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//
// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "LstmTestHelper.hpp"
#include <armnn_delegate.hpp>
#include <flatbuffers/flatbuffers.h>
#include <schema_generated.h>
#include <doctest/doctest.h>
namespace armnnDelegate
{
void LstmTest(std::vector<armnn::BackendId>& backends)
{
int32_t batchSize = 2;
int32_t inputSize = 2;
int32_t outputSize = 4;
// cellSize and outputSize have the same size when there is no projection.
int32_t numUnits = outputSize;
std::vector<int32_t> inputShape {batchSize , inputSize};
std::vector<int32_t> cellStateInTensorInfo {batchSize , numUnits};
std::vector<int32_t> outputStateInTensorInfo {batchSize , outputSize};
std::vector<int32_t> scratchBufferTensorInfo {batchSize, numUnits * 4};
std::vector<int32_t> cellStateOutTensorInfo {batchSize, numUnits};
std::vector<int32_t> outputStateOutTensorInfo {batchSize, outputSize};
std::vector<int32_t> outputTensorInfo {batchSize, outputSize};
std::vector<int32_t> tensorInfo4 {numUnits};
std::vector<int32_t> tensorInfo8 {numUnits, 2};
std::vector<int32_t> tensorInfo16 {numUnits, 4};
//tensorInfo8,
bool hasInputToInputWeights = true;
std::vector<float> inputToInputWeights {-0.45018822f, -0.02338299f, -0.0870589f,
-0.34550029f, 0.04266912f, -0.15680569f,
-0.34856534f, 0.43890524f};
std::vector<float> inputToForgetWeights {0.09701663f, 0.20334584f, -0.50592935f,
-0.31343272f, -0.40032279f, 0.44781327f,
0.01387155f, -0.35593212f};
std::vector<float> inputToCellWeights {-0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f,
-0.20583314f, 0.44344562f, 0.22077113f,
-0.29909778f};
std::vector<float> inputToOutputWeights {-0.25065863f, -0.28290087f, 0.04613829f,
0.40525138f, 0.44272184f, 0.03897077f,
-0.1556896f, 0.19487578f};
//tensorInfo16,
bool hasRecurrentToInputWeights = true;
std::vector<float> recurrentToInputWeights {-0.0063535f, -0.2042388f, 0.31454784f,
-0.35746509f, 0.28902304f, 0.08183324f,
-0.16555229f, 0.02286911f, -0.13566875f,
0.03034258f, 0.48091322f, -0.12528998f,
0.24077177f, -0.51332325f, -0.33502164f,
0.10629296f};
std::vector<float> recurrentToForgetWeights {-0.48684245f, -0.06655136f, 0.42224967f,
0.2112639f, 0.27654213f, 0.20864892f,
-0.07646349f, 0.45877004f, 0.00141793f,
-0.14609534f, 0.36447752f, 0.09196436f,
0.28053468f, 0.01560611f, -0.20127171f,
-0.01140004f};
std::vector<float> recurrentToCellWeights {-0.3407414f, 0.24443203f, -0.2078532f,
0.26320225f, 0.05695659f, -0.00123841f,
-0.4744786f, -0.35869038f, -0.06418842f,
-0.13502428f, -0.501764f, 0.22830659f,
-0.46367589f, 0.26016325f, -0.03894562f,
-0.16368064f};
std::vector<float> recurrentToOutputWeights {0.43385774f, -0.17194885f, 0.2718237f,
0.09215671f, 0.24107647f, -0.39835793f,
0.18212086f, 0.01301402f, 0.48572797f,
-0.50656658f, 0.20047462f, -0.20607421f,
-0.51818722f, -0.15390486f, 0.0468148f,
0.39922136f};
// tensorInfo4
bool hasCellToInputWeights = false;
std::vector<float> cellToInputWeights {};
bool hasCellToForgetWeights = false;
std::vector<float> cellToForgetWeights {};
bool hasCellToOutputWeights = false;
std::vector<float> cellToOutputWeights {};
bool hasInputGateBias = true;
std::vector<float> inputGateBias {0., 0., 0., 0.};
std::vector<float> forgetGateBias {1., 1., 1., 1.};
std::vector<float> cellBias {0., 0., 0., 0.};
std::vector<float> outputGateBias {0., 0., 0., 0.};
bool hasProjectionWeights = false;
std::vector<float> projectionWeights;
bool hasProjectionBias = false;
std::vector<float> projectionBias;
bool hasInputLayerNormWeights = false;
std::vector<float> inputLayerNormWeights;
bool hasForgetLayerNormWeights = false;
std::vector<float> forgetLayerNormWeights;
bool hasCellLayerNormWeights = false;
std::vector<float> cellLayerNormWeights;
bool hasOutputLayerNormWeights = false;
std::vector<float> outputLayerNormWeights;
std::vector<float> inputValues {2., 3., 3., 4.};
std::vector<float> expectedOutputValues {-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f,
-0.0185422f, 0.11281417f, 0.24466537f, -0.1826292f};
tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH;
float clippingThresCell = 0.f;
float clippingThresProj = 0.f;
LstmTestImpl<float>(backends,
::tflite::TensorType_FLOAT32,
batchSize,
inputSize,
outputSize,
numUnits,
hasInputToInputWeights,
inputToInputWeights,
inputToForgetWeights,
inputToCellWeights,
inputToOutputWeights,
hasRecurrentToInputWeights,
recurrentToInputWeights,
recurrentToForgetWeights,
recurrentToCellWeights,
recurrentToOutputWeights,
hasCellToInputWeights,
cellToInputWeights,
hasCellToForgetWeights,
cellToForgetWeights,
hasCellToOutputWeights,
cellToOutputWeights,
hasInputGateBias,
inputGateBias,
forgetGateBias,
cellBias,
outputGateBias,
hasProjectionWeights,
projectionWeights,
hasProjectionBias,
projectionBias,
hasInputLayerNormWeights,
inputLayerNormWeights,
hasForgetLayerNormWeights,
forgetLayerNormWeights,
hasCellLayerNormWeights,
cellLayerNormWeights,
hasOutputLayerNormWeights,
outputLayerNormWeights,
inputValues,
expectedOutputValues,
activationFunction,
clippingThresCell,
clippingThresProj);
}
TEST_SUITE("LstmTest_CpuRefTests")
{
TEST_CASE ("LstmTest_CpuRef_Test")
{
std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef};
LstmTest(backends);
}
} //End of TEST_SUITE("Convolution2dTest_CpuRef")
TEST_SUITE("LstmTest_CpuAccTests")
{
TEST_CASE ("LstmTest_CpuAcc_Test")
{
std::vector <armnn::BackendId> backends = {armnn::Compute::CpuAcc};
LstmTest(backends);
}
} //End of TEST_SUITE("Convolution2dTest_CpuAcc")
} // namespace armnnDelegate
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