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Commit 632b4bd9 authored by David Gross's avatar David Gross Committed by Xusong Wang
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Add @V1_2::Capabilities to support all non extension operand types. 

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
parent ad439a04

current.txt

+2 −2
Original line number Diff line number Diff line
@@ -508,11 +508,11 @@ b9422a9aca84df1ff9623dc12c0562abce97716e28d63a965f2bfb88f9ad9607 android.hardwar 
4cb139f729c29d8d6f4ecdab149c4feb571dad8a06e56cd57fcb52e70208bab4 android.hardware.media.c2@1.0::types

4880af120fc1640225abdc2c60bda6d79617d73484d5124913c7278af3b11e2d android.hardware.neuralnetworks@1.2::IBurstCallback

19877e466ad8c6ed42b38050b77bd010cf7800ff365fdc8574f45bbfda03a758 android.hardware.neuralnetworks@1.2::IBurstContext

363821d1b71147b896a08e2a570946db9b9d46f90d9f91b085bd8d3013a2b4d5 android.hardware.neuralnetworks@1.2::IDevice

b83317b66721241887d2770b5ae95fd5af1e77c5daa7530ecb08fae8892f2b43 android.hardware.neuralnetworks@1.2::IDevice

92714960d1a53fc2ec557302b41c7cc93d2636d8364a44bd0f85be0c92927ff8 android.hardware.neuralnetworks@1.2::IExecutionCallback

36e1064c869965dee533c537cefbe87e54db8bd8cd45be7e0e93e00e8a43863a android.hardware.neuralnetworks@1.2::IPreparedModel

e1c734d1545e1a4ae749ff1dd9704a8e594c59aea7c8363159dc258e93e0df3b android.hardware.neuralnetworks@1.2::IPreparedModelCallback

39a6d7cf9bc7290bd90739e971ccad5f35f5cc0faea4a417b59f22c9ca9f1f2a android.hardware.neuralnetworks@1.2::types

d734c2441b602da240fa0e9afe3b612cdc9f3ae9c1db13216f957861d0673c5e 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

neuralnetworks/1.2/IDevice.hal

+11 −0
Original line number Diff line number Diff line
@@ -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.

     *

neuralnetworks/1.2/types.hal

+33 −0
Original line number Diff line number Diff line
@@ -4617,6 +4617,39 @@ 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.

 */

neuralnetworks/1.2/vts/functional/BasicTests.cpp

+26 −1
Original line number Diff line number Diff line
@@ -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) {