MLCE-1165 Model failing to load when pad is folded into Conv2d
* Skipping the optimization which folds pad and conv2d
together for a specific case: 1x1 filter and
padding size >= filter size
Signed-off-by: Tracy Narine <tracy.narine@arm.com>
Change-Id: I46944e9f736df1ff60469b2d2852e1bba01ab8cd
diff --git a/src/armnn/optimizations/FoldPadIntoLayer2d.hpp b/src/armnn/optimizations/FoldPadIntoLayer2d.hpp
index 2f70e63..5592491 100644
--- a/src/armnn/optimizations/FoldPadIntoLayer2d.hpp
+++ b/src/armnn/optimizations/FoldPadIntoLayer2d.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2021-2023 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2021-2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -154,6 +154,29 @@
return nullptr;
}
+ // Workaround an issue in the compute library. The conv2d algorithm that the
+ // compute library is choosing is not handling the 1x1 filter case when
+ // the padding size >= filter size
+ if constexpr (std::is_same<Layer2dT, armnn::Convolution2dLayer>::value)
+ {
+ // Get filter width and height
+ armnnUtils::DataLayoutIndexed dataLayoutIndex(newLayer2dDescriptor.m_DataLayout);
+ const TensorShape& filterShape = layer2d.GetInputSlot(1).GetTensorInfo().GetShape();
+ unsigned int filterWidth = filterShape[dataLayoutIndex.GetWidthIndex()];
+ unsigned int filterHeight = filterShape[dataLayoutIndex.GetHeightIndex()];
+ // Calculate total padding and check conditions
+ auto horizontalPadding = newLayer2dDescriptor.m_PadLeft + newLayer2dDescriptor.m_PadRight;
+ auto verticalPadding = newLayer2dDescriptor.m_PadTop + newLayer2dDescriptor.m_PadBottom;
+ if ((filterWidth == 1) && (horizontalPadding >= filterWidth))
+ {
+ return nullptr;
+ }
+ else if ((filterHeight == 1) && (verticalPadding >= filterHeight))
+ {
+ return nullptr;
+ }
+ }
+
// Save original parent output slot of the pad layer
OutputSlot& parentSlot = *padLayer.GetInputSlot(0).GetConnectedOutputSlot();
diff --git a/src/armnn/test/optimizations/FoldPadTests.cpp b/src/armnn/test/optimizations/FoldPadTests.cpp
index b2672ea..5c6d1b6 100644
--- a/src/armnn/test/optimizations/FoldPadTests.cpp
+++ b/src/armnn/test/optimizations/FoldPadTests.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2022, 2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -97,6 +97,73 @@
&IsLayerOfType<OutputLayer>));
}
+TEST_CASE("RejectFoldPadLayerIntoConvolution2dLayerWith1x1Filter")
+{
+ // This test can be fixed to check for the folding once the
+ // compute library issue is addressed and the restriction in
+ // FoldPadIntoLayer2dImpl() is removed for the 1x1 case
+
+ Graph graph;
+ const unsigned int inputShape[] = {1, 18, 18, 512};
+ const unsigned int paddedShape[] = {1, 19, 19, 512};
+ const unsigned int weightsShape[] = {512, 1, 1, 512};
+ const unsigned int outputShape[] = {1, 19, 19, 512};
+
+ TensorInfo inputInfo(4, inputShape, DataType::Float32);
+ TensorInfo paddedInfo(4, paddedShape, DataType::Float32);
+ TensorInfo weightsInfo(4, weightsShape, DataType::Float32, 1.0f, 0, true);
+ TensorInfo outputInfo(4, outputShape, DataType::Float32);
+
+ Layer* input = graph.AddLayer<InputLayer>(0, "input");
+ input->GetOutputSlot().SetTensorInfo(inputInfo);
+
+ PadDescriptor padDescriptor({{0, 0},
+ {1, 1},
+ {2, 1},
+ {0, 0}});
+
+ PadLayer* padLayer = graph.AddLayer<PadLayer>(padDescriptor, "pad");
+ padLayer->GetOutputSlot().SetTensorInfo(paddedInfo);
+
+ Convolution2dDescriptor convolution2dDescriptor;
+ convolution2dDescriptor.m_BiasEnabled = false;
+ convolution2dDescriptor.m_StrideX = 1;
+ convolution2dDescriptor.m_StrideY = 1;
+ convolution2dDescriptor.m_DataLayout = DataLayout::NHWC;
+
+ std::vector<float> weightsVector(512 * 512);
+ ConstTensor weights(weightsInfo, weightsVector);
+
+ ConstantLayer* weightsLayer = graph.AddLayer<ConstantLayer>("Weights");
+ weightsLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(weights);
+ weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsInfo);
+
+ Convolution2dLayer* conv2dLayer = graph.AddLayer<Convolution2dLayer>(convolution2dDescriptor, "conv2d");
+ conv2dLayer->GetOutputSlot().SetTensorInfo(outputInfo);
+
+ Layer* output = graph.AddLayer<OutputLayer>(0, "output");
+
+ // Connect up layers - input -> pad -> conv2d -> output
+ input->GetOutputSlot().Connect(padLayer->GetInputSlot(0));
+ padLayer->GetOutputSlot().Connect(conv2dLayer->GetInputSlot(0));
+ weightsLayer->GetOutputSlot().Connect(conv2dLayer->GetInputSlot(1));
+ conv2dLayer->GetOutputSlot().Connect(output->GetInputSlot(0));
+
+ CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<InputLayer>,
+ &IsLayerOfType<ConstantLayer>,
+ &IsLayerOfType<PadLayer>,
+ &IsLayerOfType<Convolution2dLayer>,
+ &IsLayerOfType<OutputLayer>));
+
+ armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(FoldPadIntoConvolution2d()));
+
+ CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<InputLayer>,
+ &IsLayerOfType<ConstantLayer>,
+ &IsLayerOfType<PadLayer>,
+ &IsLayerOfType<Convolution2dLayer>,
+ &IsLayerOfType<OutputLayer>));
+}
+
TEST_CASE("FoldPadLayerIntoDepthwiseConvolution2dLayer")
{
Graph graph;