAL_TIMER must match legacy value.");
-static_assert(CEC_MESSAGE_CLEAR_DIGITAL_TIMER ==
- static_cast<int>(CecMessageType::CLEAR_DIGITAL_TIMER),
- "CecMessageType::CLEAR_DIGITAL_TIMER must match legacy value.");
-static_assert(CEC_MESSAGE_SET_AUDIO_RATE == static_cast<int>(CecMessageType::SET_AUDIO_RATE),
- "CecMessageType::SET_AUDIO_RATE must match legacy value.");
-static_assert(CEC_MESSAGE_INACTIVE_SOURCE == static_cast<int>(CecMessageType::INACTIVE_SOURCE),
- "CecMessageType::INACTIVE_SOURCE must match legacy value.");
-static_assert(CEC_MESSAGE_CEC_VERSION == static_cast<int>(CecMessageType::CEC_VERSION),
- "CecMessageType::CEC_VERSION must match legacy value.");
-static_assert(CEC_MESSAGE_GET_CEC_VERSION == static_cast<int>(CecMessageType::GET_CEC_VERSION),
- "CecMessageType::GET_CEC_VERSION must match legacy value.");
-static_assert(CEC_MESSAGE_VENDOR_COMMAND_WITH_ID ==
- static_cast<int>(CecMessageType::VENDOR_COMMAND_WITH_ID),
- "CecMessageType::VENDOR_COMMAND_WITH_ID must match legacy value.");
-static_assert(CEC_MESSAGE_CLEAR_EXTERNAL_TIMER ==
- static_cast<int>(CecMessageType::CLEAR_EXTERNAL_TIMER),
- "CecMessageType::CLEAR_EXTERNAL_TIMER must match legacy value.");
-static_assert(CEC_MESSAGE_SET_EXTERNAL_TIMER ==
- static_cast<int>(CecMessageType::SET_EXTERNAL_TIMER),
- "CecMessageType::SET_EXTERNAL_TIMER must match legacy value.");
-static_assert(CEC_MESSAGE_INITIATE_ARC == static_cast<int>(CecMessageType::INITIATE_ARC),
- "CecMessageType::INITIATE_ARC must match legacy value.");
-static_assert(CEC_MESSAGE_REPORT_ARC_INITIATED ==
- static_cast<int>(CecMessageType::REPORT_ARC_INITIATED),
- "CecMessageType::REPORT_ARC_INITIATED must match legacy value.");
-static_assert(CEC_MESSAGE_REPORT_ARC_TERMINATED ==
- static_cast<int>(CecMessageType::REPORT_ARC_TERMINATED),
- "CecMessageType::REPORT_ARC_TERMINATED must match legacy value.");
-static_assert(CEC_MESSAGE_REQUEST_ARC_INITIATION ==
- static_cast<int>(CecMessageType::REQUEST_ARC_INITIATION),
- "CecMessageType::REQUEST_ARC_INITIATION must match legacy value.");
-static_assert(CEC_MESSAGE_REQUEST_ARC_TERMINATION ==
- static_cast<int>(CecMessageType::REQUEST_ARC_TERMINATION),
- "CecMessageType::REQUEST_ARC_TERMINATION must match legacy value.");
-static_assert(CEC_MESSAGE_TERMINATE_ARC == static_cast<int>(CecMessageType::TERMINATE_ARC),
- "CecMessageType::TERMINATE_ARC must match legacy value.");
-static_assert(CEC_MESSAGE_ABORT == static_cast<int>(CecMessageType::ABORT),
- "CecMessageType::ABORT must match legacy value.");
-
-static_assert(ABORT_UNRECOGNIZED_MODE == static_cast<int>(AbortReason::UNRECOGNIZED_MODE),
- "AbortReason::UNRECOGNIZED_MODE must match legacy value.");
-static_assert(ABORT_NOT_IN_CORRECT_MODE == static_cast<int>(AbortReason::NOT_IN_CORRECT_MODE),
- "AbortReason::NOT_IN_CORRECT_MODE must match legacy value.");
-static_assert(ABORT_CANNOT_PROVIDE_SOURCE == static_cast<int>(AbortReason::CANNOT_PROVIDE_SOURCE),
- "AbortReason::CANNOT_PROVIDE_SOURCE must match legacy value.");
-static_assert(ABORT_INVALID_OPERAND == static_cast<int>(AbortReason::INVALID_OPERAND),
- "AbortReason::INVALID_OPERAND must match legacy value.");
-static_assert(ABORT_REFUSED == static_cast<int>(AbortReason::REFUSED),
- "AbortReason::REFUSED must match legacy value.");
-static_assert(ABORT_UNABLE_TO_DETERMINE == static_cast<int>(AbortReason::UNABLE_TO_DETERMINE),
- "AbortReason::UNABLE_TO_DETERMINE must match legacy value.");
-
-static_assert(HDMI_RESULT_SUCCESS == static_cast<int>(SendMessageResult::SUCCESS),
- "SendMessageResult::SUCCESS must match legacy value.");
-static_assert(HDMI_RESULT_NACK == static_cast<int>(SendMessageResult::NACK),
- "SendMessageResult::NACK must match legacy value.");
-static_assert(HDMI_RESULT_BUSY == static_cast<int>(SendMessageResult::BUSY),
- "SendMessageResult::BUSY must match legacy value.");
-static_assert(HDMI_RESULT_FAIL == static_cast<int>(SendMessageResult::FAIL),
- "SendMessageResult::FAIL must match legacy value.");
-
-static_assert(HDMI_INPUT == static_cast<int>(HdmiPortType::INPUT),
- "HdmiPortType::INPUT must match legacy value.");
-static_assert(HDMI_OUTPUT == static_cast<int>(HdmiPortType::OUTPUT),
- "HdmiPortType::OUTPUT must match legacy value.");
-
-static_assert(HDMI_OPTION_WAKEUP == static_cast<int>(OptionKey::WAKEUP),
- "OptionKey::WAKEUP must match legacy value.");
-static_assert(HDMI_OPTION_ENABLE_CEC == static_cast<int>(OptionKey::ENABLE_CEC),
- "OptionKey::ENABLE_CEC must match legacy value.");
-static_assert(HDMI_OPTION_SYSTEM_CEC_CONTROL == static_cast<int>(OptionKey::SYSTEM_CEC_CONTROL),
- "OptionKey::SYSTEM_CEC_CONTROL must match legacy value.");
-
-sp<IHdmiCecCallback> HdmiCec::mCallback = nullptr;
-
-HdmiCec::HdmiCec(hdmi_cec_device_t* device) : mDevice(device) {}
-
-// Methods from ::android::hardware::tv::cec::V2_0::IHdmiCec follow.
-Return<Result> HdmiCec::addDeviceType(CecDeviceType deviceType) {
- // TODO implement
- if (deviceType <= CecDeviceType::MAX) {
- return Result::SUCCESS;
- } else {
- return Result::FAILURE_INVALID_ARGS;
- }
-}
-
-Return<void> HdmiCec::clearDeviceTypes() {
- // TODO implement
- return Void();
-}
-
-Return<void> HdmiCec::setAllDeviceTypes(CecAllDeviceTypes allDeviceTypes) {
- // TODO implement
- if (allDeviceTypes == 1) {
- }
- return Void();
-}
-
-Return<void> HdmiCec::setDeviceFeatures(CecDeviceType deviceType,
- CecDeviceFeatures /* deviceFeatures */) {
- // TODO implement
- if (deviceType != CecDeviceType::MAX) {
- }
- return Void();
-}
-
-Return<void> HdmiCec::setRcProfile(CecDeviceType deviceType, const CecRcProfile& /* rcProfile */) {
- // TODO implement
- if (deviceType != CecDeviceType::MAX) {
- }
- return Void();
-}
-
-Return<void> HdmiCec::readDeviceInfo(CecLogicalAddress logicalAddress,
- CecPhysicalAddress physicalAddress,
- const readDeviceInfo_cb _hidl_cb) {
- // TODO implement
- CecDeviceInfo deviceInfo;
-
- if (logicalAddress == CecLogicalAddress::TV) {
- _hidl_cb(Result::SUCCESS, deviceInfo);
- if (physicalAddress) {
- }
- }
- return Void();
-}
-
-Return<SendMessageResult> HdmiCec::sendMessage(const CecMessage& message) {
- cec_message_t legacyMessage{
- .initiator = static_cast<cec_logical_address_t>(message.initiator),
- .destination = static_cast<cec_logical_address_t>(message.destination),
- .length = message.body.size(),
- };
- for (size_t i = 0; i < message.body.size(); ++i) {
- legacyMessage.body[i] = static_cast<unsigned char>(message.body[i]);
- }
- return static_cast<SendMessageResult>(mDevice->send_message(mDevice, &legacyMessage));
-}
-
-Return<void> HdmiCec::setCallback(const sp<IHdmiCecCallback>& callback) {
- if (mCallback != nullptr) {
- mCallback->unlinkToDeath(this);
- mCallback = nullptr;
- }
-
- if (callback != nullptr) {
- mCallback = callback;
- mCallback->linkToDeath(this, 0 /*cookie*/);
- mDevice->register_event_callback(mDevice, eventCallback, nullptr);
- }
- return Void();
-}
-
-Return<void> HdmiCec::getPortInfo(getPortInfo_cb _hidl_cb) {
- struct hdmi_port_info* legacyPorts;
- int numPorts;
- hidl_vec<HdmiPortInfo> portInfos;
- mDevice->get_port_info(mDevice, &legacyPorts, &numPorts);
- portInfos.resize(numPorts);
- for (int i = 0; i < numPorts; ++i) {
- portInfos[i] = {.type = static_cast<HdmiPortType>(legacyPorts[i].type),
- .portId = static_cast<HdmiPortId>(legacyPorts[i].port_id),
- .cecSupported = legacyPorts[i].cec_supported != 0,
- .arcSupported = legacyPorts[i].arc_supported != 0,
- .physicalAddress = legacyPorts[i].physical_address};
- }
- _hidl_cb(portInfos);
- return Void();
-}
-
-Return<void> HdmiCec::setOption(OptionKey key, bool value) {
- mDevice->set_option(mDevice, static_cast<int>(key), value ? 1 : 0);
- return Void();
-}
-
-Return<void> HdmiCec::setLanguage(const hidl_string& language) {
- if (language.size() != 3) {
- LOG(ERROR) << "Wrong language code: expected 3 letters, but it was " << language.size()
- << ".";
- return Void();
- }
- const char* languageStr = language.c_str();
- int convertedLanguage = ((languageStr[0] & 0xFF) << 16) | ((languageStr[1] & 0xFF) << 8) |
- (languageStr[2] & 0xFF);
- mDevice->set_option(mDevice, HDMI_OPTION_SET_LANG, convertedLanguage);
- return Void();
-}
-
-Return<void> HdmiCec::enableAudioReturnChannel(HdmiPortId portId, bool enable) {
- mDevice->set_audio_return_channel(mDevice, portId, enable ? 1 : 0);
- return Void();
-}
-
-Return<bool> HdmiCec::isConnected(HdmiPortId portId) {
- return mDevice->is_connected(mDevice, portId) > 0;
-}
-
-IHdmiCec* HIDL_FETCH_IHdmiCec(const char* hal) {
- hdmi_cec_device_t* hdmi_cec_device;
- int ret = 0;
- const hw_module_t* hw_module = nullptr;
-
- ret = hw_get_module(HDMI_CEC_HARDWARE_MODULE_ID, &hw_module);
- if (ret == 0) {
- ret = hdmi_cec_open(hw_module, &hdmi_cec_device);
- if (ret != 0) {
- LOG(ERROR) << "hdmi_cec_open " << hal << " failed: " << ret;
- }
- } else {
- LOG(ERROR) << "hw_get_module " << hal << " failed: " << ret;
- }
-
- if (ret == 0) {
- return new HdmiCec(hdmi_cec_device);
- } else {
- LOG(ERROR) << "Passthrough failed to load legacy HAL.";
- return nullptr;
- }
-}
-
-} // namespace implementation
-} // namespace V2_0
-} // namespace cec
-} // namespace tv
-} // namespace hardware
-} // namespace android
diff --git a/tv/cec/2.0/default/HdmiCec.h b/tv/cec/2.0/default/HdmiCec.h
deleted file mode 100644
index ab54770..0000000
--- a/tv/cec/2.0/default/HdmiCec.h
+++ /dev/null
@@ -1,115 +0,0 @@
-/*
- * 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_TV_CEC_V2_0_HDMICEC_H
-#define ANDROID_HARDWARE_TV_CEC_V2_0_HDMICEC_H
-
-#include <algorithm>
-
-#include <android/hardware/tv/cec/2.0/IHdmiCec.h>
-#include <hardware/hardware.h>
-#include <hardware/hdmi_cec.h>
-#include <hidl/Status.h>
-
-#include <hidl/MQDescriptor.h>
-namespace android {
-namespace hardware {
-namespace tv {
-namespace cec {
-namespace V2_0 {
-namespace implementation {
-
-using ::android::sp;
-using ::android::hardware::hidl_string;
-using ::android::hardware::hidl_vec;
-using ::android::hardware::Return;
-using ::android::hardware::Void;
-using ::android::hardware::tv::cec::V2_0::CecLogicalAddress;
-using ::android::hardware::tv::cec::V2_0::CecMessage;
-using ::android::hardware::tv::cec::V2_0::CecPhysicalAddress;
-using ::android::hardware::tv::cec::V2_0::HdmiPortId;
-using ::android::hardware::tv::cec::V2_0::HdmiPortInfo;
-using ::android::hardware::tv::cec::V2_0::IHdmiCec;
-using ::android::hardware::tv::cec::V2_0::IHdmiCecCallback;
-using ::android::hardware::tv::cec::V2_0::MaxLength;
-using ::android::hardware::tv::cec::V2_0::OptionKey;
-using ::android::hardware::tv::cec::V2_0::Result;
-using ::android::hardware::tv::cec::V2_0::SendMessageResult;
-
-struct HdmiCec : public IHdmiCec, public hidl_death_recipient {
- HdmiCec(hdmi_cec_device_t* device);
- // Methods from ::android::hardware::tv::cec::V2_0::IHdmiCec follow.
- Return<Result> addDeviceType(CecDeviceType deviceType) override;
- Return<void> clearDeviceTypes() override;
- Return<void> setAllDeviceTypes(CecAllDeviceTypes allDeviceTypes) override;
- Return<void> setDeviceFeatures(CecDeviceType deviceType,
- CecDeviceFeatures /* deviceFeatures */) override;
- Return<void> setRcProfile(CecDeviceType deviceType,
- const CecRcProfile& /* rcProfile */) override;
- Return<void> readDeviceInfo(CecLogicalAddress logicalAddress,
- CecPhysicalAddress physicalAddress,
- const readDeviceInfo_cb _hidl_cb) override;
- Return<SendMessageResult> sendMessage(const CecMessage& message) override;
- Return<void> setCallback(const sp<IHdmiCecCallback>& callback) override;
- Return<void> getPortInfo(getPortInfo_cb _hidl_cb) override;
- Return<void> setOption(OptionKey key, bool value) override;
- Return<void> setLanguage(const hidl_string& language) override;
- Return<void> enableAudioReturnChannel(HdmiPortId portId, bool enable) override;
- Return<bool> isConnected(HdmiPortId portId) override;
-
- static void eventCallback(const hdmi_event_t* event, void* /* arg */) {
- if (mCallback != nullptr && event != nullptr) {
- if (event->type == HDMI_EVENT_CEC_MESSAGE) {
- size_t length =
- std::min(event->cec.length, static_cast<size_t>(MaxLength::MESSAGE_BODY));
- CecMessage cecMessage{
- .initiator = static_cast<CecLogicalAddress>(event->cec.initiator),
- .destination = static_cast<CecLogicalAddress>(event->cec.destination),
- };
- cecMessage.body.resize(length);
- for (size_t i = 0; i < length; ++i) {
- cecMessage.body[i] = static_cast<uint8_t>(event->cec.body[i]);
- }
- mCallback->onCecMessage(cecMessage);
- } else if (event->type == HDMI_EVENT_HOT_PLUG) {
- HotplugEvent hotplugEvent{
- .connected = event->hotplug.connected > 0,
- .portId = static_cast<HdmiPortId>(event->hotplug.port_id)};
- mCallback->onHotplugEvent(hotplugEvent);
- }
- }
- }
-
- virtual void serviceDied(uint64_t /*cookie*/,
- const wp<::android::hidl::base::V1_0::IBase>& /*who*/) {
- setCallback(nullptr);
- }
-
- private:
- static sp<IHdmiCecCallback> mCallback;
- const hdmi_cec_device_t* mDevice;
-};
-
-extern "C" IHdmiCec* HIDL_FETCH_IHdmiCec(const char* name);
-
-} // namespace implementation
-} // namespace V2_0
-} // namespace cec
-} // namespace tv
-} // namespace hardware
-} // namespace android
-
-#endif // ANDROID_HARDWARE_TV_CEC_V2_0_HDMICEC_H
diff --git a/tv/cec/2.0/default/OWNERS b/tv/cec/2.0/default/OWNERS
deleted file mode 100644
index 1b3d095..0000000
--- a/tv/cec/2.0/default/OWNERS
+++ /dev/null
@@ -1,4 +0,0 @@
-nchalko@google.com
-amyjojo@google.com
-shubang@google.com
-quxiangfang@google.com
diff --git a/tv/cec/2.0/default/android.hardware.tv.cec@2.0-service.rc b/tv/cec/2.0/default/android.hardware.tv.cec@2.0-service.rc
deleted file mode 100644
index 1e8cd80..0000000
--- a/tv/cec/2.0/default/android.hardware.tv.cec@2.0-service.rc
+++ /dev/null
@@ -1,4 +0,0 @@
-service vendor.cec-hal-2-0 /vendor/bin/hw/android.hardware.tv.cec@2.0-service
- class hal
- user system
- group system
diff --git a/tv/cec/2.0/default/android.hardware.tv.cec@2.0-service.xml b/tv/cec/2.0/default/android.hardware.tv.cec@2.0-service.xml
deleted file mode 100644
index 61fb1bb..0000000
--- a/tv/cec/2.0/default/android.hardware.tv.cec@2.0-service.xml
+++ /dev/null
@@ -1,11 +0,0 @@
-<manifest version="1.0" type="device">
- <hal format="hidl">
- <name>android.hardware.tv.cec</name>
- <transport>hwbinder</transport>
- <version>2.0</version>
- <interface>
- <name>IHdmiCec</name>
- <instance>default</instance>
- </interface>
- </hal>
-</manifest>
diff --git a/tv/cec/2.0/default/service.cpp b/tv/cec/2.0/default/service.cpp
deleted file mode 100644
index dacc38c..0000000
--- a/tv/cec/2.0/default/service.cpp
+++ /dev/null
@@ -1,27 +0,0 @@
-/*
- * 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 "android.hardware.tv.cec@2.0-service"
-
-#include <android/hardware/tv/cec/2.0/IHdmiCec.h>
-#include <hidl/LegacySupport.h>
-
-using android::hardware::defaultPassthroughServiceImplementation;
-using android::hardware::tv::cec::V2_0::IHdmiCec;
-
-int main() {
- return defaultPassthroughServiceImplementation<IHdmiCec>();
-}
diff --git a/tv/cec/2.0/types.hal b/tv/cec/2.0/types.hal
deleted file mode 100644
index cad6c39..0000000
--- a/tv/cec/2.0/types.hal
+++ /dev/null
@@ -1,548 +0,0 @@
-/*
- * 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.cec@2.0;
-
-import android.hidl.safe_union@1.0;
-
-/**
- * CEC device type as specified in CEC Table 11-7 of the CEC spec 2.0b.
- */
-enum CecDeviceType : int32_t {
- INACTIVE = -1,
- TV = 0,
- RECORDER = 1,
- TUNER = 3,
- PLAYBACK = 4,
- AUDIO_SYSTEM = 5,
- PURE_CEC_SWITCH = 6,
- PROCESSOR = 7,
- MAX = PROCESSOR,
-};
-
-/**
- * CEC logical address as specified in CEC Table 11-9 of the CEC spec 2.0b.
- */
-enum CecLogicalAddress : int32_t {
- TV = 0,
- RECORDER_1 = 1,
- RECORDER_2 = 2,
- TUNER_1 = 3,
- PLAYBACK_1 = 4,
- AUDIO_SYSTEM = 5,
- TUNER_2 = 6,
- TUNER_3 = 7,
- PLAYBACK_2 = 8,
- RECORDER_3 = 9,
- TUNER_4 = 10,
- PLAYBACK_3 = 11,
- BACKUP_1 = 12, // backup1 for Playback/Recording/Tuner/Processor device
- BACKUP_2 = 13, // backup2 for Playback/Recording/Tuner/Processor device
- SPECIFIC_USE = 14,
- UNREGISTERED = 15, // as Initiator address
- BROADCAST = 15, // as Destination address
-};
-
-/**
- * HDMI CEC message types.
- *
- * The assigned values represent opcode used in CEC frame as specified in
- * Section 11.10 of the CEC spec 2.0b on top of Section CEC 15 of the CEC
- * Spec 1.4b.
- */
-enum CecMessageType : int32_t {
- FEATURE_ABORT = 0x00,
- IMAGE_VIEW_ON = 0x04,
- TUNER_STEP_INCREMENT = 0x05,
- TUNER_STEP_DECREMENT = 0x06,
- TUNER_DEVICE_STATUS = 0x07,
- GIVE_TUNER_DEVICE_STATUS = 0x08,
- RECORD_ON = 0x09,
- RECORD_STATUS = 0x0A,
- RECORD_OFF = 0x0B,
- TEXT_VIEW_ON = 0x0D,
- RECORD_TV_SCREEN = 0x0F,
- GIVE_DECK_STATUS = 0x1A,
- DECK_STATUS = 0x1B,
- SET_MENU_LANGUAGE = 0x32,
- CLEAR_ANALOG_TIMER = 0x33,
- SET_ANALOG_TIMER = 0x34,
- TIMER_STATUS = 0x35,
- STANDBY = 0x36,
- PLAY = 0x41,
- DECK_CONTROL = 0x42,
- TIMER_CLEARED_STATUS = 0x43,
- USER_CONTROL_PRESSED = 0x44,
- USER_CONTROL_RELEASED = 0x45,
- GIVE_OSD_NAME = 0x46,
- SET_OSD_NAME = 0x47,
- SET_OSD_STRING = 0x64,
- SET_TIMER_PROGRAM_TITLE = 0x67,
- SYSTEM_AUDIO_MODE_REQUEST = 0x70,
- GIVE_AUDIO_STATUS = 0x71,
- SET_SYSTEM_AUDIO_MODE = 0x72,
- REPORT_AUDIO_STATUS = 0x7A,
- GIVE_SYSTEM_AUDIO_MODE_STATUS = 0x7D,
- SYSTEM_AUDIO_MODE_STATUS = 0x7E,
- ROUTING_CHANGE = 0x80,
- ROUTING_INFORMATION = 0x81,
- ACTIVE_SOURCE = 0x82,
- GIVE_PHYSICAL_ADDRESS = 0x83,
- REPORT_PHYSICAL_ADDRESS = 0x84,
- REQUEST_ACTIVE_SOURCE = 0x85,
- SET_STREAM_PATH = 0x86,
- DEVICE_VENDOR_ID = 0x87,
- VENDOR_COMMAND = 0x89,
- VENDOR_REMOTE_BUTTON_DOWN = 0x8A,
- VENDOR_REMOTE_BUTTON_UP = 0x8B,
- GIVE_DEVICE_VENDOR_ID = 0x8C,
- MENU_REQUEST = 0x8D,
- MENU_STATUS = 0x8E,
- GIVE_DEVICE_POWER_STATUS = 0x8F,
- REPORT_POWER_STATUS = 0x90,
- GET_MENU_LANGUAGE = 0x91,
- SELECT_ANALOG_SERVICE = 0x92,
- SELECT_DIGITAL_SERVICE = 0x93,
- SET_DIGITAL_TIMER = 0x97,
- CLEAR_DIGITAL_TIMER = 0x99,
- SET_AUDIO_RATE = 0x9A,
- INACTIVE_SOURCE = 0x9D,
- CEC_VERSION = 0x9E,
- GET_CEC_VERSION = 0x9F,
- VENDOR_COMMAND_WITH_ID = 0xA0,
- CLEAR_EXTERNAL_TIMER = 0xA1,
- SET_EXTERNAL_TIMER = 0xA2,
- REPORT_SHORT_AUDIO_DESCRIPTOR = 0xA3,
- REQUEST_SHORT_AUDIO_DESCRIPTOR = 0xA4,
- GIVE_FEATURES = 0XA5,
- REPORT_FEATURES = 0xA6,
- REQUEST_CURRENT_LATENCY = 0xA7,
- REPORT_CURRENT_LATENCY = 0xA8,
- INITIATE_ARC = 0xC0,
- REPORT_ARC_INITIATED = 0xC1,
- REPORT_ARC_TERMINATED = 0xC2,
- REQUEST_ARC_INITIATION = 0xC3,
- REQUEST_ARC_TERMINATION = 0xC4,
- TERMINATE_ARC = 0xC5,
- ABORT = 0xFF,
- POLLING_MESSAGE = 0xFFFFFF00, // used for cec polling message
-};
-
-/**
- * Abort Reason as specified in CEC Table 29 of the CEC spec 1.4b.
- */
-enum AbortReason : int32_t {
- UNRECOGNIZED_MODE = 0,
- NOT_IN_CORRECT_MODE = 1,
- CANNOT_PROVIDE_SOURCE = 2,
- INVALID_OPERAND = 3,
- REFUSED = 4,
- UNABLE_TO_DETERMINE = 5,
-};
-
-enum MaxLength : int32_t {
- MESSAGE_BODY = 14,
-};
-
-struct CecMessage {
- /** logical address of sender */
- CecLogicalAddress initiator;
-
- /** logical address of receiver */
- CecLogicalAddress destination;
-
- /** cec message type */
- CecMessageType cecMessageType;
-
- /**
- * The maximum size of body is 14 (MaxLength::MESSAGE_BODY) as specified in
- * the section 6 of the CEC Spec 1.4b. Overflowed data must be ignored.
- */
- vec<uint8_t> body;
-};
-
-/**
- * error code used for send_message.
- */
-enum SendMessageResult : int32_t {
- SUCCESS = 0,
- NACK = 1, // not acknowledged
- BUSY = 2, // bus is busy
- FAIL = 3,
-};
-
-/**
- * CEC All Device Type Value as specified in Table 11-30 of the CEC spec 2.0b.
- */
-enum CecAllDeviceTypeValue : uint8_t {
- RESERVED_DEVICE_2 = 1 << 0,
- RESERVED_DEVICE_1 = 1 << 1,
- CEC_SWITCH_DEVICE = 1 << 2,
- AUDIO_DEVICE = 1 << 3,
- PLAYBACK_DEVICE = 1 << 4,
- TUNER_DEVICE = 1 << 5,
- RECORDING_DEVICE = 1 << 6,
- TV_DEVICE = 1 << 7,
-};
-
-/**
- * CEC All Device Types
- *
- * It is a combination of all supported type from CecAllDeviceTypeValue.
- * For example a record with tuner functionalitye,
- * cecAllDeviceTypes = ((CecAllDeviceTypeValue::RECORDING_DEVICE)
- * |(CecAllDeviceTypeValue::TUNER_DEVICE))
- */
-typedef bitfield<CecAllDeviceTypeValue> CecAllDeviceTypes;
-
-/**
- * CEC Versions as specified in CEC Table 11-30 of the CEC spec 2.0b.
- */
-enum CecVersion : int32_t {
- V_1_3_A = 0x04,
- V_1_4 = 0x05, // indicate CEC 1.4, 1.4a or 1.4b
- V_2_0 = 0x06,
-};
-
-/**
- * Device Feature
- *
- * It is specified in CEC Table 11-30 of the CEC spec 2.0b. As a uint32 there
- * is room for future extensions aka DeviceFeature2 through DeviceFeature4.
- */
-enum CecDeviceFeature : uint32_t {
- RESERVED = 1 << 0,
- SOURCE_SUPPORT_ARC_RX = 1 << 1,
- SINK_SUPPORT_ARC_TX = 1 << 2,
- SOURCE_SUPPORT_SET_AUDIO_RATE = 1 << 3,
- SUPPORT_CONTROLLED_BY_DECK = 1 << 4,
- TV_SUPPORT_SET_OSD_STRINGS = 1 << 5,
- TV_SUPPORT_RECORD_TV_SCREEN = 1 << 6,
-};
-
-/**
- * CEC Device Features
- *
- * It is a combination of all supported features from CecDeviceFeature.
- * For example a TV with OSD and ARC capabilities,
- * CecDeviceFeatures = ((CecDeviceFeature::TV_SUPPORT_SET_OSD_STRINGS)
- * |(CecDeviceFeature::SINK_SUPPORT_ARC_TX))
- */
-typedef bitfield<CecDeviceFeature> CecDeviceFeatures;
-
-/**
- * Remote Control Profile
- *
- * It is specified in CEC Table 11-30 of the CEC spec 2.0b.
- */
-enum CecRcProfileId : uint8_t {
- NONE = 0, // TV doesn’t support any of these profiles
- RC_PROFILE_1 = 0x02, // minimalistic zapper (low button count)
- RC_PROFILE_2 = 0x06, // intermediate between profile 1 and profile 3
- RC_PROFILE_3 = 0x0A, // typical TV remote
- RC_PROFILE_4 = 0x0E, // extended form of profile 3
-};
-
-/**
- * Remote Control Profile Source
- *
- * It is specified in CEC Table 11-30 of the CEC spec 2.0b.
- */
-enum CecRcProfileSource : uint8_t {
- MEDIA_CONTEXT_SENSITIVE = 1 << 0, // source can handle UI command 0x11
- MEDIA_TO = 1 << 1, // source can handle UI command 0x10
- CONTENTS = 1 << 2, // source can handle UI command 0x0B
- DEVICE_SETUP = 1 << 3, // source can handle UI command 0x0A
- DEVICE_ROOT = 1 << 4, // source can handle UI command 0x09
- SOURCE_FLAG = 1 << 6, // Indicate the profile is for source
-};
-
-/**
- * Remote Control Profile for either TV or Source.
- */
-safe_union CecRcProfile1 {
- /** CEC remote control profile for TV. */
- CecRcProfileId profileId;
-
- /* CEC remote control profile for source
- *
- * It is a combination of all supported profiles from CecRcProfileSource.
- * For example a playback device support root menu and setup menu,
- * profileSource = ((CecRcProfileSource::DEVICE_ROOT)
- * |(CecRcProfileSource::DEVICE_SETUP)
- * |(CecRcProfileSource::SOURCE_FLAG))
- */
- bitfield<CecRcProfileSource> profileSource;
-};
-
-/**
- * CEC Remote Control Profiles
- *
- * CEC 2.0 only use one byte to represent Remote Control Profile.
- */
-struct CecRcProfile {
- CecRcProfile1 rcProfile1;
-};
-
-/**
- * CEC device power states as specified in CEC Table 11-10 of the CEC spec 2.0b
- */
-enum CecPowerState : int8_t {
- ON = 0,
- STANDBY = 1,
- ON_TO_STANDBY = 2,
- STANDBY_TO_ON = 4,
- UNKNOWN = 0xFF, // some devices may not report power status
-};
-
-/** CEC physical address of device */
-typedef uint16_t CecPhysicalAddress;
-
-/**
- * CEC device information
- *
- * It is initially built during addressing specified in CEC section 11.3 of
- * the CEC spec 2.0b. It may be updated with cec devices's status changed.
- */
-struct CecDeviceInfo {
- /** CEC version which device supports */
- CecVersion version;
-
- /** CEC device primary type */
- CecDeviceType devceType;
-
- /** CEC all device types */
- CecAllDeviceTypes allDeviceTypes;
-
- /** CEC device features */
- CecDeviceFeatures deviceFeatures;
-
- /** CEC Device Remote Control Profile */
- CecRcProfile rcProfile;
-
- /** CEC Device Vendor ID */
- uint32_t vendorId;
-
- /** logical address of device */
- CecLogicalAddress logicalAddress;
-
- /** physical of device */
- CecPhysicalAddress physicalAddress;
-
- /** power status of device */
- CecPowerState powerState;
-};
-
-/**
- * Topology Event Type.
- */
-enum CecTopologyEventType : int32_t {
- DEVICE_ADDED,
- DEVICE_REMOVED,
- DEVICE_UPDATED,
-};
-
-/**
- * Topology Event.
- */
-struct CecTopologyEvent {
- CecTopologyEventType eventType;
- CecLogicalAddress logicalAddress;
- CecPhysicalAddress physicalAddress;
-
- /** true if the event is about the device which the system run on */
- bool isHostDevice;
-};
-
-
-/**
- * CEC UI Command Codes as specified in CEC Table 11-31 of the CEC spec 2.0b
- */
-enum CecUICommandCodes : int32_t {
- SELECT_OK = 0x00,
- UP = 0x01,
- DOWN = 0x02,
- LEFT = 0x03,
- RIGHT = 0x04,
- RIGHT_UP = 0x05,
- RIGHT_DOWN = 0x06,
- LEFT_UP = 0x07,
- LEFT_DOWN = 0x08,
- DEVICE_ROOT_MENU = 0x09,
- DEVICE_SETUP_MENU = 0x0A,
- CONTENTS_MENU = 0x0B,
- FAVORITE_MENU = 0x0C,
- BACK = 0x0D,
- MEDIA_TOP_MENU = 0x10,
- MEDIA_CONTEXT_SENSITIVE_MENU = 0x11,
- NUMBER_ENTRY_MODE = 0x1D,
- NUMBER_11 = 0x1E,
- NUMBER_12 = 0x1F,
- NUMBER_0 = 0x20, // or NUMBER 10
- NUMBER_1 = 0x21,
- NUMBER_2 = 0x22,
- NUMBER_3 = 0x23,
- NUMBER_4 = 0x24,
- NUMBER_5 = 0x25,
- NUMBER_6 = 0x26,
- NUMBER_7 = 0x27,
- NUMBER_8 = 0x28,
- NUMBER_9 = 0x29,
- DOT = 0x2A,
- ENTER = 0x2B,
- CLEAR = 0x2C,
- NEXT_FAVORITE = 0x2F,
- CHANNEL_UP = 0x30,
- CHANNEL_DOWN = 0x31,
- PREVIOUS_CHANNEL = 0x32,
- SOUND_SELECT = 0x33,
- INPUT_SELECT = 0x34,
- DISPLAY_INFORMATION = 0x35,
- HELP = 0x36,
- PAGE_UP = 0x37,
- PAGE_DOWN = 0x38,
- POWER = 0x40,
- VOLUME_UP = 0x41,
- VOLUME_DOWN = 0x42,
- MUTE = 0x43,
- PLAY = 0x44,
- STOP = 0x45,
- PAUSE = 0x46,
- RECORD = 0x47,
- REWIND = 0x48,
- FAST_FORWARD = 0x49,
- EJECT = 0x4A,
- SKIP_FORWARD = 0x4B,
- SKIP_BACKWARD = 0x4C,
- STOP_RECORD = 0x4D,
- PAUSE_RECORD = 0x4E,
- ANGLE = 0x50,
- SUB_PICTURE = 0x51,
- VIDEO_ON_DEMAND = 0x52,
- ELECTRONIC_PROGRAM_GUIDE = 0x53,
- TIMER_PROGRAMMING = 0x54,
- INITIAL_CONFIGURATION = 0x55,
- SELECT_BROADCAST_TYPE = 0x56,
- SELECT_SOUND_PRESENTATION = 0x57,
- AUDIO_DESCRIPTION = 0x58,
- INTERNET = 0x59,
- THREE_DIMENSIONAL_MODE = 0x5A,
- PLAY_FUNCTION = 0x60,
- PAUSE_PLAY_FUNCTION = 0x61,
- RECORD_FUNCTION = 0x62,
- PAUSE_RECORD_FUNCTION = 0x63,
- STOP_FUNCTION = 0x64,
- MUTE_FUNCTION = 0x65,
- RESTORE_VOLUME_FUNCTION = 0x66,
- TUNE_FUNCTION = 0x67,
- SELECT_MEDIA_FUNCTION = 0x68,
- SELECT_AV_INPUT_FUNCTION = 0x69,
- SELECT_AUDIO_INPUT_FUNCTION = 0x6A,
- POWER_TOGGLE_FUNCTION = 0x6B,
- POWER_OFF_FUNCTION = 0x6C,
- POWER_ON_FUNCTION = 0x6D,
- F1 = 0x71, // BLUE
- F2 = 0x72, // RED
- F3 = 0x73, // GREEN
- F4 = 0x74, // YELLOW
- F5 = 0x75,
- DATA = 0x76,
-};
-
-/**
- * HDMI port type.
- */
-enum HdmiPortType : int32_t {
- INPUT = 0,
- OUTPUT = 1,
-};
-
-/**
- * Options used for IHdmiCec.setOption()
- */
-enum OptionKey : int32_t {
- /**
- * When set to false, HAL does not wake up the system upon receiving <Image
- * View On> or <Text View On>. Used when user changes the TV settings to
- * disable the auto TV on functionality.
- * Deprecated since <Image View On> and <Text View On> become mandatory
- * featrues for CEC device. Use ENABLE_CEC OptionKey to disable CEC
- * functionality instead.
- * True by Default
- */
- WAKEUP = 1,
-
- /**
- * When set to false, all the CEC commands are discarded. if logical address
- * is ever used, it shall be released. Used when user changes the TV
- * settings to disable CEC functionality.
- * True by default.
- *
- */
- ENABLE_CEC = 2,
-
- /**
- * Setting this flag to false means Android system must stop handling CEC
- * service and yield the control over to the microprocessor that is powered
- * on through the standby mode.The microprocessor shall keep current logical
- * and physical address. It shall response POLLING_MESSAGE, GIVE_FEATURES,
- * GIVE_DEVICE_POWER_STATUS,GIVE_DEVICE_VENDOR_ID and GIVE_PHYSICAL_ADDRESS
- * to allow other CEC devices to build CEC devices map specified in CEC
- * section 11.3 of the CEC spec 2.0b.
- * When set to true, the system must gain the control over, hence telling
- * the microprocessor to start forwarding CEC messages to Android system.
- * For example, this may be called when system goes in and out of
- * standby mode to notify the microprocessor that it should start/stop
- * handling CEC commands on behalf of the system.
- * True by default.
- */
- SYSTEM_CEC_CONTROL = 3,
-
- /* Option 4 not used */
-};
-
-/**
- * Hdmi port ID.
- *
- * It shall start from 1 which corresponds to HDMI "port 1".
- */
-typedef uint32_t HdmiPortId;
-
-/** Hdmi hotplug event */
-struct HotplugEvent {
- bool connected;
- HdmiPortId portId;
-};
-
-/**
- * HDMI port descriptor
- */
-struct HdmiPortInfo {
- HdmiPortType type;
- HdmiPortId portId;
- bool cecSupported;
- bool arcSupported;
- CecPhysicalAddress physicalAddress;
-};
-
-enum Result : int32_t {
- SUCCESS = 0,
- FAILURE_UNKNOWN = 1,
- FAILURE_INVALID_ARGS = 2,
- FAILURE_INVALID_STATE = 3,
- FAILURE_NOT_SUPPORTED = 4,
- FAILURE_BUSY = 5,
-};