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Add @V1_2::Capabilities to support all non extension operand types.

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Performance information in Capabilities is used by the runtime when
it selects the appropriate processor to distribute work to.  Prior to
this CL, Capabilities can only distinguish between float and non-float
data types -- so, for example, float16 and float32 performance is
considered to be the same, and performance for all non-float data types is
considered to be the same.

Bug: 124041010

Test: NeuralNetworksTest_static
Test: VtsHalNeuralnetworksV1_2TargetTest --hal_service_instance=android.hardware.neuralnetworks@1.2::IDevice/sample-all

Change-Id: I83fb5920c1c75afbd7750d793a0b8f3e72a0552c
Merged-In: I83fb5920c1c75afbd7750d793a0b8f3e72a0552c
(cherry picked from commit 632b4bd9b0)
This commit is contained in:
David Gross
2019-03-15 17:26:32 -07:00
parent c8b3d162c2
commit 2d47c80c8e
4 changed files with 72 additions and 3 deletions
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4
current.txt
View File

@@ -446,11 +446,11 @@ dd1ec219f5d2e2b33c6c0bcb92e63bbedb36f7c716413462848f6b6ae74fc864 android.hardwar
2b4a14661e6a38617b7dd0c6ebb66a56a90e564674ac7697a14cb8a0cab92b2f android.hardware.health.storage@1.0::types
4880af120fc1640225abdc2c60bda6d79617d73484d5124913c7278af3b11e2d android.hardware.neuralnetworks@1.2::IBurstCallback
19877e466ad8c6ed42b38050b77bd010cf7800ff365fdc8574f45bbfda03a758 android.hardware.neuralnetworks@1.2::IBurstContext
96249c852dabeefa3a9496ecdfc44681a071c665bfbf88527bf775c88bf1ab1b android.hardware.neuralnetworks@1.2::IDevice
dbe96a8ecf3c1f645585c27568464bc4db3c4b2d9a9624d88da606c59959afbe android.hardware.neuralnetworks@1.2::IDevice
92714960d1a53fc2ec557302b41c7cc93d2636d8364a44bd0f85be0c92927ff8 android.hardware.neuralnetworks@1.2::IExecutionCallback
83885d366f22ada42c00d8854f0b7e7ba4cf73ddf80bb0d8e168ce132cec57ea android.hardware.neuralnetworks@1.2::IPreparedModel
e1c734d1545e1a4ae749ff1dd9704a8e594c59aea7c8363159dc258e93e0df3b android.hardware.neuralnetworks@1.2::IPreparedModelCallback
730c74ee5a3dd61a73f150cf07653e4b928e413b0f228eb004541bfcc22ed245 android.hardware.neuralnetworks@1.2::types
ba7e93fb136cabfde41ac1b035abd87a51f2c260cea89163984e4e9c69b55a5f android.hardware.neuralnetworks@1.2::types
cf7a4ba516a638f9b82a249c91fb603042c2d9ca43fd5aad9cf6c0401ed2a5d7 android.hardware.nfc@1.2::INfc
abf98c2ae08bf765db54edc8068e36d52eb558cff6706b6fd7c18c65a1f3fc18 android.hardware.nfc@1.2::types
4cb252dc6372a874aef666b92a6e9529915aa187521a700f0789065c3c702ead android.hardware.power.stats@1.0::IPowerStats

11
neuralnetworks/1.2/IDevice.hal
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@@ -75,6 +75,17 @@ interface IDevice extends @1.1::IDevice {
*/
getType() generates (ErrorStatus status, DeviceType type);
/**
* Gets the capabilities of a driver.
*
* @return status Error status of the call, must be:
* - NONE if successful
* - DEVICE_UNAVAILABLE if driver is offline or busy
* - GENERAL_FAILURE if there is an unspecified error
* @return capabilities Capabilities of the driver.
*/
getCapabilities_1_2() generates (ErrorStatus status, Capabilities capabilities);
/**
* Gets information about extensions supported by the driver implementation.
*

33
neuralnetworks/1.2/types.hal
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@@ -4582,6 +4582,39 @@ enum DeviceType : int32_t {
ACCELERATOR = 4,
};
/**
* The capabilities of a driver.
*
* Performance of an operation comes from the type of its first operand.
* This represents performance for non extension operand types.
*/
struct Capabilities {
/**
* Driver performance when operating on float32 data but performing
* calculations with range and/or precision as low as that of the IEEE
* 754 16-bit floating-point format.
*/
PerformanceInfo relaxedFloat32toFloat16PerformanceScalar;
PerformanceInfo relaxedFloat32toFloat16PerformanceTensor;
/**
* Driver performance when operating on a particular data type.
* In the case of float32 data, this is used when the calculations
* are not relaxed.
*/
struct OperandPerformance {
OperandType type;
PerformanceInfo info;
};
/**
* Performance by operand type. Must be sorted by OperandType.
* If a particular OperandType is not present in operandPerformance,
* its performance is treated as { .execTime = FLT_MAX, .powerUsage = FLT_MAX }.
*/
vec<OperandPerformance> operandPerformance;
};
/**
* Describes one operation of the model's graph.
*/

27
neuralnetworks/1.2/vts/functional/BasicTests.cpp
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@@ -25,7 +25,7 @@ namespace V1_2 {
namespace vts {
namespace functional {
using V1_1::Capabilities;
using V1_0::PerformanceInfo;
// create device test
TEST_F(NeuralnetworksHidlTest, CreateDevice) {}
@@ -37,6 +37,31 @@ TEST_F(NeuralnetworksHidlTest, StatusTest) {
EXPECT_EQ(DeviceStatus::AVAILABLE, static_cast<DeviceStatus>(status));
}
// initialization
TEST_F(NeuralnetworksHidlTest, GetCapabilitiesTest) {
using OperandPerformance = Capabilities::OperandPerformance;
Return<void> ret = device->getCapabilities_1_2([](ErrorStatus status,
const Capabilities& capabilities) {
EXPECT_EQ(ErrorStatus::NONE, status);
auto isPositive = [](const PerformanceInfo& perf) {
return perf.execTime > 0.0f && perf.powerUsage > 0.0f;
};
EXPECT_TRUE(isPositive(capabilities.relaxedFloat32toFloat16PerformanceScalar));
EXPECT_TRUE(isPositive(capabilities.relaxedFloat32toFloat16PerformanceTensor));
const auto& opPerf = capabilities.operandPerformance;
EXPECT_TRUE(std::all_of(
opPerf.begin(), opPerf.end(),
[isPositive](const OperandPerformance& a) { return isPositive(a.info); }));
EXPECT_TRUE(std::is_sorted(opPerf.begin(), opPerf.end(),
[](const OperandPerformance& a, const OperandPerformance& b) {
return a.type < b.type;
}));
});
EXPECT_TRUE(ret.isOk());
}
// device version test
TEST_F(NeuralnetworksHidlTest, GetDeviceVersionStringTest) {
Return<void> ret = device->getVersionString([](ErrorStatus status, const hidl_string& version) {