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