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Commit dce38f13 authored by Lev Proleev's avatar Lev Proleev
Browse files

Fix VTS tests. 

* Adds a specification of invalid scale and zero point for TENSOR_BOOL8.
This fixes vts failures for comparison ops.
* Removes (FUNDAMENTAL_MIN - 1) from invalid OperationTypes.
FUNDAMENTAL_MIN is equal to 0 and resulting -1 was statically casted to
uint32_t and passed 4294967295 as an invalid OperationType. However, our
validateOperation function interpreted this ID as an extension ID and
didn't fail.
* Adds mutateOperationOperandTypeSkip for QUANTIZE and DEQUANTIZE.
* Adds removeOperandSkip for BIDIRECTIONAL_SEQUENCE_RNN.

Fix: 121130841
Fix: 123247345
Test: VtsHalNeuralnetworksV1_2TargetTest --hal_service_instance=android.hardware.neuralnetworks@1.2::IDevice/sample-all
Change-Id: Iefb502c6b9301d5470eb4cdaa46d398f1a0e512a
Merged-In: Iefb502c6b9301d5470eb4cdaa46d398f1a0e512a
(cherry picked from commit 923b8c58)
parent e694cb2d

neuralnetworks/1.2/vts/functional/ValidateModel.cpp

+32 −2
Original line number Diff line number Diff line
@@ -157,6 +157,7 @@ static uint32_t getInvalidRank(OperandType type) { 
        case OperandType::UINT32:

        case OperandType::BOOL:

            return 1;

        case OperandType::TENSOR_BOOL8:

        case OperandType::TENSOR_FLOAT16:

        case OperandType::TENSOR_FLOAT32:

        case OperandType::TENSOR_INT32:
@@ -194,6 +195,7 @@ static float getInvalidScale(OperandType type) { 
        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:
@@ -230,6 +232,7 @@ static std::vector<int32_t> getInvalidZeroPoints(OperandType type) { 
        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:
@@ -283,6 +286,7 @@ static void mutateOperand(Operand* operand, OperandType type) { 
            newOperand.scale = 0.0f;

            newOperand.zeroPoint = 0;

            break;

        case OperandType::TENSOR_BOOL8:

        case OperandType::TENSOR_FLOAT16:

        case OperandType::TENSOR_FLOAT32:

            newOperand.dimensions =
@@ -339,6 +343,10 @@ static bool mutateOperationOperandTypeSkip(size_t operand, OperandType type, con 
        // TENSOR_(FLOAT16|FLOAT32|INT32|QUANT8_ASYMM).

        // - CAST's argument can be any of TENSOR_(FLOAT16|FLOAT32|INT32|QUANT8_ASYMM).

        // - RANDOM_MULTINOMIAL's argument can be either TENSOR_FLOAT16 or TENSOR_FLOAT32.

        // - DEQUANTIZE input can be any of

        // TENSOR_(QUANT8_ASYMM|QUANT8_SYMM|QUANT8_SYMM_PER_CHANNEL), output can

        // be of either TENSOR_FLOAT16 or TENSOR_FLOAT32.

        // - QUANTIZE input can be either TENSOR_FLOAT16 or TENSOR_FLOAT32

        // - CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL

        // - DEPTHWISE_CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL

        // - GROUPED_CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
@@ -357,8 +365,22 @@ static bool mutateOperationOperandTypeSkip(size_t operand, OperandType type, con 
                    return true;

                }

            } break;

            case OperationType::QUANTIZE:

            case OperationType::RANDOM_MULTINOMIAL: {

                if (type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32) {

                if (operand == operation.inputs[0] &&

                    (type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32)) {

                    return true;

                }

            } break;

            case OperationType::DEQUANTIZE: {

                if (operand == operation.inputs[0] &&

                    (type == OperandType::TENSOR_QUANT8_ASYMM ||

                     type == OperandType::TENSOR_QUANT8_SYMM ||

                     type == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL)) {

                    return true;

                }

                if (operand == operation.outputs[0] &&

                    (type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32)) {

                    return true;

                }

            } break;
@@ -397,7 +419,6 @@ static void mutateOperationOperandTypeTest(const sp<IDevice>& device, const Mode 
///////////////////////// VALIDATE MODEL OPERATION TYPE /////////////////////////



static const uint32_t invalidOperationTypes[] = {

        static_cast<uint32_t>(OperationTypeRange::FUNDAMENTAL_MIN) - 1,

        static_cast<uint32_t>(OperationTypeRange::FUNDAMENTAL_MAX) + 1,

        static_cast<uint32_t>(OperationTypeRange::OEM_MIN) - 1,

        static_cast<uint32_t>(OperationTypeRange::OEM_MAX) + 1,
@@ -484,6 +505,15 @@ static bool removeOperandSkip(size_t operand, const Model& model) { 
                }

            }

        }

        // BIDIRECTIONAL_SEQUENCE_RNN can have either on or two outputs

        // depending on a mergeOutputs parameter

        if (operation.type == OperationType::BIDIRECTIONAL_SEQUENCE_RNN) {

            for (const size_t outOprand : operation.outputs) {

                if (operand == outOprand) {

                    return true;

                }

            }

        }

    }

    return false;

}