Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2020 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
Sadik Armagan | a097d2a | 2021-11-24 15:47:28 +0000 | [diff] [blame] | 6 | #include <CommonTestUtils.hpp> |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 7 | #include <backendsCommon/test/mockBackend/MockImportBackend.hpp> |
| 8 | |
Sadik Armagan | a097d2a | 2021-11-24 15:47:28 +0000 | [diff] [blame] | 9 | #include <GraphUtils.hpp> |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 10 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 11 | #include <doctest/doctest.h> |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 12 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 13 | TEST_SUITE("NeonFallback") |
| 14 | { |
| 15 | TEST_CASE("FallbackImportToCpuAcc") |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 16 | { |
| 17 | using namespace armnn; |
| 18 | |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 19 | // Create a mock backend objectN |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 20 | MockImportBackendInitialiser initialiser; // Register the Mock Backend |
| 21 | auto backendObjPtr = CreateBackendObject(MockImportBackendId()); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 22 | CHECK((backendObjPtr != nullptr)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 23 | |
| 24 | BackendIdSet backendIds = BackendRegistryInstance().GetBackendIds(); |
| 25 | if (backendIds.find("MockRef") == backendIds.end()) |
| 26 | { |
| 27 | std::string message = "Cannot load MockRef"; |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 28 | FAIL(message); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 29 | } |
| 30 | |
| 31 | // Create runtime in which test will run and allow fallback to CpuRef. |
| 32 | IRuntime::CreationOptions options; |
| 33 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 34 | |
| 35 | // Builds up the structure of the network. |
| 36 | INetworkPtr net(INetwork::Create()); |
| 37 | |
| 38 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 39 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 40 | IConnectableLayer* input2 = net->AddInputLayer(2, "input2"); |
| 41 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 42 | IConnectableLayer* sub = net->AddSubtractionLayer("sub"); |
| 43 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 44 | |
| 45 | input0->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 46 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 47 | input2->GetOutputSlot(0).Connect(sub->GetInputSlot(0)); |
| 48 | add->GetOutputSlot(0).Connect(sub->GetInputSlot(1)); |
| 49 | sub->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 50 | |
| 51 | TensorInfo info = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32); |
| 52 | |
| 53 | input0->GetOutputSlot(0).SetTensorInfo(info); |
| 54 | input1->GetOutputSlot(0).SetTensorInfo(info); |
| 55 | input2->GetOutputSlot(0).SetTensorInfo(info); |
| 56 | add->GetOutputSlot(0).SetTensorInfo(info); |
| 57 | sub->GetOutputSlot(0).SetTensorInfo(info); |
| 58 | |
| 59 | // optimize the network |
| 60 | std::vector<BackendId> backends = { "MockRef", Compute::CpuAcc }; |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 61 | OptimizerOptions optOptions; |
| 62 | optOptions.m_ImportEnabled = true; |
Colm Donelan | 03bf98a | 2022-05-30 15:20:36 +0100 | [diff] [blame^] | 63 | optOptions.m_ExportEnabled = true; |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 64 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec(), optOptions); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 65 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 66 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 67 | |
| 68 | armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); |
| 69 | armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); |
| 70 | armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "input2"); |
| 71 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "add"); |
| 72 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "[ add (0) -> sub (1) ]"); |
| 73 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "sub"); |
| 74 | armnn::Layer* const layer6 = GetFirstLayerWithName(graph, "output"); |
| 75 | |
| 76 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 77 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 78 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 79 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 80 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 81 | CHECK(CheckOrder(graph, layer4, layer5)); |
| 82 | CHECK(CheckOrder(graph, layer5, layer6)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 83 | |
| 84 | // Load it into the runtime. It should pass. |
| 85 | NetworkId netId; |
| 86 | std::string ignoredErrorMessage; |
Francis Murtagh | 73d3e2e | 2021-04-29 14:23:04 +0100 | [diff] [blame] | 87 | INetworkProperties networkProperties(false, MemorySource::Malloc, MemorySource::Malloc); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 88 | runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties); |
| 89 | |
| 90 | // Creates structures for input & output |
| 91 | std::vector<float> inputData0 |
| 92 | { |
| 93 | 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f |
| 94 | }; |
| 95 | std::vector<float> inputData1 |
| 96 | { |
| 97 | 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f |
| 98 | }; |
| 99 | std::vector<float> inputData2 |
| 100 | { |
| 101 | 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f |
| 102 | }; |
| 103 | |
| 104 | std::vector<float> outputData(12); |
| 105 | |
| 106 | std::vector<float> expectedOutput |
| 107 | { |
| 108 | 11.0f, 9.0f, 7.0f, 5.0f, 3.0f, 1.0f, -1.0f, -3.0f, -5.0f, -7.0f, -9.0f, -11.0f |
| 109 | }; |
| 110 | |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 111 | armnn::TensorInfo inputTensorInfo0 = runtime->GetInputTensorInfo(netId, 0); |
| 112 | armnn::TensorInfo inputTensorInfo1 = runtime->GetInputTensorInfo(netId, 1); |
| 113 | armnn::TensorInfo inputTensorInfo2 = runtime->GetInputTensorInfo(netId, 2); |
| 114 | inputTensorInfo0.SetConstant(true); |
| 115 | inputTensorInfo1.SetConstant(true); |
| 116 | inputTensorInfo2.SetConstant(true); |
| 117 | |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 118 | InputTensors inputTensors |
| 119 | { |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 120 | { 0, armnn::ConstTensor(inputTensorInfo0, inputData0.data()) }, |
| 121 | { 1, armnn::ConstTensor(inputTensorInfo1, inputData1.data()) }, |
| 122 | { 2, armnn::ConstTensor(inputTensorInfo2, inputData2.data()) } |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 123 | }; |
| 124 | OutputTensors outputTensors |
| 125 | { |
| 126 | { 0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 127 | }; |
| 128 | |
| 129 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 130 | |
| 131 | // Do the inference |
| 132 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 133 | |
| 134 | // Retrieve the Profiler.Print() output to get the workload execution |
| 135 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 136 | std::stringstream ss; |
| 137 | profilerManager.GetProfiler()->Print(ss);; |
| 138 | std::string dump = ss.str(); |
| 139 | |
| 140 | // Contains ImportMemGeneric |
| 141 | std::size_t found = dump.find("ImportMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 142 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 143 | |
| 144 | // Contains SyncMemGeneric |
| 145 | found = dump.find("SyncMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 146 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 147 | |
| 148 | // Does not contain CopyMemGeneric |
| 149 | found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 150 | CHECK(found == std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 151 | |
| 152 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 153 | CHECK((layer4->GetType() == LayerType::MemImport)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 154 | |
| 155 | // Check output is as expected |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 156 | CHECK(outputData == expectedOutput); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 157 | } |
| 158 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 159 | TEST_CASE("FallbackPaddingCopyToCpuAcc") |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 160 | { |
| 161 | using namespace armnn; |
| 162 | |
| 163 | // Create a mock backend object |
| 164 | MockImportBackendInitialiser initialiser; // Register the Mock Backend |
| 165 | auto backendObjPtr = CreateBackendObject(MockImportBackendId()); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 166 | CHECK((backendObjPtr != nullptr)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 167 | |
| 168 | BackendIdSet backendIds = BackendRegistryInstance().GetBackendIds(); |
| 169 | if (backendIds.find("MockRef") == backendIds.end()) |
| 170 | { |
| 171 | std::string message = "Cannot load MockRef"; |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 172 | FAIL(message); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 173 | } |
| 174 | |
| 175 | // Create runtime in which test will run and allow fallback to CpuRef. |
| 176 | IRuntime::CreationOptions options; |
| 177 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 178 | |
| 179 | // Builds up the structure of the network. |
| 180 | INetworkPtr net(INetwork::Create()); |
| 181 | |
| 182 | Pooling2dDescriptor desc; |
| 183 | |
| 184 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 185 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 186 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 187 | IConnectableLayer* pooling = net->AddPooling2dLayer(desc, "pooling"); |
| 188 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 189 | |
| 190 | input0->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 191 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 192 | add->GetOutputSlot(0).Connect(pooling->GetInputSlot(0)); |
| 193 | pooling->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 194 | |
| 195 | TensorInfo info = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32); |
| 196 | TensorInfo poolingInfo = TensorInfo({ 1, 2, 1, 1 }, DataType::Float32); |
| 197 | |
| 198 | input0->GetOutputSlot(0).SetTensorInfo(info); |
| 199 | input1->GetOutputSlot(0).SetTensorInfo(info); |
| 200 | add->GetOutputSlot(0).SetTensorInfo(info); |
| 201 | pooling->GetOutputSlot(0).SetTensorInfo(poolingInfo); |
| 202 | |
| 203 | // optimize the network |
| 204 | std::vector<BackendId> backends = { "MockRef", Compute::CpuAcc }; |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 205 | OptimizerOptions optOptions; |
| 206 | optOptions.m_ImportEnabled = true; |
Colm Donelan | 03bf98a | 2022-05-30 15:20:36 +0100 | [diff] [blame^] | 207 | optOptions.m_ExportEnabled = true; |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 208 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec(), optOptions); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 209 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 210 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 211 | |
| 212 | armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); |
| 213 | armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); |
| 214 | armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "add"); |
| 215 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "[ add (0) -> pooling (0) ]"); |
| 216 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "pooling"); |
| 217 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "output"); |
| 218 | |
| 219 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 220 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 221 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 222 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 223 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 224 | CHECK(CheckOrder(graph, layer4, layer5)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 225 | |
| 226 | // Load it into the runtime. It should pass. |
| 227 | NetworkId netId; |
| 228 | std::string ignoredErrorMessage; |
Francis Murtagh | 73d3e2e | 2021-04-29 14:23:04 +0100 | [diff] [blame] | 229 | INetworkProperties networkProperties(false, MemorySource::Malloc, MemorySource::Malloc); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 230 | |
| 231 | runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties); |
| 232 | |
| 233 | // Creates structures for input & output |
| 234 | std::vector<float> inputData0 |
| 235 | { |
| 236 | 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f |
| 237 | }; |
| 238 | std::vector<float> inputData1 |
| 239 | { |
| 240 | 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f |
| 241 | }; |
| 242 | |
| 243 | std::vector<float> outputData(2); |
| 244 | |
| 245 | std::vector<float> expectedOutput |
| 246 | { |
| 247 | 6.0f, 12.0f |
| 248 | }; |
| 249 | |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 250 | armnn::TensorInfo inputTensorInfo0 = runtime->GetInputTensorInfo(netId, 0); |
| 251 | armnn::TensorInfo inputTensorInfo1 = runtime->GetInputTensorInfo(netId, 1); |
| 252 | inputTensorInfo0.SetConstant(true); |
| 253 | inputTensorInfo1.SetConstant(true); |
| 254 | |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 255 | InputTensors inputTensors |
| 256 | { |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 257 | { 0, armnn::ConstTensor(inputTensorInfo0, inputData0.data()) }, |
| 258 | { 1, armnn::ConstTensor(inputTensorInfo1, inputData1.data()) } |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 259 | }; |
| 260 | OutputTensors outputTensors |
| 261 | { |
| 262 | { 0, armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 263 | }; |
| 264 | |
| 265 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 266 | |
| 267 | // Do the inference |
| 268 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 269 | |
| 270 | // Retrieve the Profiler.Print() output to get the workload execution |
| 271 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 272 | std::stringstream ss; |
| 273 | profilerManager.GetProfiler()->Print(ss);; |
| 274 | std::string dump = ss.str(); |
| 275 | |
| 276 | // Contains CopyMemGeneric between the backends |
| 277 | std::size_t found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 278 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 279 | |
| 280 | // Contains SyncMemGeneric for the output |
| 281 | found = dump.find("SyncMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 282 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 283 | |
| 284 | // Does not contain ImportMemGeneric |
| 285 | found = dump.find("ImportMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 286 | CHECK(found == std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 287 | |
| 288 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 289 | CHECK((layer3->GetType() == LayerType::MemCopy)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 290 | |
| 291 | // Check output is as expected |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 292 | CHECK(outputData == expectedOutput); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 293 | } |
| 294 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 295 | TEST_CASE("FallbackImportFromCpuAcc") |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 296 | { |
| 297 | using namespace armnn; |
| 298 | |
| 299 | // Create a mock backend object |
| 300 | MockImportBackendInitialiser initialiser; // Register the Mock Backend |
| 301 | auto backendObjPtr = CreateBackendObject(MockImportBackendId()); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 302 | CHECK((backendObjPtr != nullptr)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 303 | |
| 304 | BackendIdSet backendIds = BackendRegistryInstance().GetBackendIds(); |
| 305 | if (backendIds.find("MockRef") == backendIds.end()) |
| 306 | { |
| 307 | std::string message = "Cannot load MockRef"; |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 308 | FAIL(message); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 309 | } |
| 310 | |
| 311 | // Create runtime in which test will run and allow fallback to CpuRef. |
| 312 | IRuntime::CreationOptions options; |
| 313 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 314 | |
| 315 | // Builds up the structure of the network. |
| 316 | INetworkPtr net(INetwork::Create()); |
| 317 | |
| 318 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 319 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 320 | IConnectableLayer* input2 = net->AddInputLayer(2, "input2"); |
| 321 | IConnectableLayer* sub = net->AddSubtractionLayer("sub"); |
| 322 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 323 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 324 | |
| 325 | input0->GetOutputSlot(0).Connect(sub->GetInputSlot(0)); |
| 326 | input1->GetOutputSlot(0).Connect(sub->GetInputSlot(1)); |
| 327 | input2->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 328 | sub->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 329 | add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 330 | |
| 331 | TensorInfo info = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32); |
| 332 | |
| 333 | input0->GetOutputSlot(0).SetTensorInfo(info); |
| 334 | input1->GetOutputSlot(0).SetTensorInfo(info); |
| 335 | input2->GetOutputSlot(0).SetTensorInfo(info); |
| 336 | sub->GetOutputSlot(0).SetTensorInfo(info); |
| 337 | add->GetOutputSlot(0).SetTensorInfo(info); |
| 338 | |
| 339 | // optimize the network |
| 340 | std::vector<BackendId> backends = { "MockRef", Compute::CpuAcc }; |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 341 | OptimizerOptions optOptions; |
| 342 | optOptions.m_ImportEnabled = true; |
Colm Donelan | 03bf98a | 2022-05-30 15:20:36 +0100 | [diff] [blame^] | 343 | optOptions.m_ExportEnabled = true; |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 344 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec(), optOptions); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 345 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 346 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 347 | |
| 348 | armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); |
| 349 | armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); |
| 350 | armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "input2"); |
| 351 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "sub"); |
| 352 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "[ sub (0) -> add (1) ]"); |
| 353 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "add"); |
| 354 | armnn::Layer* const layer6 = GetFirstLayerWithName(graph, "output"); |
| 355 | |
| 356 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 357 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 358 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 359 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 360 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 361 | CHECK(CheckOrder(graph, layer4, layer5)); |
| 362 | CHECK(CheckOrder(graph, layer5, layer6)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 363 | |
| 364 | // Load it into the runtime. It should pass. |
| 365 | NetworkId netId; |
| 366 | std::string ignoredErrorMessage; |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 367 | |
Francis Murtagh | 73d3e2e | 2021-04-29 14:23:04 +0100 | [diff] [blame] | 368 | INetworkProperties networkProperties(false, MemorySource::Malloc, MemorySource::Malloc); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 369 | runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties); |
| 370 | |
| 371 | // Creates structures for input & output |
| 372 | std::vector<float> inputData0 |
| 373 | { |
| 374 | 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 0.0f |
| 375 | }; |
| 376 | std::vector<float> inputData1 |
| 377 | { |
| 378 | 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f |
| 379 | }; |
| 380 | std::vector<float> inputData2 |
| 381 | { |
| 382 | 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f |
| 383 | }; |
| 384 | |
| 385 | std::vector<float> outputData(12); |
| 386 | |
| 387 | std::vector<float> expectedOutput |
| 388 | { |
| 389 | 13.0f, 11.0f, 11.0f, 9.0f, 7.0f, 7.0f, 7.0f, 5.0f, 5.0f, 3.0f, 3.0f, -5.0f |
| 390 | }; |
| 391 | |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 392 | armnn::TensorInfo inputTensorInfo0 = runtime->GetInputTensorInfo(netId, 0); |
| 393 | armnn::TensorInfo inputTensorInfo1 = runtime->GetInputTensorInfo(netId, 1); |
| 394 | armnn::TensorInfo inputTensorInfo2 = runtime->GetInputTensorInfo(netId, 2); |
| 395 | inputTensorInfo0.SetConstant(true); |
| 396 | inputTensorInfo1.SetConstant(true); |
| 397 | inputTensorInfo2.SetConstant(true); |
| 398 | |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 399 | InputTensors inputTensors |
| 400 | { |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 401 | { 0, armnn::ConstTensor(inputTensorInfo0, inputData0.data()) }, |
| 402 | { 1, armnn::ConstTensor(inputTensorInfo1, inputData1.data()) }, |
| 403 | { 2, armnn::ConstTensor(inputTensorInfo2, inputData2.data()) } |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 404 | }; |
| 405 | OutputTensors outputTensors |
| 406 | { |
| 407 | { 0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 408 | }; |
| 409 | |
| 410 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 411 | |
| 412 | // Do the inference |
| 413 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 414 | |
| 415 | // Retrieve the Profiler.Print() output to get the workload execution |
| 416 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 417 | std::stringstream ss; |
| 418 | profilerManager.GetProfiler()->Print(ss);; |
| 419 | std::string dump = ss.str(); |
| 420 | |
| 421 | // Contains ImportMemGeneric |
| 422 | std::size_t found = dump.find("ImportMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 423 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 424 | |
| 425 | // Contains SyncMemGeneric |
| 426 | found = dump.find("SyncMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 427 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 428 | |
| 429 | // Does not contain CopyMemGeneric |
| 430 | found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 431 | CHECK(found == std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 432 | |
| 433 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 434 | CHECK((layer4->GetType() == LayerType::MemImport)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 435 | |
| 436 | // Check output is as expected |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 437 | CHECK(outputData == expectedOutput); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 438 | } |
| 439 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 440 | TEST_CASE("FallbackPaddingCopyFromCpuAcc") |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 441 | { |
| 442 | using namespace armnn; |
| 443 | |
| 444 | // Create a mock backend object |
| 445 | MockImportBackendInitialiser initialiser; // Register the Mock Backend |
| 446 | auto backendObjPtr = CreateBackendObject(MockImportBackendId()); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 447 | CHECK((backendObjPtr != nullptr)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 448 | |
| 449 | BackendIdSet backendIds = BackendRegistryInstance().GetBackendIds(); |
| 450 | if (backendIds.find("MockRef") == backendIds.end()) |
| 451 | { |
| 452 | std::string message = "Cannot load MockRef"; |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 453 | FAIL(message); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 454 | } |
| 455 | |
| 456 | // Create runtime in which test will run and allow fallback to CpuRef. |
| 457 | IRuntime::CreationOptions options; |
| 458 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 459 | |
| 460 | // Builds up the structure of the network. |
| 461 | INetworkPtr net(INetwork::Create()); |
| 462 | |
| 463 | Pooling2dDescriptor desc; |
| 464 | |
| 465 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 466 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 467 | IConnectableLayer* pooling = net->AddPooling2dLayer(desc, "pooling"); |
| 468 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 469 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 470 | |
| 471 | input0->GetOutputSlot(0).Connect(pooling->GetInputSlot(0)); |
| 472 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 473 | pooling->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 474 | add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 475 | |
| 476 | TensorInfo inputInfo = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32); |
| 477 | TensorInfo poolingInfo = TensorInfo({ 1, 2, 1, 1 }, DataType::Float32); |
| 478 | |
| 479 | input0->GetOutputSlot(0).SetTensorInfo(inputInfo); |
| 480 | input1->GetOutputSlot(0).SetTensorInfo(poolingInfo); |
| 481 | pooling->GetOutputSlot(0).SetTensorInfo(poolingInfo); |
| 482 | add->GetOutputSlot(0).SetTensorInfo(poolingInfo); |
| 483 | |
| 484 | // optimize the network |
| 485 | std::vector<BackendId> backends = { "MockRef", Compute::CpuAcc }; |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 486 | OptimizerOptions optOptions; |
| 487 | optOptions.m_ImportEnabled = true; |
Colm Donelan | 03bf98a | 2022-05-30 15:20:36 +0100 | [diff] [blame^] | 488 | optOptions.m_ExportEnabled = true; |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 489 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec(), optOptions); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 490 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 491 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 492 | |
| 493 | armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); |
| 494 | armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); |
| 495 | armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "pooling"); |
| 496 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "[ pooling (0) -> add (0) ]"); |
| 497 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "add"); |
| 498 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "output"); |
| 499 | |
| 500 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 501 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 502 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 503 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 504 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 505 | CHECK(CheckOrder(graph, layer4, layer5)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 506 | |
| 507 | // Load it into the runtime. It should pass. |
| 508 | NetworkId netId; |
| 509 | std::string ignoredErrorMessage; |
Francis Murtagh | 73d3e2e | 2021-04-29 14:23:04 +0100 | [diff] [blame] | 510 | INetworkProperties networkProperties(false, MemorySource::Malloc, MemorySource::Malloc); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 511 | |
| 512 | runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties); |
| 513 | |
| 514 | // Creates structures for input & output |
| 515 | std::vector<float> inputData0 |
| 516 | { |
| 517 | 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f |
| 518 | }; |
| 519 | std::vector<float> inputData1 |
| 520 | { |
| 521 | -1.0f, 3.0f |
| 522 | }; |
| 523 | |
| 524 | std::vector<float> outputData(2); |
| 525 | |
| 526 | std::vector<float> expectedOutput |
| 527 | { |
| 528 | 5.0f, 15.0f |
| 529 | }; |
| 530 | |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 531 | armnn::TensorInfo inputTensorInfo0 = runtime->GetInputTensorInfo(netId, 0); |
| 532 | armnn::TensorInfo inputTensorInfo1 = runtime->GetInputTensorInfo(netId, 1); |
| 533 | inputTensorInfo0.SetConstant(true); |
| 534 | inputTensorInfo1.SetConstant(true); |
| 535 | |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 536 | InputTensors inputTensors |
| 537 | { |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 538 | { 0, armnn::ConstTensor(inputTensorInfo0, inputData0.data()) }, |
| 539 | { 1, armnn::ConstTensor(inputTensorInfo1, inputData1.data()) } |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 540 | }; |
| 541 | OutputTensors outputTensors |
| 542 | { |
| 543 | { 0, armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 544 | }; |
| 545 | |
| 546 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 547 | |
| 548 | // Do the inference |
| 549 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 550 | |
| 551 | // Retrieve the Profiler.Print() output to get the workload execution |
| 552 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 553 | std::stringstream ss; |
| 554 | profilerManager.GetProfiler()->Print(ss);; |
| 555 | std::string dump = ss.str(); |
| 556 | |
| 557 | // Contains CopyMemGeneric between the backends |
| 558 | std::size_t found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 559 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 560 | |
| 561 | // Contains SyncMemGeneric for the output |
| 562 | found = dump.find("SyncMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 563 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 564 | |
| 565 | // Does not contain ImportMemGeneric |
| 566 | found = dump.find("ImportMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 567 | CHECK(found == std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 568 | |
| 569 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 570 | CHECK((layer3->GetType() == LayerType::MemCopy)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 571 | |
| 572 | // Check output is as expected |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 573 | CHECK(outputData == expectedOutput); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 574 | } |
| 575 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 576 | TEST_CASE("FallbackDisableImportFromCpuAcc") |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 577 | { |
| 578 | using namespace armnn; |
| 579 | |
| 580 | // Create a mock backend object |
| 581 | MockImportBackendInitialiser initialiser; // Register the Mock Backend |
| 582 | auto backendObjPtr = CreateBackendObject(MockImportBackendId()); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 583 | CHECK((backendObjPtr != nullptr)); |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 584 | |
| 585 | BackendIdSet backendIds = BackendRegistryInstance().GetBackendIds(); |
| 586 | if (backendIds.find("MockRef") == backendIds.end()) |
| 587 | { |
| 588 | std::string message = "Cannot load MockRef"; |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 589 | FAIL(message); |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 590 | } |
| 591 | |
| 592 | // Create runtime in which test will run and allow fallback to CpuRef. |
| 593 | IRuntime::CreationOptions options; |
| 594 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 595 | |
| 596 | // Builds up the structure of the network. |
| 597 | INetworkPtr net(INetwork::Create()); |
| 598 | |
| 599 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 600 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 601 | IConnectableLayer* input2 = net->AddInputLayer(2, "input2"); |
| 602 | IConnectableLayer* sub = net->AddSubtractionLayer("sub"); |
| 603 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 604 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 605 | |
| 606 | input0->GetOutputSlot(0).Connect(sub->GetInputSlot(0)); |
| 607 | input1->GetOutputSlot(0).Connect(sub->GetInputSlot(1)); |
| 608 | input2->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 609 | sub->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 610 | add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 611 | |
| 612 | TensorInfo info = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32); |
| 613 | |
| 614 | input0->GetOutputSlot(0).SetTensorInfo(info); |
| 615 | input1->GetOutputSlot(0).SetTensorInfo(info); |
| 616 | input2->GetOutputSlot(0).SetTensorInfo(info); |
| 617 | sub->GetOutputSlot(0).SetTensorInfo(info); |
| 618 | add->GetOutputSlot(0).SetTensorInfo(info); |
| 619 | |
| 620 | // optimize the network |
| 621 | std::vector<BackendId> backends = { "MockRef", Compute::CpuAcc }; |
| 622 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec()); |
| 623 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 624 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 625 | |
| 626 | armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); |
| 627 | armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); |
| 628 | armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "input2"); |
| 629 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "sub"); |
| 630 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "[ sub (0) -> add (1) ]"); |
| 631 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "add"); |
| 632 | armnn::Layer* const layer6 = GetFirstLayerWithName(graph, "output"); |
| 633 | |
| 634 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 635 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 636 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 637 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 638 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 639 | CHECK(CheckOrder(graph, layer4, layer5)); |
| 640 | CHECK(CheckOrder(graph, layer5, layer6)); |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 641 | |
| 642 | // Load it into the runtime. It should pass. |
| 643 | NetworkId netId; |
| 644 | std::string ignoredErrorMessage; |
Francis Murtagh | 73d3e2e | 2021-04-29 14:23:04 +0100 | [diff] [blame] | 645 | INetworkProperties networkProperties(false, MemorySource::Undefined, MemorySource::Undefined); |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 646 | |
| 647 | runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties); |
| 648 | |
| 649 | // Creates structures for input & output |
| 650 | std::vector<float> inputData0 |
| 651 | { |
| 652 | 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 0.0f |
| 653 | }; |
| 654 | std::vector<float> inputData1 |
| 655 | { |
| 656 | 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f |
| 657 | }; |
| 658 | std::vector<float> inputData2 |
| 659 | { |
| 660 | 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f |
| 661 | }; |
| 662 | |
| 663 | std::vector<float> outputData(12); |
| 664 | |
| 665 | std::vector<float> expectedOutput |
| 666 | { |
| 667 | 13.0f, 11.0f, 11.0f, 9.0f, 7.0f, 7.0f, 7.0f, 5.0f, 5.0f, 3.0f, 3.0f, -5.0f |
| 668 | }; |
| 669 | |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 670 | armnn::TensorInfo inputTensorInfo0 = runtime->GetInputTensorInfo(netId, 0); |
| 671 | armnn::TensorInfo inputTensorInfo1 = runtime->GetInputTensorInfo(netId, 1); |
| 672 | armnn::TensorInfo inputTensorInfo2 = runtime->GetInputTensorInfo(netId, 2); |
| 673 | inputTensorInfo0.SetConstant(true); |
| 674 | inputTensorInfo1.SetConstant(true); |
| 675 | inputTensorInfo2.SetConstant(true); |
| 676 | |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 677 | InputTensors inputTensors |
| 678 | { |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 679 | { 0, armnn::ConstTensor(inputTensorInfo0, inputData0.data()) }, |
| 680 | { 1, armnn::ConstTensor(inputTensorInfo1, inputData1.data()) }, |
| 681 | { 2, armnn::ConstTensor(inputTensorInfo2, inputData2.data()) } |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 682 | }; |
| 683 | OutputTensors outputTensors |
| 684 | { |
| 685 | { 0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 686 | }; |
| 687 | |
| 688 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 689 | |
| 690 | // Do the inference |
| 691 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 692 | |
| 693 | // Retrieve the Profiler.Print() output to get the workload execution |
| 694 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 695 | std::stringstream ss; |
| 696 | profilerManager.GetProfiler()->Print(ss);; |
| 697 | std::string dump = ss.str(); |
| 698 | |
| 699 | // Contains CopyMemGeneric between the backends |
| 700 | std::size_t found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 701 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 702 | |
| 703 | // Does not contain ImportMemGeneric |
| 704 | found = dump.find("ImportMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 705 | CHECK(found == std::string::npos); |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 706 | |
| 707 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 708 | CHECK((layer4->GetType() == LayerType::MemCopy)); |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 709 | |
| 710 | // Check output is as expected |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 711 | CHECK(outputData == expectedOutput); |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 712 | } |
| 713 | |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 714 | #if defined(ARMCOMPUTECL_ENABLED) |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 715 | TEST_CASE("NeonImportEnabledFallbackToCl") |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 716 | { |
| 717 | using namespace armnn; |
| 718 | |
| 719 | IRuntime::CreationOptions options; |
| 720 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 721 | |
| 722 | // Builds up the structure of the network. |
| 723 | INetworkPtr net(INetwork::Create()); |
| 724 | |
| 725 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 726 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 727 | IConnectableLayer* input2 = net->AddInputLayer(2, "input2"); |
| 728 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 729 | IConnectableLayer* sub = net->AddSubtractionLayer("sub"); |
| 730 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 731 | |
| 732 | input0->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 733 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 734 | input2->GetOutputSlot(0).Connect(sub->GetInputSlot(0)); |
| 735 | add->GetOutputSlot(0).Connect(sub->GetInputSlot(1)); |
| 736 | sub->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 737 | |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 738 | TensorInfo info = TensorInfo({ 1, 2, 4, 2 }, DataType::Float32); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 739 | |
| 740 | input0->GetOutputSlot(0).SetTensorInfo(info); |
| 741 | input1->GetOutputSlot(0).SetTensorInfo(info); |
| 742 | input2->GetOutputSlot(0).SetTensorInfo(info); |
| 743 | add->GetOutputSlot(0).SetTensorInfo(info); |
| 744 | sub->GetOutputSlot(0).SetTensorInfo(info); |
| 745 | |
| 746 | std::vector<BackendId> backends = { Compute::CpuAcc, Compute::GpuAcc }; |
| 747 | // Use BackendSelectionHint to specify GpuAcc for Subtraction layer |
| 748 | sub->BackendSelectionHint(backends[1]); |
| 749 | |
| 750 | // optimize the network |
| 751 | OptimizerOptions optOptions; |
| 752 | optOptions.m_ImportEnabled = true; |
Colm Donelan | 03bf98a | 2022-05-30 15:20:36 +0100 | [diff] [blame^] | 753 | optOptions.m_ExportEnabled = true; |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 754 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec(), optOptions); |
| 755 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 756 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 757 | |
| 758 | armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); |
| 759 | armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); |
| 760 | armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "input2"); |
| 761 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "add"); |
| 762 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "[ add (0) -> sub (1) ]"); |
| 763 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "sub"); |
| 764 | armnn::Layer* const layer6 = GetFirstLayerWithName(graph, "output"); |
| 765 | |
| 766 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 767 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 768 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 769 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 770 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 771 | CHECK(CheckOrder(graph, layer4, layer5)); |
| 772 | CHECK(CheckOrder(graph, layer5, layer6)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 773 | |
| 774 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 775 | CHECK((layer4->GetType() == LayerType::MemCopy)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 776 | |
| 777 | // Correctly use backend hint |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 778 | CHECK((layer5->GetBackendId() == Compute::GpuAcc )); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 779 | |
| 780 | // Load it into the runtime. It should pass. |
| 781 | NetworkId netId; |
| 782 | std::string ignoredErrorMessage; |
Francis Murtagh | 73d3e2e | 2021-04-29 14:23:04 +0100 | [diff] [blame] | 783 | |
| 784 | INetworkProperties networkProperties(false, MemorySource::Malloc, MemorySource::Malloc); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 785 | |
| 786 | runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties); |
| 787 | |
| 788 | // Creates structures for input & output |
| 789 | std::vector<float> inputData0 |
| 790 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 791 | 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f, 1.0f, 1.0f, 2.0f, 2.0f |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 792 | }; |
| 793 | std::vector<float> inputData1 |
| 794 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 795 | 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 0.0f, 1.0f, 1.0f, 2.0f |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 796 | }; |
| 797 | std::vector<float> inputData2 |
| 798 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 799 | 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f, 12.0f, 11.0f, 10.0f, 9.0f |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 800 | }; |
| 801 | |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 802 | std::vector<float> outputData(16); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 803 | |
| 804 | std::vector<float> expectedOutput |
| 805 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 806 | 11.0f, 9.0f, 7.0f, 5.0f, 3.0f, 1.0f, -1.0f, -3.0f, -5.0f, -7.0f, -9.0f, -11.0f, 11.0f, 9.0f, 7.0f, 5.0f |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 807 | }; |
| 808 | |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 809 | // Creates structures for input & output |
| 810 | unsigned int numElements = info.GetNumElements(); |
| 811 | size_t totalBytes = numElements * sizeof(float); |
| 812 | |
| 813 | // Prepare aligned data |
| 814 | const size_t alignment = 64; |
| 815 | size_t space = totalBytes + alignment + alignment; |
| 816 | auto inputData = std::make_unique<uint8_t[]>(space); |
| 817 | void* alignedInputPtr = inputData.get(); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 818 | CHECK(std::align(alignment, totalBytes, alignedInputPtr, space)); |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 819 | |
| 820 | auto* intputPtr = reinterpret_cast<float*>(alignedInputPtr); |
| 821 | std::copy(inputData2.begin(), inputData2.end(), intputPtr); |
| 822 | |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 823 | armnn::TensorInfo inputTensorInfo0 = runtime->GetInputTensorInfo(netId, 0); |
| 824 | armnn::TensorInfo inputTensorInfo1 = runtime->GetInputTensorInfo(netId, 1); |
| 825 | armnn::TensorInfo inputTensorInfo2 = runtime->GetInputTensorInfo(netId, 2); |
| 826 | inputTensorInfo0.SetConstant(true); |
| 827 | inputTensorInfo1.SetConstant(true); |
| 828 | inputTensorInfo2.SetConstant(true); |
| 829 | |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 830 | InputTensors inputTensors |
| 831 | { |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 832 | { 0, armnn::ConstTensor(inputTensorInfo0, inputData0.data()) }, |
| 833 | { 1, armnn::ConstTensor(inputTensorInfo1, inputData1.data()) }, |
| 834 | { 2, armnn::ConstTensor(inputTensorInfo2, alignedInputPtr) } |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 835 | }; |
| 836 | OutputTensors outputTensors |
| 837 | { |
| 838 | { 0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 839 | }; |
| 840 | |
| 841 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 842 | |
| 843 | // Do the inference |
| 844 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 845 | |
| 846 | // Retrieve the Profiler.Print() output to get the workload execution |
| 847 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 848 | std::stringstream ss; |
| 849 | profilerManager.GetProfiler()->Print(ss);; |
| 850 | std::string dump = ss.str(); |
| 851 | |
| 852 | // Executed Subtraction using GpuAcc |
| 853 | std::size_t found = dump.find("ClSubtractionWorkload_Execute"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 854 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 855 | |
| 856 | // Contain CopyMemGeneric |
| 857 | found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 858 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 859 | |
| 860 | // Check output is as expected |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 861 | for(unsigned int i = 0; i < numElements; ++i) |
| 862 | { |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 863 | CHECK(outputData[i] == expectedOutput[i]); |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 864 | } |
| 865 | runtime->UnloadNetwork(netId); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 866 | } |
| 867 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 868 | TEST_CASE("NeonImportDisabledFallbackToCl") |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 869 | { |
| 870 | using namespace armnn; |
| 871 | |
| 872 | IRuntime::CreationOptions options; |
| 873 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 874 | |
| 875 | // Builds up the structure of the network. |
| 876 | INetworkPtr net(INetwork::Create()); |
| 877 | |
| 878 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 879 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 880 | IConnectableLayer* input2 = net->AddInputLayer(2, "input2"); |
| 881 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 882 | IConnectableLayer* sub = net->AddSubtractionLayer("sub"); |
| 883 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 884 | |
| 885 | input0->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 886 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 887 | input2->GetOutputSlot(0).Connect(sub->GetInputSlot(0)); |
| 888 | add->GetOutputSlot(0).Connect(sub->GetInputSlot(1)); |
| 889 | sub->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 890 | |
| 891 | TensorInfo info = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32); |
| 892 | |
| 893 | input0->GetOutputSlot(0).SetTensorInfo(info); |
| 894 | input1->GetOutputSlot(0).SetTensorInfo(info); |
| 895 | input2->GetOutputSlot(0).SetTensorInfo(info); |
| 896 | add->GetOutputSlot(0).SetTensorInfo(info); |
| 897 | sub->GetOutputSlot(0).SetTensorInfo(info); |
| 898 | |
| 899 | std::vector<BackendId> backends = { Compute::CpuAcc, Compute::GpuAcc }; |
| 900 | // Use BackendSelectionHint to specify GpuAcc for Subtraction layer |
| 901 | sub->BackendSelectionHint(backends[1]); |
| 902 | |
| 903 | // optimize the network |
| 904 | OptimizerOptions optOptions; |
| 905 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec(), optOptions); |
| 906 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 907 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 908 | |
| 909 | armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); |
| 910 | armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); |
| 911 | armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "input2"); |
| 912 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "add"); |
| 913 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "[ add (0) -> sub (1) ]"); |
| 914 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "sub"); |
| 915 | armnn::Layer* const layer6 = GetFirstLayerWithName(graph, "output"); |
| 916 | |
| 917 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 918 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 919 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 920 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 921 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 922 | CHECK(CheckOrder(graph, layer4, layer5)); |
| 923 | CHECK(CheckOrder(graph, layer5, layer6)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 924 | |
| 925 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 926 | CHECK((layer4->GetType() == LayerType::MemCopy)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 927 | |
| 928 | // Correctly use backend hint |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 929 | CHECK((layer5->GetBackendId() == Compute::GpuAcc )); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 930 | |
| 931 | // Load it into the runtime. It should pass. |
| 932 | NetworkId netId; |
| 933 | runtime->LoadNetwork(netId, std::move(optNet)); |
| 934 | |
| 935 | // Creates structures for input & output |
| 936 | std::vector<float> inputData0 |
| 937 | { |
| 938 | 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f |
| 939 | }; |
| 940 | std::vector<float> inputData1 |
| 941 | { |
| 942 | 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f |
| 943 | }; |
| 944 | std::vector<float> inputData2 |
| 945 | { |
| 946 | 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f |
| 947 | }; |
| 948 | |
| 949 | std::vector<float> outputData(12); |
| 950 | |
| 951 | std::vector<float> expectedOutput |
| 952 | { |
| 953 | 11.0f, 9.0f, 7.0f, 5.0f, 3.0f, 1.0f, -1.0f, -3.0f, -5.0f, -7.0f, -9.0f, -11.0f |
| 954 | }; |
| 955 | |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 956 | armnn::TensorInfo inputTensorInfo0 = runtime->GetInputTensorInfo(netId, 0); |
| 957 | armnn::TensorInfo inputTensorInfo1 = runtime->GetInputTensorInfo(netId, 1); |
| 958 | armnn::TensorInfo inputTensorInfo2 = runtime->GetInputTensorInfo(netId, 2); |
| 959 | inputTensorInfo0.SetConstant(true); |
| 960 | inputTensorInfo1.SetConstant(true); |
| 961 | inputTensorInfo2.SetConstant(true); |
| 962 | |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 963 | InputTensors inputTensors |
| 964 | { |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 965 | { 0, armnn::ConstTensor(inputTensorInfo0, inputData0.data()) }, |
| 966 | { 1, armnn::ConstTensor(inputTensorInfo1, inputData1.data()) }, |
| 967 | { 2, armnn::ConstTensor(inputTensorInfo2, inputData2.data()) } |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 968 | }; |
| 969 | OutputTensors outputTensors |
| 970 | { |
| 971 | { 0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 972 | }; |
| 973 | |
| 974 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 975 | |
| 976 | // Do the inference |
| 977 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 978 | |
| 979 | // Retrieve the Profiler.Print() output to get the workload execution |
| 980 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 981 | std::stringstream ss; |
| 982 | profilerManager.GetProfiler()->Print(ss);; |
| 983 | std::string dump = ss.str(); |
| 984 | |
| 985 | // Executed Subtraction using GpuAcc |
| 986 | std::size_t found = dump.find("ClSubtractionWorkload_Execute"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 987 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 988 | |
| 989 | // Contain CopyMemGeneric |
| 990 | found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 991 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 992 | |
| 993 | // Check output is as expected |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 994 | CHECK(outputData == expectedOutput); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 995 | } |
| 996 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 997 | TEST_CASE("NeonImportEnabledFallbackSubgraphToCl") |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 998 | { |
| 999 | using namespace armnn; |
| 1000 | |
| 1001 | IRuntime::CreationOptions options; |
| 1002 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 1003 | |
| 1004 | // Builds up the structure of the network. |
| 1005 | INetworkPtr net(INetwork::Create()); |
| 1006 | |
| 1007 | Pooling2dDescriptor desc; |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 1008 | desc.m_PoolWidth = 2; |
| 1009 | desc.m_PoolHeight = 2; |
| 1010 | desc.m_StrideX = 2; |
| 1011 | desc.m_StrideY = 2; |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1012 | |
| 1013 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 1014 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 1015 | IConnectableLayer* input2 = net->AddInputLayer(2, "input2"); |
| 1016 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 1017 | IConnectableLayer* sub = net->AddSubtractionLayer("sub"); |
| 1018 | IConnectableLayer* pooling = net->AddPooling2dLayer(desc, "pooling"); |
| 1019 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 1020 | |
| 1021 | input0->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 1022 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 1023 | input2->GetOutputSlot(0).Connect(sub->GetInputSlot(0)); |
| 1024 | add->GetOutputSlot(0).Connect(sub->GetInputSlot(1)); |
| 1025 | sub->GetOutputSlot(0).Connect(pooling->GetInputSlot(0)); |
| 1026 | pooling->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 1027 | |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 1028 | TensorInfo info = TensorInfo({ 1, 2, 4, 2 }, DataType::Float32); |
| 1029 | TensorInfo poolingInfo = TensorInfo({ 1, 2, 2, 1 }, DataType::Float32); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1030 | |
| 1031 | input0->GetOutputSlot(0).SetTensorInfo(info); |
| 1032 | input1->GetOutputSlot(0).SetTensorInfo(info); |
| 1033 | input2->GetOutputSlot(0).SetTensorInfo(info); |
| 1034 | add->GetOutputSlot(0).SetTensorInfo(info); |
| 1035 | sub->GetOutputSlot(0).SetTensorInfo(info); |
| 1036 | pooling->GetOutputSlot(0).SetTensorInfo(poolingInfo); |
| 1037 | |
| 1038 | std::vector<BackendId> backends = { Compute::CpuAcc, Compute::GpuAcc }; |
| 1039 | // Use BackendSelectionHint to specify GpuAcc for Subtraction layer |
| 1040 | sub->BackendSelectionHint(backends[1]); |
| 1041 | |
| 1042 | // optimize the network |
| 1043 | OptimizerOptions optOptions; |
| 1044 | optOptions.m_ImportEnabled = true; |
Colm Donelan | 03bf98a | 2022-05-30 15:20:36 +0100 | [diff] [blame^] | 1045 | optOptions.m_ExportEnabled = true; |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1046 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec(), optOptions); |
| 1047 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 1048 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1049 | |
| 1050 | armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); |
| 1051 | armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); |
| 1052 | armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "input2"); |
| 1053 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "add"); |
| 1054 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "[ add (0) -> sub (1) ]"); |
| 1055 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "sub"); |
| 1056 | armnn::Layer* const layer6 = GetFirstLayerWithName(graph, "[ sub (0) -> pooling (0) ]"); |
| 1057 | armnn::Layer* const layer7 = GetFirstLayerWithName(graph, "pooling"); |
| 1058 | armnn::Layer* const layer8 = GetFirstLayerWithName(graph, "output"); |
| 1059 | |
| 1060 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1061 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 1062 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 1063 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 1064 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 1065 | CHECK(CheckOrder(graph, layer4, layer5)); |
| 1066 | CHECK(CheckOrder(graph, layer5, layer6)); |
| 1067 | CHECK(CheckOrder(graph, layer6, layer7)); |
| 1068 | CHECK(CheckOrder(graph, layer7, layer8)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1069 | |
| 1070 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1071 | CHECK((layer4->GetType() == LayerType::MemCopy)); |
| 1072 | CHECK((layer6->GetType() == LayerType::MemCopy)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1073 | |
| 1074 | // Correctly use backend hint |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1075 | CHECK((layer5->GetBackendId() == Compute::GpuAcc )); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1076 | |
| 1077 | // Load it into the runtime. It should pass. |
| 1078 | NetworkId netId; |
| 1079 | std::string ignoredErrorMessage; |
Francis Murtagh | 73d3e2e | 2021-04-29 14:23:04 +0100 | [diff] [blame] | 1080 | |
| 1081 | INetworkProperties networkProperties(false, MemorySource::Malloc, MemorySource::Malloc); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1082 | |
| 1083 | runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties); |
| 1084 | |
| 1085 | // Creates structures for input & output |
| 1086 | std::vector<float> inputData0 |
| 1087 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 1088 | 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f, 1.0f, 1.0f, 2.0f, 2.0f |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1089 | }; |
| 1090 | std::vector<float> inputData1 |
| 1091 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 1092 | 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 0.0f, 1.0f, 1.0f, 2.0f |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1093 | }; |
| 1094 | std::vector<float> inputData2 |
| 1095 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 1096 | 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f, 12.0f, 11.0f, 10.0f, 9.0f |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1097 | }; |
| 1098 | |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 1099 | std::vector<float> outputData(4); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1100 | |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 1101 | std::vector<float> expectedOutput{ 11.0f, 3.0f, -5.0f, 11.0f }; |
| 1102 | |
| 1103 | // Prepare aligned data |
| 1104 | unsigned int numElements = info.GetNumElements(); |
| 1105 | size_t totalBytes = numElements * sizeof(float); |
| 1106 | const size_t alignment = 64; |
| 1107 | size_t space = totalBytes + alignment + alignment; |
| 1108 | auto inputData = std::make_unique<uint8_t[]>(space); |
| 1109 | void* alignedInputPtr = inputData.get(); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1110 | CHECK(std::align(alignment, totalBytes, alignedInputPtr, space)); |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 1111 | |
| 1112 | auto* intputPtr = reinterpret_cast<float*>(alignedInputPtr); |
| 1113 | std::copy(inputData2.begin(), inputData2.end(), intputPtr); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1114 | |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 1115 | armnn::TensorInfo inputTensorInfo0 = runtime->GetInputTensorInfo(netId, 0); |
| 1116 | armnn::TensorInfo inputTensorInfo1 = runtime->GetInputTensorInfo(netId, 1); |
| 1117 | armnn::TensorInfo inputTensorInfo2 = runtime->GetInputTensorInfo(netId, 2); |
| 1118 | inputTensorInfo0.SetConstant(true); |
| 1119 | inputTensorInfo1.SetConstant(true); |
| 1120 | inputTensorInfo2.SetConstant(true); |
| 1121 | |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1122 | InputTensors inputTensors |
| 1123 | { |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 1124 | { 0, armnn::ConstTensor(inputTensorInfo0, inputData0.data()) }, |
| 1125 | { 1, armnn::ConstTensor(inputTensorInfo1, inputData1.data()) }, |
| 1126 | { 2, armnn::ConstTensor(inputTensorInfo2, alignedInputPtr) } |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1127 | }; |
| 1128 | OutputTensors outputTensors |
| 1129 | { |
| 1130 | { 0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 1131 | }; |
| 1132 | |
| 1133 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 1134 | |
| 1135 | // Do the inference |
| 1136 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 1137 | |
| 1138 | // Retrieve the Profiler.Print() output to get the workload execution |
| 1139 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 1140 | std::stringstream ss; |
| 1141 | profilerManager.GetProfiler()->Print(ss);; |
| 1142 | std::string dump = ss.str(); |
| 1143 | |
| 1144 | // Executed Subtraction using GpuAcc |
| 1145 | std::size_t found = dump.find("ClSubtractionWorkload_Execute"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1146 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1147 | |
| 1148 | // Correctly switch back to CpuAcc |
| 1149 | found = dump.find("NeonPooling2dWorkload_Execute"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1150 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1151 | |
| 1152 | // Contain CopyMemGeneric |
| 1153 | found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1154 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1155 | |
| 1156 | // Contains SyncMemGeneric for output |
| 1157 | found = dump.find("SyncMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1158 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1159 | |
| 1160 | // Check output is as expected |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1161 | CHECK(outputData == expectedOutput); |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 1162 | runtime->UnloadNetwork(netId); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1163 | } |
| 1164 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1165 | TEST_CASE("NeonImportDisableFallbackSubgraphToCl") |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1166 | { |
| 1167 | using namespace armnn; |
| 1168 | |
| 1169 | IRuntime::CreationOptions options; |
| 1170 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 1171 | |
| 1172 | // Builds up the structure of the network. |
| 1173 | INetworkPtr net(INetwork::Create()); |
| 1174 | |
| 1175 | Pooling2dDescriptor desc; |
| 1176 | |
| 1177 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 1178 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 1179 | IConnectableLayer* input2 = net->AddInputLayer(2, "input2"); |
| 1180 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 1181 | IConnectableLayer* sub = net->AddSubtractionLayer("sub"); |
| 1182 | IConnectableLayer* pooling = net->AddPooling2dLayer(desc, "pooling"); |
| 1183 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 1184 | |
| 1185 | input0->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 1186 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 1187 | input2->GetOutputSlot(0).Connect(sub->GetInputSlot(0)); |
| 1188 | add->GetOutputSlot(0).Connect(sub->GetInputSlot(1)); |
| 1189 | sub->GetOutputSlot(0).Connect(pooling->GetInputSlot(0)); |
| 1190 | pooling->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 1191 | |
| 1192 | TensorInfo info = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32); |
| 1193 | TensorInfo poolingInfo = TensorInfo({ 1, 2, 1, 1 }, DataType::Float32); |
| 1194 | |
| 1195 | input0->GetOutputSlot(0).SetTensorInfo(info); |
| 1196 | input1->GetOutputSlot(0).SetTensorInfo(info); |
| 1197 | input2->GetOutputSlot(0).SetTensorInfo(info); |
| 1198 | add->GetOutputSlot(0).SetTensorInfo(info); |
| 1199 | sub->GetOutputSlot(0).SetTensorInfo(info); |
| 1200 | pooling->GetOutputSlot(0).SetTensorInfo(poolingInfo); |
| 1201 | |
| 1202 | std::vector<BackendId> backends = { Compute::CpuAcc, Compute::GpuAcc }; |
| 1203 | // Use BackendSelectionHint to specify GpuAcc for Subtraction layer |
| 1204 | sub->BackendSelectionHint(backends[1]); |
| 1205 | |
| 1206 | // optimize the network |
| 1207 | OptimizerOptions optOptions; |
| 1208 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec(), optOptions); |
| 1209 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 1210 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1211 | |
| 1212 | armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); |
| 1213 | armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); |
| 1214 | armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "input2"); |
| 1215 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "add"); |
| 1216 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "[ add (0) -> sub (1) ]"); |
| 1217 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "sub"); |
| 1218 | armnn::Layer* const layer6 = GetFirstLayerWithName(graph, "[ sub (0) -> pooling (0) ]"); |
| 1219 | armnn::Layer* const layer7 = GetFirstLayerWithName(graph, "pooling"); |
| 1220 | armnn::Layer* const layer8 = GetFirstLayerWithName(graph, "output"); |
| 1221 | |
| 1222 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1223 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 1224 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 1225 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 1226 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 1227 | CHECK(CheckOrder(graph, layer4, layer5)); |
| 1228 | CHECK(CheckOrder(graph, layer5, layer6)); |
| 1229 | CHECK(CheckOrder(graph, layer6, layer7)); |
| 1230 | CHECK(CheckOrder(graph, layer7, layer8)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1231 | |
| 1232 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1233 | CHECK((layer4->GetType() == LayerType::MemCopy)); |
| 1234 | CHECK((layer6->GetType() == LayerType::MemCopy)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1235 | |
| 1236 | // Correctly use backend hint |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1237 | CHECK((layer5->GetBackendId() == Compute::GpuAcc )); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1238 | |
| 1239 | // Load it into the runtime. It should pass. |
| 1240 | NetworkId netId; |
| 1241 | runtime->LoadNetwork(netId, std::move(optNet)); |
| 1242 | |
| 1243 | // Creates structures for input & output |
| 1244 | std::vector<float> inputData0 |
| 1245 | { |
| 1246 | 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f |
| 1247 | }; |
| 1248 | std::vector<float> inputData1 |
| 1249 | { |
| 1250 | 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f |
| 1251 | }; |
| 1252 | std::vector<float> inputData2 |
| 1253 | { |
| 1254 | 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f |
| 1255 | }; |
| 1256 | |
| 1257 | std::vector<float> outputData(2); |
| 1258 | |
| 1259 | std::vector<float> expectedOutput{ 11.0f, -1.0f }; |
| 1260 | |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 1261 | armnn::TensorInfo inputTensorInfo0 = runtime->GetInputTensorInfo(netId, 0); |
| 1262 | armnn::TensorInfo inputTensorInfo1 = runtime->GetInputTensorInfo(netId, 1); |
| 1263 | armnn::TensorInfo inputTensorInfo2 = runtime->GetInputTensorInfo(netId, 2); |
| 1264 | inputTensorInfo0.SetConstant(true); |
| 1265 | inputTensorInfo1.SetConstant(true); |
| 1266 | inputTensorInfo2.SetConstant(true); |
| 1267 | |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1268 | InputTensors inputTensors |
| 1269 | { |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 1270 | { 0, armnn::ConstTensor(inputTensorInfo0, inputData0.data()) }, |
| 1271 | { 1, armnn::ConstTensor(inputTensorInfo1, inputData1.data()) }, |
| 1272 | { 2, armnn::ConstTensor(inputTensorInfo2, inputData2.data()) } |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1273 | }; |
| 1274 | OutputTensors outputTensors |
| 1275 | { |
| 1276 | { 0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 1277 | }; |
| 1278 | |
| 1279 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 1280 | |
| 1281 | // Do the inference |
| 1282 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 1283 | |
| 1284 | // Retrieve the Profiler.Print() output to get the workload execution |
| 1285 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 1286 | std::stringstream ss; |
| 1287 | profilerManager.GetProfiler()->Print(ss);; |
| 1288 | std::string dump = ss.str(); |
| 1289 | |
| 1290 | // Executed Subtraction using GpuAcc |
| 1291 | std::size_t found = dump.find("ClSubtractionWorkload_Execute"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1292 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1293 | |
| 1294 | // Correctly switch back to CpuAcc |
| 1295 | found = dump.find("NeonPooling2dWorkload_Execute"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1296 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1297 | |
| 1298 | // Contain CopyMemGeneric |
| 1299 | found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1300 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1301 | |
| 1302 | // Check output is as expected |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1303 | CHECK(outputData == expectedOutput); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1304 | } |
| 1305 | #endif |
| 1306 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1307 | } |