IVGCVSW-5274 'Update ConvertQuantizedLstm function to use ShapeInferenceMethod'
* Enabled Dynamic Tensors in QUANTIZED_LSTM operator.
!android-nn-driver:3897
Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>
Change-Id: I415014d19729aac255479099e372e5ff1a6dd3e2
diff --git a/ConversionUtils_1_3.hpp b/ConversionUtils_1_3.hpp
index e696125..445b9ea 100644
--- a/ConversionUtils_1_3.hpp
+++ b/ConversionUtils_1_3.hpp
@@ -600,29 +600,36 @@
}
// Check if the layer is supported
-
- if (IsDynamicTensor(constOutputStateOutInfo) ||
- IsDynamicTensor(cellStateOutInfo) ||
- IsDynamicTensor(constOutputInfo))
+ bool isSupported = false;
+ auto validateFunc = [&](const armnn::TensorInfo& cellStateOutInfo, bool& isSupported)
{
- return Fail("%s: Dynamic output tensors are not supported %d %d %d %d", __func__,
- IsDynamicTensor(constOutputStateOutInfo), IsDynamicTensor(cellStateOutInfo),
- IsDynamicTensor(constOutputInfo));
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsQLstmSupported,
+ data.m_Backends,
+ isSupported,
+ inputInfo,
+ outputStatePrevTimeStepInfo,
+ cellStatePrevTimeStepInfo,
+ constOutputStateOutInfo,
+ cellStateOutInfo,
+ constOutputInfo,
+ desc,
+ paramsInfo);
+ };
+
+ bool isDynamic = false;
+ if (!IsDynamicTensor(constOutputStateOutInfo) &&
+ !IsDynamicTensor(cellStateOutInfo) &&
+ !IsDynamicTensor(constOutputInfo))
+ {
+ validateFunc(outputInfo, isSupported);
+ }
+ else
+ {
+ isDynamic = true;
+ isSupported = AreDynamicTensorsSupported();
}
- bool isSupported = false;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsQLstmSupported,
- data.m_Backends,
- isSupported,
- inputInfo,
- outputStatePrevTimeStepInfo,
- cellStatePrevTimeStepInfo,
- constOutputStateOutInfo,
- cellStateOutInfo,
- constOutputInfo,
- desc,
- paramsInfo);
if (!isSupported)
{
return false;
@@ -635,10 +642,21 @@
outputStatePrevTimeStep.Connect(layer->GetInputSlot(1));
cellStatePrevTimeStep.Connect(layer->GetInputSlot(2));
- return ( SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, 0, model, data,
- &constOutputStateOutInfo) &&
- SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 1, *layer, 1, model, data) &&
- SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 2, *layer, 2, model, data, &constOutputInfo));
+ if (!isDynamic)
+ {
+ return ( SetupAndTrackLayerOutputSlot<HalPolicy>(
+ operation, 0, *layer, 0, model, data, &constOutputStateOutInfo) &&
+ SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 1, *layer, 1, model, data) &&
+ SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 2, *layer, 2, model, data, &constOutputInfo));
+ }
+ else
+ {
+ return ( SetupAndTrackLayerOutputSlot<HalPolicy>(
+ operation, 0, *layer, 0, model, data, &constOutputStateOutInfo) &&
+ SetupAndTrackLayerOutputSlot<HalPolicy>(
+ operation, 1, *layer, 1, model, data, nullptr, validateFunc, true) &&
+ SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 2, *layer, 2, model, data, &constOutputInfo));
+ }
}
template<typename HalPolicy,
diff --git a/test/1.3/QLstm.cpp b/test/1.3/QLstm.cpp
index 608e408..f58ed0a 100644
--- a/test/1.3/QLstm.cpp
+++ b/test/1.3/QLstm.cpp
@@ -863,6 +863,167 @@
compute);
}
+void DynamicOutputQLstmWithNoProjection(armnn::Compute compute)
+{
+ // This replicates android/frameworks/ml/nn/runtime/test/specs/V1_3/qlstm_noprojection.mod.py
+ // with values from android/frameworks/ml/nn/runtime/test/generated/spec_V1_3/qlstm_noprojection.example.cpp
+ // and weights, biases and scalars passed as CONSTANT_COPY tensors (instead of SUBGRAPH_INPUT tensors)
+ // and made cellStateOutput dynamic.
+
+ uint32_t batchSize = 2;
+ uint32_t inputSize = 5;
+ uint32_t outputSize = 4;
+ uint32_t numUnits = 4;
+
+ // Inputs:
+ hidl_vec<uint32_t> inputDimensions{batchSize, inputSize};
+ std::vector<int8_t> inputValue { 90, 102, 13, 26, 38, 102, 13, 26, 51, 64 };
+
+ hidl_vec<uint32_t> inputToInputWeightsDimensions{0, 0};
+ std::vector<int8_t> inputToInputWeightsValue;
+
+ hidl_vec<uint32_t> inputToForgetWeightsDimensions{numUnits, inputSize};
+ std::vector<int8_t> inputToForgetWeightsValue { -77, -13, 38, 25, 115,
+ -64, -25, -51, 38, -102,
+ -51, 38, -64, -51, -77,
+ 38, -51, -77, -64, -64 };
+
+ hidl_vec<uint32_t> inputToCellWeightsDimensions{numUnits, inputSize};
+ std::vector<int8_t> inputToCellWeightsValue { -51, -38, -25, -13, -64,
+ 64, -25, -38, -25, -77,
+ 77, -13, -51, -38, -89,
+ 89, -115, -64, 102, 77 };
+
+ hidl_vec<uint32_t> inputToOutputWeightsDimensions{numUnits, inputSize};
+ std::vector<int8_t> inputToOutputWeightsValue { -102, -51, -25, -115, -13,
+ -89, 38, -38, -102, -25,
+ 77, -25, 51, -89, -38,
+ -64, 13, 64, -77, -51 };
+
+ hidl_vec<uint32_t> recurrentToInputWeightsDimensions{0, 0};
+ std::vector<int8_t> recurrentToInputWeightsValue;
+
+ hidl_vec<uint32_t> recurrentToForgetWeightsDimensions{numUnits, outputSize};
+ std::vector<int8_t> recurrentToForgetWeightsValue { -64, -38, -64, -25,
+ 77, 51, 115, 38,
+ -13, 25, 64, 25,
+ 25, 38, -13, 51 };
+
+ hidl_vec<uint32_t> recurrentToCellWeightsDimensions{numUnits, outputSize};
+ std::vector<int8_t> recurrentToCellWeightsValue { -38, 25, 13, -38,
+ 102, -10, -25, 38,
+ 102, -77, -13, 25,
+ 38, -13, 25, 64 };
+
+ hidl_vec<uint32_t> recurrentToOutputWeightsDimensions{numUnits, outputSize};
+ std::vector<int8_t> recurrentToOutputWeightsValue { 38, -13, 13, -25,
+ -64, -89, -25, -77,
+ -13, -51, -89, -25,
+ 13, 64, 25, -38 };
+
+ hidl_vec<uint32_t> cellToInputWeightsDimensions{0};
+ std::vector<int16_t> cellToInputWeightsValue;
+
+ hidl_vec<uint32_t> cellToForgetWeightsDimensions{0};
+ std::vector<int16_t> cellToForgetWeightsValue;
+
+ hidl_vec<uint32_t> cellToOutputWeightsDimensions{0};
+ std::vector<int16_t> cellToOutputWeightsValue;
+
+ hidl_vec<uint32_t> inputGateBiasDimensions{0};
+ std::vector<int32_t> inputGateBiasValue;
+
+ hidl_vec<uint32_t> forgetGateBiasDimensions{numUnits};
+ std::vector<int32_t> forgetGateBiasValue { 2147484, -6442451, -4294968, 2147484 };
+
+ hidl_vec<uint32_t> cellBiasDimensions{numUnits};
+ std::vector<int32_t> cellBiasValue { -1073742, 15461883, 5368709, 1717987 };
+
+ hidl_vec<uint32_t> outputGateBiasDimensions{numUnits};
+ std::vector<int32_t> outputGateBiasValue { 1073742, -214748, 4294968, 2147484 };
+
+ hidl_vec<uint32_t> projectionWeightsDimensions{0, 0};
+ std::vector<int8_t> projectionWeightsValue;
+
+ hidl_vec<uint32_t> projectionBiasDimensions{0};
+ std::vector<int32_t> projectionBiasValue;
+
+ hidl_vec<uint32_t> outputStateInDimensions{batchSize, outputSize};
+ std::vector<int8_t> outputStateInValue { 0, 0, 0, 0, 0, 0, 0, 0 };
+
+ hidl_vec<uint32_t> cellStateInDimensions{batchSize, numUnits};
+ std::vector<int16_t> cellStateInValue { 0, 0, 0, 0, 0, 0, 0, 0 };
+
+ // Normalization:
+ hidl_vec<uint32_t> inputLayerNormWeightsDimensions{0};
+ std::vector<int16_t> inputLayerNormWeightsValue;
+
+ hidl_vec<uint32_t> forgetLayerNormWeightsDimensions{numUnits};
+ std::vector<int16_t> forgetLayerNormWeightsValue { 6553, 6553, 13107, 9830 };
+
+ hidl_vec<uint32_t> cellLayerNormWeightsDimensions{numUnits};
+ std::vector<int16_t> cellLayerNormWeightsValue { 22937, 6553, 9830, 26214 };
+
+ hidl_vec<uint32_t> outputLayerNormWeightsDimensions{numUnits};
+ std::vector<int16_t> outputLayerNormWeightsValue { 19660, 6553, 6553, 16384 };
+
+ float cellClipValue = 0.0f;
+ float projectionClipValue = 0.0f;
+ float inputIntermediateScale = 0.007059f;
+ float forgetIntermediateScale = 0.007812f;
+ float cellIntermediateScale = 0.007059f;
+ float outputIntermediateScale = 0.007812f;
+ int32_t hiddenStateZeroPoint = 0;
+ float hiddenStateScale = 0.007f;
+
+ // Outputs:
+ hidl_vec<uint32_t> outputStateOutDimensions{batchSize, outputSize};
+ std::vector<int8_t> outputStateOutValue { -15, 21, 14, 20, -15, 15, 5, 27 };
+
+ hidl_vec<uint32_t> cellStateOutDimensions{};
+ std::vector<int16_t> cellStateOutValue { -11692, 9960, 5491, 8861, -9422, 7726, 2056, 13149 };
+
+ hidl_vec<uint32_t> outputDimensions{batchSize, outputSize};
+ std::vector<int8_t> outputValue { -15, 21, 14, 20, -15, 15, 5, 27 };
+
+ QLstmTestImpl(inputDimensions, inputValue,
+ inputToInputWeightsDimensions, inputToInputWeightsValue,
+ inputToForgetWeightsDimensions, inputToForgetWeightsValue,
+ inputToCellWeightsDimensions, inputToCellWeightsValue,
+ inputToOutputWeightsDimensions, inputToOutputWeightsValue,
+ recurrentToInputWeightsDimensions, recurrentToInputWeightsValue,
+ recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue,
+ recurrentToCellWeightsDimensions, recurrentToCellWeightsValue,
+ recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue,
+ cellToInputWeightsDimensions, cellToInputWeightsValue,
+ cellToForgetWeightsDimensions, cellToForgetWeightsValue,
+ cellToOutputWeightsDimensions, cellToOutputWeightsValue,
+ inputGateBiasDimensions, inputGateBiasValue,
+ forgetGateBiasDimensions, forgetGateBiasValue,
+ cellBiasDimensions, cellBiasValue,
+ outputGateBiasDimensions, outputGateBiasValue,
+ projectionWeightsDimensions, projectionWeightsValue,
+ projectionBiasDimensions, projectionBiasValue,
+ outputStateInDimensions, outputStateInValue,
+ cellStateInDimensions, cellStateInValue,
+ inputLayerNormWeightsDimensions, inputLayerNormWeightsValue,
+ forgetLayerNormWeightsDimensions, forgetLayerNormWeightsValue,
+ cellLayerNormWeightsDimensions, cellLayerNormWeightsValue,
+ outputLayerNormWeightsDimensions, outputLayerNormWeightsValue,
+ cellClipValue,
+ projectionClipValue,
+ inputIntermediateScale,
+ forgetIntermediateScale,
+ cellIntermediateScale,
+ outputIntermediateScale,
+ hiddenStateZeroPoint,
+ hiddenStateScale,
+ outputStateOutDimensions, outputStateOutValue,
+ cellStateOutDimensions, cellStateOutValue,
+ outputDimensions, outputValue,
+ compute);
+}
+
} // anonymous namespace
// Support is not added yet
@@ -876,4 +1037,9 @@
QLstmWithNoProjection(sample);
}
+BOOST_DATA_TEST_CASE(DynamicOutputQLSTMWithNoProjectionTest, COMPUTE_DEVICES)
+{
+ DynamicOutputQLstmWithNoProjection(sample);
+}
+
BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file