Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2022 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #define LOG_TAG "arm-armnn-sl" |
| 7 | |
| 8 | #include "CanonicalUtils.hpp" |
| 9 | |
| 10 | #include <armnn/Utils.hpp> |
| 11 | #include <armnn/utility/Assert.hpp> |
| 12 | #include <armnnSerializer/ISerializer.hpp> |
| 13 | #include <armnnUtils/Permute.hpp> |
| 14 | |
| 15 | #include <ghc/filesystem.hpp> |
| 16 | namespace fs = ghc::filesystem; |
| 17 | #include <half/half.hpp> |
| 18 | #include <log/log.h> |
| 19 | |
| 20 | #include <cassert> |
| 21 | #include <cerrno> |
| 22 | #include <cinttypes> |
| 23 | #include <cstdio> |
| 24 | #include <sstream> |
| 25 | #include <time.h> |
| 26 | #include <variant> |
| 27 | |
| 28 | namespace armnn |
| 29 | { |
| 30 | using Half = half_float::half; //import half float implementation |
| 31 | } //namespace armnn |
| 32 | |
| 33 | using namespace android; |
| 34 | using namespace android::nn; |
| 35 | |
| 36 | namespace armnn_driver |
| 37 | { |
| 38 | const armnn::PermutationVector g_DontPermute{}; |
| 39 | |
| 40 | void SwizzleAndroidNn4dTensorToArmNn(armnn::TensorInfo& tensorInfo, |
| 41 | const void* input, |
| 42 | void* output, |
| 43 | const armnn::PermutationVector& mappings) |
| 44 | { |
| 45 | assert(tensorInfo.GetNumDimensions() == 4U); |
| 46 | |
| 47 | armnn::DataType dataType = tensorInfo.GetDataType(); |
| 48 | switch (dataType) |
| 49 | { |
| 50 | case armnn::DataType::Float16: |
| 51 | case armnn::DataType::Float32: |
| 52 | case armnn::DataType::QAsymmU8: |
| 53 | case armnn::DataType::QSymmS8: |
| 54 | case armnn::DataType::QAsymmS8: |
| 55 | // First swizzle tensor info |
| 56 | tensorInfo = armnnUtils::Permuted(tensorInfo, mappings); |
| 57 | // Then swizzle tensor data |
| 58 | armnnUtils::Permute(tensorInfo.GetShape(), mappings, input, output, armnn::GetDataTypeSize(dataType)); |
| 59 | break; |
| 60 | default: |
| 61 | VLOG(DRIVER) << "Unknown armnn::DataType for swizzling"; |
| 62 | assert(0); |
| 63 | } |
| 64 | } |
| 65 | |
| 66 | void* GetMemoryFromPool(DataLocation location, const std::vector<android::nn::RunTimePoolInfo>& memPools) |
| 67 | { |
| 68 | // find the location within the pool |
| 69 | assert(location.poolIndex < memPools.size()); |
| 70 | |
| 71 | const android::nn::RunTimePoolInfo& memPool = memPools[location.poolIndex]; |
| 72 | uint8_t* memPoolBuffer = memPool.getBuffer(); |
| 73 | uint8_t* memory = memPoolBuffer + location.offset; |
| 74 | return memory; |
| 75 | } |
| 76 | |
| 77 | void* GetMemoryFromPointer(const Request::Argument& requestArg) |
| 78 | { |
| 79 | // get the pointer memory |
| 80 | auto ptrMemory = std::visit([](std::variant<const void*, void*>&& memory) |
| 81 | { |
| 82 | if (std::holds_alternative<const void*>(memory)) |
| 83 | { |
| 84 | auto ptr = std::get<const void*>(memory); |
| 85 | auto ptrMemory = static_cast<const uint8_t*>(ptr); |
| 86 | return const_cast<uint8_t*>(ptrMemory); |
| 87 | } |
| 88 | else |
| 89 | { |
| 90 | auto ptr = std::get<void*>(memory); |
| 91 | return static_cast<uint8_t*>(ptr); |
| 92 | } |
| 93 | }, requestArg.location.pointer); |
| 94 | return ptrMemory; |
| 95 | } |
| 96 | |
| 97 | armnn::TensorInfo GetTensorInfoForOperand(const Operand& operand) |
| 98 | { |
| 99 | using namespace armnn; |
| 100 | bool perChannel = false; |
| 101 | bool isScalar = false; |
| 102 | |
| 103 | DataType type; |
| 104 | switch (operand.type) |
| 105 | { |
| 106 | case OperandType::TENSOR_BOOL8: |
| 107 | type = armnn::DataType::Boolean; |
| 108 | break; |
| 109 | case OperandType::TENSOR_FLOAT32: |
| 110 | type = armnn::DataType::Float32; |
| 111 | break; |
| 112 | case OperandType::TENSOR_FLOAT16: |
| 113 | type = armnn::DataType::Float16; |
| 114 | break; |
| 115 | case OperandType::TENSOR_QUANT8_ASYMM: |
| 116 | type = armnn::DataType::QAsymmU8; |
| 117 | break; |
| 118 | case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL: |
| 119 | perChannel=true; |
| 120 | ARMNN_FALLTHROUGH; |
| 121 | case OperandType::TENSOR_QUANT8_SYMM: |
| 122 | type = armnn::DataType::QSymmS8; |
| 123 | break; |
| 124 | case OperandType::TENSOR_QUANT16_SYMM: |
| 125 | type = armnn::DataType::QSymmS16; |
| 126 | break; |
| 127 | case OperandType::TENSOR_INT32: |
| 128 | type = armnn::DataType::Signed32; |
| 129 | break; |
| 130 | case OperandType::INT32: |
| 131 | type = armnn::DataType::Signed32; |
| 132 | isScalar = true; |
| 133 | break; |
| 134 | case OperandType::TENSOR_QUANT8_ASYMM_SIGNED: |
| 135 | type = armnn::DataType::QAsymmS8; |
| 136 | break; |
| 137 | default: |
| 138 | throw UnsupportedOperand<OperandType>(operand.type); |
| 139 | } |
| 140 | |
| 141 | TensorInfo ret; |
| 142 | if (isScalar) |
| 143 | { |
| 144 | ret = TensorInfo(TensorShape(armnn::Dimensionality::Scalar), type); |
| 145 | } |
| 146 | else |
| 147 | { |
| 148 | if (operand.dimensions.size() == 0) |
| 149 | { |
| 150 | TensorShape tensorShape(Dimensionality::NotSpecified); |
| 151 | ret = TensorInfo(tensorShape, type); |
| 152 | } |
| 153 | else |
| 154 | { |
| 155 | bool dimensionsSpecificity[5] = { true, true, true, true, true }; |
| 156 | int count = 0; |
| 157 | std::for_each(operand.dimensions.data(), |
| 158 | operand.dimensions.data() + operand.dimensions.size(), |
| 159 | [&](const unsigned int val) |
| 160 | { |
| 161 | if (val == 0) |
| 162 | { |
| 163 | dimensionsSpecificity[count] = false; |
| 164 | } |
| 165 | count++; |
| 166 | }); |
| 167 | |
| 168 | TensorShape tensorShape(operand.dimensions.size(), operand.dimensions.data(), dimensionsSpecificity); |
| 169 | ret = TensorInfo(tensorShape, type); |
| 170 | } |
| 171 | } |
| 172 | |
| 173 | if (perChannel) |
| 174 | { |
| 175 | // ExtraParams is expected to be of type channelQuant |
| 176 | const auto& perAxisQuantParams = std::get<Operand::SymmPerChannelQuantParams>(operand.extraParams); |
| 177 | |
| 178 | ret.SetQuantizationScales(perAxisQuantParams.scales); |
| 179 | ret.SetQuantizationDim(MakeOptional<unsigned int>(perAxisQuantParams.channelDim)); |
| 180 | } |
| 181 | else |
| 182 | { |
| 183 | ret.SetQuantizationScale(operand.scale); |
| 184 | ret.SetQuantizationOffset(operand.zeroPoint); |
| 185 | } |
| 186 | return ret; |
| 187 | } |
| 188 | |
| 189 | std::string GetOperandSummary(const Operand& operand) |
| 190 | { |
| 191 | std::stringstream ss; |
| 192 | ss << "operand dimensions: [ "; |
| 193 | for (unsigned int i = 0; i < operand.dimensions.size(); ++i) |
| 194 | { |
| 195 | ss << operand.dimensions[i] << " "; |
| 196 | } |
| 197 | ss << "] operand type: " << operand.type; |
| 198 | return ss.str(); |
| 199 | } |
| 200 | |
Sadik Armagan | 0974238 | 2022-07-15 10:22:49 +0100 | [diff] [blame^] | 201 | template <typename TensorType> |
| 202 | using DumpElementFunction = void (*)(const TensorType& tensor, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 203 | unsigned int elementIndex, |
| 204 | std::ofstream& fileStream); |
| 205 | |
| 206 | namespace |
| 207 | { |
Sadik Armagan | 0974238 | 2022-07-15 10:22:49 +0100 | [diff] [blame^] | 208 | template <typename TensorType, typename ElementType, typename PrintableType = ElementType> |
| 209 | void DumpTensorElement(const TensorType& tensor, unsigned int elementIndex, std::ofstream& fileStream) |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 210 | { |
| 211 | const ElementType* elements = reinterpret_cast<const ElementType*>(tensor.GetMemoryArea()); |
| 212 | fileStream << static_cast<PrintableType>(elements[elementIndex]) << " "; |
| 213 | } |
| 214 | |
| 215 | } // namespace |
Sadik Armagan | 0974238 | 2022-07-15 10:22:49 +0100 | [diff] [blame^] | 216 | template <typename TensorType> |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 217 | void DumpTensor(const std::string& dumpDir, |
| 218 | const std::string& requestName, |
| 219 | const std::string& tensorName, |
Sadik Armagan | 0974238 | 2022-07-15 10:22:49 +0100 | [diff] [blame^] | 220 | const TensorType& tensor) |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 221 | { |
| 222 | // The dump directory must exist in advance. |
| 223 | fs::path dumpPath = dumpDir; |
| 224 | const fs::path fileName = dumpPath / (requestName + "_" + tensorName + ".dump"); |
| 225 | |
| 226 | std::ofstream fileStream; |
| 227 | fileStream.open(fileName.c_str(), std::ofstream::out | std::ofstream::trunc); |
| 228 | |
| 229 | if (!fileStream.good()) |
| 230 | { |
| 231 | VLOG(DRIVER) << "Could not open file %s for writing" << fileName.c_str(); |
| 232 | return; |
| 233 | } |
| 234 | |
Sadik Armagan | 0974238 | 2022-07-15 10:22:49 +0100 | [diff] [blame^] | 235 | DumpElementFunction<TensorType> dumpElementFunction = nullptr; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 236 | |
| 237 | switch (tensor.GetDataType()) |
| 238 | { |
| 239 | case armnn::DataType::Float32: |
| 240 | { |
Sadik Armagan | 0974238 | 2022-07-15 10:22:49 +0100 | [diff] [blame^] | 241 | dumpElementFunction = &DumpTensorElement<TensorType, float>; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 242 | break; |
| 243 | } |
| 244 | case armnn::DataType::QAsymmU8: |
| 245 | { |
Sadik Armagan | 0974238 | 2022-07-15 10:22:49 +0100 | [diff] [blame^] | 246 | dumpElementFunction = &DumpTensorElement<TensorType, uint8_t, uint32_t>; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 247 | break; |
| 248 | } |
| 249 | case armnn::DataType::Signed32: |
| 250 | { |
Sadik Armagan | 0974238 | 2022-07-15 10:22:49 +0100 | [diff] [blame^] | 251 | dumpElementFunction = &DumpTensorElement<TensorType, int32_t>; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 252 | break; |
| 253 | } |
| 254 | case armnn::DataType::Float16: |
| 255 | { |
Sadik Armagan | 0974238 | 2022-07-15 10:22:49 +0100 | [diff] [blame^] | 256 | dumpElementFunction = &DumpTensorElement<TensorType, armnn::Half>; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 257 | break; |
| 258 | } |
| 259 | case armnn::DataType::QAsymmS8: |
| 260 | { |
Sadik Armagan | 0974238 | 2022-07-15 10:22:49 +0100 | [diff] [blame^] | 261 | dumpElementFunction = &DumpTensorElement<TensorType, int8_t, int32_t>; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 262 | break; |
| 263 | } |
| 264 | case armnn::DataType::Boolean: |
| 265 | { |
Sadik Armagan | 0974238 | 2022-07-15 10:22:49 +0100 | [diff] [blame^] | 266 | dumpElementFunction = &DumpTensorElement<TensorType, bool>; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 267 | break; |
| 268 | } |
| 269 | default: |
| 270 | { |
| 271 | dumpElementFunction = nullptr; |
| 272 | } |
| 273 | } |
| 274 | |
| 275 | if (dumpElementFunction != nullptr) |
| 276 | { |
| 277 | const unsigned int numDimensions = tensor.GetNumDimensions(); |
| 278 | const armnn::TensorShape shape = tensor.GetShape(); |
| 279 | |
| 280 | if (!shape.AreAllDimensionsSpecified()) |
| 281 | { |
| 282 | fileStream << "Cannot dump tensor elements: not all dimensions are specified" << std::endl; |
| 283 | return; |
| 284 | } |
| 285 | fileStream << "# Number of elements " << tensor.GetNumElements() << std::endl; |
| 286 | |
| 287 | if (numDimensions == 0) |
| 288 | { |
| 289 | fileStream << "# Shape []" << std::endl; |
| 290 | return; |
| 291 | } |
| 292 | fileStream << "# Shape [" << shape[0]; |
| 293 | for (unsigned int d = 1; d < numDimensions; ++d) |
| 294 | { |
| 295 | fileStream << "," << shape[d]; |
| 296 | } |
| 297 | fileStream << "]" << std::endl; |
| 298 | fileStream << "Each line contains the data of each of the elements of dimension0. In NCHW and NHWC, each line" |
| 299 | " will be a batch" << std::endl << std::endl; |
| 300 | |
| 301 | // Split will create a new line after all elements of the first dimension |
| 302 | // (in a 4, 3, 2, 3 tensor, there will be 4 lines of 18 elements) |
| 303 | unsigned int split = 1; |
| 304 | if (numDimensions == 1) |
| 305 | { |
| 306 | split = shape[0]; |
| 307 | } |
| 308 | else |
| 309 | { |
| 310 | for (unsigned int i = 1; i < numDimensions; ++i) |
| 311 | { |
| 312 | split *= shape[i]; |
| 313 | } |
| 314 | } |
| 315 | |
| 316 | // Print all elements in the tensor |
| 317 | for (unsigned int elementIndex = 0; elementIndex < tensor.GetNumElements(); ++elementIndex) |
| 318 | { |
| 319 | (*dumpElementFunction)(tensor, elementIndex, fileStream); |
| 320 | |
| 321 | if ( (elementIndex + 1) % split == 0 ) |
| 322 | { |
| 323 | fileStream << std::endl; |
| 324 | } |
| 325 | } |
| 326 | fileStream << std::endl; |
| 327 | } |
| 328 | else |
| 329 | { |
| 330 | fileStream << "Cannot dump tensor elements: Unsupported data type " |
| 331 | << static_cast<unsigned int>(tensor.GetDataType()) << std::endl; |
| 332 | } |
| 333 | |
| 334 | if (!fileStream.good()) |
| 335 | { |
| 336 | VLOG(DRIVER) << "An error occurred when writing to file %s" << fileName.c_str(); |
| 337 | } |
| 338 | } |
| 339 | |
Sadik Armagan | 0974238 | 2022-07-15 10:22:49 +0100 | [diff] [blame^] | 340 | template void DumpTensor<armnn::ConstTensor>(const std::string& dumpDir, |
| 341 | const std::string& requestName, |
| 342 | const std::string& tensorName, |
| 343 | const armnn::ConstTensor& tensor); |
| 344 | |
| 345 | template void DumpTensor<armnn::Tensor>(const std::string& dumpDir, |
| 346 | const std::string& requestName, |
| 347 | const std::string& tensorName, |
| 348 | const armnn::Tensor& tensor); |
| 349 | |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 350 | void DumpJsonProfilingIfRequired(bool gpuProfilingEnabled, |
| 351 | const std::string& dumpDir, |
| 352 | armnn::NetworkId networkId, |
| 353 | const armnn::IProfiler* profiler) |
| 354 | { |
| 355 | // Check if profiling is required. |
| 356 | if (!gpuProfilingEnabled) |
| 357 | { |
| 358 | return; |
| 359 | } |
| 360 | |
| 361 | // The dump directory must exist in advance. |
| 362 | if (dumpDir.empty()) |
| 363 | { |
| 364 | return; |
| 365 | } |
| 366 | |
| 367 | ARMNN_ASSERT(profiler); |
| 368 | |
| 369 | // Set the name of the output profiling file. |
| 370 | fs::path dumpPath = dumpDir; |
| 371 | const fs::path fileName = dumpPath / (std::to_string(networkId) + "_profiling.json"); |
| 372 | |
| 373 | // Open the ouput file for writing. |
| 374 | std::ofstream fileStream; |
| 375 | fileStream.open(fileName.c_str(), std::ofstream::out | std::ofstream::trunc); |
| 376 | |
| 377 | if (!fileStream.good()) |
| 378 | { |
| 379 | VLOG(DRIVER) << "Could not open file %s for writing" << fileName.c_str(); |
| 380 | return; |
| 381 | } |
| 382 | |
| 383 | // Write the profiling info to a JSON file. |
| 384 | profiler->Print(fileStream); |
| 385 | } |
| 386 | |
| 387 | std::string ExportNetworkGraphToDotFile(const armnn::IOptimizedNetwork& optimizedNetwork, |
| 388 | const std::string& dumpDir) |
| 389 | { |
| 390 | std::string fileName; |
| 391 | // The dump directory must exist in advance. |
| 392 | if (dumpDir.empty()) |
| 393 | { |
| 394 | return fileName; |
| 395 | } |
| 396 | |
| 397 | std::string timestamp = GetFileTimestamp(); |
| 398 | if (timestamp.empty()) |
| 399 | { |
| 400 | return fileName; |
| 401 | } |
| 402 | |
| 403 | // Set the name of the output .dot file. |
| 404 | fs::path dumpPath = dumpDir; |
| 405 | fs::path tempFilePath = dumpPath / (timestamp + "_networkgraph.dot"); |
| 406 | fileName = tempFilePath.string(); |
| 407 | |
| 408 | VLOG(DRIVER) << "Exporting the optimized network graph to file: %s" << fileName.c_str(); |
| 409 | |
| 410 | // Write the network graph to a dot file. |
| 411 | std::ofstream fileStream; |
| 412 | fileStream.open(fileName, std::ofstream::out | std::ofstream::trunc); |
| 413 | |
| 414 | if (!fileStream.good()) |
| 415 | { |
| 416 | VLOG(DRIVER) << "Could not open file %s for writing" << fileName.c_str(); |
| 417 | return fileName; |
| 418 | } |
| 419 | |
| 420 | if (optimizedNetwork.SerializeToDot(fileStream) != armnn::Status::Success) |
| 421 | { |
| 422 | VLOG(DRIVER) << "An error occurred when writing to file %s" << fileName.c_str(); |
| 423 | } |
| 424 | return fileName; |
| 425 | } |
| 426 | |
| 427 | std::string SerializeNetwork(const armnn::INetwork& network, |
| 428 | const std::string& dumpDir, |
| 429 | std::vector<uint8_t>& dataCacheData, |
| 430 | bool dataCachingActive) |
| 431 | { |
| 432 | std::string fileName; |
| 433 | bool bSerializeToFile = true; |
| 434 | if (dumpDir.empty()) |
| 435 | { |
| 436 | bSerializeToFile = false; |
| 437 | } |
| 438 | else |
| 439 | { |
| 440 | std::string timestamp = GetFileTimestamp(); |
| 441 | if (timestamp.empty()) |
| 442 | { |
| 443 | bSerializeToFile = false; |
| 444 | } |
| 445 | } |
| 446 | if (!bSerializeToFile && !dataCachingActive) |
| 447 | { |
| 448 | return fileName; |
| 449 | } |
| 450 | |
| 451 | auto serializer(armnnSerializer::ISerializer::Create()); |
| 452 | // Serialize the Network |
| 453 | serializer->Serialize(network); |
| 454 | if (dataCachingActive) |
| 455 | { |
| 456 | std::stringstream stream; |
| 457 | auto serialized = serializer->SaveSerializedToStream(stream); |
| 458 | if (serialized) |
| 459 | { |
| 460 | std::string const serializedString{stream.str()}; |
| 461 | std::copy(serializedString.begin(), |
| 462 | serializedString.end(), |
| 463 | std::back_inserter(dataCacheData)); |
| 464 | } |
| 465 | } |
| 466 | |
| 467 | if (bSerializeToFile) |
| 468 | { |
| 469 | // Set the name of the output .armnn file. |
| 470 | fs::path dumpPath = dumpDir; |
| 471 | std::string timestamp = GetFileTimestamp(); |
| 472 | fs::path tempFilePath = dumpPath / (timestamp + "_network.armnn"); |
| 473 | fileName = tempFilePath.string(); |
| 474 | |
| 475 | // Save serialized network to a file |
| 476 | std::ofstream serializedFile(fileName, std::ios::out | std::ios::binary); |
| 477 | auto serialized = serializer->SaveSerializedToStream(serializedFile); |
| 478 | if (!serialized) |
| 479 | { |
| 480 | VLOG(DRIVER) << "An error occurred when serializing to file %s" << fileName.c_str(); |
| 481 | } |
| 482 | } |
| 483 | return fileName; |
| 484 | } |
| 485 | |
| 486 | bool IsDynamicTensor(const armnn::TensorInfo& tensorInfo) |
| 487 | { |
| 488 | if (tensorInfo.GetShape().GetDimensionality() == armnn::Dimensionality::NotSpecified) |
| 489 | { |
| 490 | return true; |
| 491 | } |
| 492 | // Account for the usage of the TensorShape empty constructor |
| 493 | if (tensorInfo.GetNumDimensions() == 0) |
| 494 | { |
| 495 | return true; |
| 496 | } |
| 497 | return !tensorInfo.GetShape().AreAllDimensionsSpecified(); |
| 498 | } |
| 499 | |
| 500 | bool AreDynamicTensorsSupported() //TODO |
| 501 | { |
| 502 | return true; |
| 503 | } |
| 504 | |
| 505 | bool isQuantizedOperand(const OperandType& operandType) |
| 506 | { |
| 507 | if (operandType == OperandType::TENSOR_QUANT8_ASYMM || |
| 508 | operandType == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL || |
| 509 | operandType == OperandType::TENSOR_QUANT8_SYMM || |
| 510 | operandType == OperandType::TENSOR_QUANT16_SYMM || |
| 511 | operandType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) |
| 512 | { |
| 513 | return true; |
| 514 | } |
| 515 | else |
| 516 | { |
| 517 | return false; |
| 518 | } |
| 519 | } |
| 520 | |
| 521 | std::string GetModelSummary(const Model& model) |
| 522 | { |
| 523 | std::stringstream result; |
| 524 | |
| 525 | result << model.main.inputIndexes.size() << " input(s), " |
| 526 | << model.main.operations.size() << " operation(s), " |
| 527 | << model.main.outputIndexes.size() << " output(s), " |
| 528 | << model.main.operands.size() << " operand(s) " |
| 529 | << std::endl; |
| 530 | |
| 531 | result << "Inputs: "; |
| 532 | for (uint32_t i = 0; i < model.main.inputIndexes.size(); i++) |
| 533 | { |
| 534 | result << GetOperandSummary(model.main.operands[model.main.inputIndexes[i]]) << ", "; |
| 535 | } |
| 536 | result << std::endl; |
| 537 | |
| 538 | result << "Operations: "; |
| 539 | for (uint32_t i = 0; i < model.main.operations.size(); i++) |
| 540 | { |
| 541 | result << model.main.operations[i].type << ", "; |
| 542 | } |
| 543 | result << std::endl; |
| 544 | |
| 545 | result << "Outputs: "; |
| 546 | for (uint32_t i = 0; i < model.main.outputIndexes.size(); i++) |
| 547 | { |
| 548 | result << GetOperandSummary(model.main.operands[model.main.outputIndexes[i]]) << ", "; |
| 549 | } |
| 550 | result << std::endl; |
| 551 | |
| 552 | return result.str(); |
| 553 | } |
| 554 | |
| 555 | std::string GetFileTimestamp() |
| 556 | { |
| 557 | // used to get a timestamp to name diagnostic files (the ArmNN serialized graph |
| 558 | // and getSupportedOperations.txt files) |
| 559 | timespec ts; |
| 560 | int iRet = clock_gettime(CLOCK_MONOTONIC_RAW, &ts); |
| 561 | std::stringstream ss; |
| 562 | if (iRet == 0) |
| 563 | { |
| 564 | ss << std::to_string(ts.tv_sec) << "_" << std::to_string(ts.tv_nsec); |
| 565 | } |
| 566 | else |
| 567 | { |
| 568 | VLOG(DRIVER) << "clock_gettime failed with errno " << |
| 569 | std::to_string(errno).c_str() << " : " << |
| 570 | std::strerror(errno); |
| 571 | } |
| 572 | return ss.str(); |
| 573 | } |
| 574 | |
| 575 | void RenameExportedFiles(const std::string& existingSerializedFileName, |
| 576 | const std::string& existingDotFileName, |
| 577 | const std::string& dumpDir, |
| 578 | const armnn::NetworkId networkId) |
| 579 | { |
| 580 | if (dumpDir.empty()) |
| 581 | { |
| 582 | return; |
| 583 | } |
| 584 | RenameFile(existingSerializedFileName, std::string("_network.armnn"), dumpDir, networkId); |
| 585 | RenameFile(existingDotFileName, std::string("_networkgraph.dot"), dumpDir, networkId); |
| 586 | } |
| 587 | |
| 588 | void RenameFile(const std::string& existingName, |
| 589 | const std::string& extension, |
| 590 | const std::string& dumpDir, |
| 591 | const armnn::NetworkId networkId) |
| 592 | { |
| 593 | if (existingName.empty() || dumpDir.empty()) |
| 594 | { |
| 595 | return; |
| 596 | } |
| 597 | |
| 598 | fs::path dumpPath = dumpDir; |
| 599 | const fs::path newFileName = dumpPath / (std::to_string(networkId) + extension); |
| 600 | int iRet = rename(existingName.c_str(), newFileName.c_str()); |
| 601 | if (iRet != 0) |
| 602 | { |
| 603 | std::stringstream ss; |
| 604 | ss << "rename of [" << existingName << "] to [" << newFileName << "] failed with errno " |
| 605 | << std::to_string(errno) << " : " << std::strerror(errno); |
| 606 | VLOG(DRIVER) << ss.str().c_str(); |
| 607 | } |
| 608 | } |
| 609 | |
| 610 | void CommitPools(std::vector<::android::nn::RunTimePoolInfo>& memPools) |
| 611 | { |
| 612 | // Commit output buffers. |
| 613 | // Note that we update *all* pools, even if they aren't actually used as outputs - |
| 614 | // this is simpler and is what the CpuExecutor does. |
| 615 | for (auto& pool : memPools) |
| 616 | { |
| 617 | // Type android::nn::RunTimePoolInfo has changed between Android P & Q and Android R, where |
| 618 | // update() has been removed and flush() added. |
| 619 | pool.flush(); |
| 620 | } |
| 621 | } |
| 622 | } // namespace armnn_driver |