IVGCVSW-8157 - Rebase existing GpuFsa patches to 23.11
Squashed commit of the following:
IVGCVSW-7159 Add GpuFsa backend skeleton
IVGCVSW-7380 Update the GpuFsa Skeleton to build and load ACL
IVGCVSW-7381 Add IsLayerSupported implementation to GpuFsa backend
IVGCVSW-7382 Implementation of Conv2d within GpuFsa
Signed-off-by: James Conroy <james.conroy@arm.com>
Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Signed-off-by: David Monahan <david.monahan@arm.com>
Change-Id: Id23d9ee598535de7b38a99ca223cdf0ad2102cef
diff --git a/src/backends/gpuFsa/test/GpuFsaOptimizedNetworkTests.cpp b/src/backends/gpuFsa/test/GpuFsaOptimizedNetworkTests.cpp
new file mode 100644
index 0000000..7e094ce
--- /dev/null
+++ b/src/backends/gpuFsa/test/GpuFsaOptimizedNetworkTests.cpp
@@ -0,0 +1,137 @@
+//
+// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <armnn/INetwork.hpp>
+
+#include <GraphUtils.hpp>
+#include <TestUtils.hpp>
+
+#include <doctest/doctest.h>
+
+using namespace armnn;
+
+TEST_SUITE("GpuFsaOptimizedNetwork")
+{
+
+TEST_CASE("SingleConv2dSupportedOptimizedNetwork")
+{
+ IRuntime::CreationOptions options;
+ IRuntimePtr runtime(IRuntime::Create(options));
+ INetworkPtr network(INetwork::Create());
+
+ TensorInfo inputInfo({ 1, 5, 5, 1 }, DataType::Float32);
+ TensorInfo outputInfo({ 1, 3, 3, 1 }, DataType::Float32);
+ TensorInfo weightsInfo({ 1, 3, 3, 1 }, DataType::Float32, 0.0f, 0, true);
+ TensorInfo biasesInfo({ 1 }, DataType::Float32, 0.0f, 0, true);
+
+ Convolution2dDescriptor desc;
+ desc.m_BiasEnabled = true;
+ desc.m_DataLayout = DataLayout::NHWC;
+
+ auto inputLayer = network->AddInputLayer(0, "input");
+ auto weightLayer = network->AddConstantLayer(ConstTensor(weightsInfo, nullptr), "weights");
+ auto biasLayer = network->AddConstantLayer(ConstTensor(biasesInfo, nullptr), "bias");
+ auto convLayer = network->AddConvolution2dLayer(desc, "conv2d");
+ auto outputLayer = network->AddOutputLayer(1, "output");
+
+ inputLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(0));
+ inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
+
+ weightLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(1));
+ weightLayer->GetOutputSlot(0).SetTensorInfo(weightsInfo);
+
+ biasLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(2));
+ biasLayer->GetOutputSlot(0).SetTensorInfo(biasesInfo);
+
+ convLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
+ convLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
+
+ std::vector<BackendId> backends = { "GpuFsa" };
+
+ OptimizerOptionsOpaque optimizedOptions;
+ IOptimizedNetworkPtr optNet = Optimize(*network, backends, runtime->GetDeviceSpec(), optimizedOptions);
+ CHECK(optNet);
+
+ Graph& graph = GetGraphForTesting(optNet.get());
+
+ // Check graph layer sequence to ensure that the network has been replaced with a PreCompiledLayer
+ CHECK(CheckSequence(graph.cbegin(), graph.cend(),
+ &IsLayerOfType<InputLayer>,
+ &IsLayerOfType<ConstantLayer>,
+ &IsLayerOfType<ConstantLayer>,
+ &IsLayerOfType<PreCompiledLayer>,
+ &IsLayerOfType<OutputLayer>));
+}
+
+TEST_CASE("TwoConv2dSupportedOptimizedNetwork")
+{
+ IRuntime::CreationOptions options;
+ IRuntimePtr runtime(IRuntime::Create(options));
+ INetworkPtr network(INetwork::Create());
+
+ TensorInfo inputInfo({ 1, 5, 5, 1 }, DataType::Float32);
+ TensorInfo intermediateInfo({ 1, 3, 3, 1 }, DataType::Float32);
+ TensorInfo outputInfo({ 1, 1, 1, 1 }, DataType::Float32);
+ TensorInfo weightsInfo({ 1, 3, 3, 1 }, DataType::Float32, 0.0f, 0, true);
+ TensorInfo biasesInfo({ 1 }, DataType::Float32, 0.0f, 0, true);
+
+ Convolution2dDescriptor desc;
+ desc.m_BiasEnabled = true;
+ desc.m_DataLayout = DataLayout::NHWC;
+
+ auto inputLayer = network->AddInputLayer(0, "input");
+
+ auto weightLayer1 = network->AddConstantLayer(ConstTensor(weightsInfo, nullptr), "weights");
+ auto biasLayer1 = network->AddConstantLayer(ConstTensor(biasesInfo, nullptr), "bias");
+ auto convLayer1 = network->AddConvolution2dLayer(desc, "conv2d");
+
+ auto weightLayer2 = network->AddConstantLayer(ConstTensor(weightsInfo, nullptr), "weights");
+ auto biasLayer2 = network->AddConstantLayer(ConstTensor(biasesInfo, nullptr), "bias");
+ auto convLayer2 = network->AddConvolution2dLayer(desc, "conv2d");
+
+ auto outputLayer = network->AddOutputLayer(0, "output");
+
+ inputLayer->GetOutputSlot(0).Connect(convLayer1->GetInputSlot(0));
+ inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
+
+ weightLayer1->GetOutputSlot(0).Connect(convLayer1->GetInputSlot(1));
+ weightLayer1->GetOutputSlot(0).SetTensorInfo(weightsInfo);
+
+ biasLayer1->GetOutputSlot(0).Connect(convLayer1->GetInputSlot(2));
+ biasLayer1->GetOutputSlot(0).SetTensorInfo(biasesInfo);
+
+ convLayer1->GetOutputSlot(0).Connect(convLayer2->GetInputSlot(0));
+ convLayer1->GetOutputSlot(0).SetTensorInfo(intermediateInfo);
+
+ weightLayer2->GetOutputSlot(0).Connect(convLayer2->GetInputSlot(1));
+ weightLayer2->GetOutputSlot(0).SetTensorInfo(weightsInfo);
+
+ biasLayer2->GetOutputSlot(0).Connect(convLayer2->GetInputSlot(2));
+ biasLayer2->GetOutputSlot(0).SetTensorInfo(biasesInfo);
+
+ convLayer2->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
+ convLayer2->GetOutputSlot(0).SetTensorInfo(outputInfo);
+
+ std::vector<BackendId> backends = { "GpuFsa" };
+
+ OptimizerOptionsOpaque optimizedOptions;
+ IOptimizedNetworkPtr optNet = Optimize(*network, backends, runtime->GetDeviceSpec(), optimizedOptions);
+ CHECK(optNet);
+
+ Graph& graph = GetGraphForTesting(optNet.get());
+
+ // Check graph layer sequence to ensure that the network has been replaced with a PreCompiledLayer
+ CHECK(CheckSequence(graph.cbegin(), graph.cend(),
+ &IsLayerOfType<InputLayer>,
+ &IsLayerOfType<ConstantLayer>,
+ &IsLayerOfType<ConstantLayer>,
+ &IsLayerOfType<ConstantLayer>,
+ &IsLayerOfType<ConstantLayer>,
+ &IsLayerOfType<PreCompiledLayer>,
+ &IsLayerOfType<PreCompiledLayer>,
+ &IsLayerOfType<OutputLayer>));
+}
+
+}
\ No newline at end of file