diff options
| author | Ryan OShea <ryan.oshea3@arm.com> | 2023-03-07 11:44:23 +0000 |
|---|---|---|
| committer | ryan.oshea3 <ryan.oshea3@arm.com> | 2023-03-22 13:12:55 +0000 |
| commit | 238ecd9279f08a1b05189e03ffa73a9201b1c0ce (patch) | |
| tree | 942386ab532039fb423ac951f375c484352faece /delegate | |
| parent | 3ec3077b4eaedcc0c20ab5774bdbe365da541445 (diff) | |
| download | armnn-238ecd9279f08a1b05189e03ffa73a9201b1c0ce.tar.gz | |
IVGCVSW-7526 Upgrade ArmNN to Tensorflow 2.12
When creating a flatbuffers model, we need to provide an empty buffer 0 that is
reserved by tensorflow. When creating empty buffers for inputs and outputs we
can not pass in an empty vector, or tflite will assume that we know how many bytes to
allocate in advance. Instead we need to only pass in the builder.
* Update libraries in FindTfLite.cmake
* Add nullptr to delegate struct for OpaqueDelegateBuilder
* Fix issue in unit tests where Flatbuffers model was not being parsed by tflite
* Tensorflow 2.12 now includes C++ 17 features. Update our cmake build
to require a compiler to support these features.
* Change minimum cmake in Arm NN to 3.7 as that's the minimum for the
delegate build.
Signed-off-by: Ryan OShea <ryan.oshea3@arm.com>
Signed-off-by: Narumol Prangnawarat <narumol.prangnawarat@arm.com>
Signed-off-by: Colm Donelan <colm.donelan@arm.com>
Change-Id: I7d15b196b8c59b1914f8fc1c4c2f8960630c069c
Diffstat (limited to 'delegate')
40 files changed, 780 insertions, 680 deletions
diff --git a/delegate/CMakeLists.txt b/delegate/CMakeLists.txt index d044ed991d..55bdb7ca53 100644 --- a/delegate/CMakeLists.txt +++ b/delegate/CMakeLists.txt @@ -5,8 +5,10 @@ cmake_minimum_required (VERSION 3.7.0) project(armnnDelegate) +set(CMAKE_CXX_STANDARD 17) +set(CMAKE_CXX_STANDARD_REQUIRED ON) -set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14 -Wall -Wextra -Werror -Wold-style-cast -Wno-missing-braces -Wconversion -Wsign-conversion -Wno-comment") +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++17 -Wall -Wextra -Werror -Wold-style-cast -Wno-missing-braces -Wconversion -Wsign-conversion -Wno-comment") set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${PROJECT_SOURCE_DIR}/cmake/Modules/") diff --git a/delegate/cmake/Modules/FindTfLite.cmake b/delegate/cmake/Modules/FindTfLite.cmake index 338cde1697..634aaea511 100644 --- a/delegate/cmake/Modules/FindTfLite.cmake +++ b/delegate/cmake/Modules/FindTfLite.cmake @@ -1,5 +1,5 @@ # -# Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +# Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. # SPDX-License-Identifier: MIT # @@ -38,8 +38,6 @@ if (TfLite_LIB MATCHES .a$) PATH ${TFLITE_LIB_ROOT}/_deps/flatbuffers-build) find_library(TfLite_cpuinfo_LIB "libcpuinfo.a" PATH ${TFLITE_LIB_ROOT}/_deps/cpuinfo-build) - find_library(TfLite_clog_LIB "libclog.a" PATH - ${TFLITE_LIB_ROOT}/_deps/clog-build) # All remaining libraries are part of libruy. find_library(TfLite_ruy_allocator_LIB "libruy_allocator.a" PATH @@ -100,6 +98,8 @@ if (TfLite_LIB MATCHES .a$) ${TFLITE_LIB_ROOT}/_deps/ruy-build/ruy) find_library(TfLite_ruy_profiler_LIB "libruy_profiler_instrumentation.a" PATH ${TFLITE_LIB_ROOT}/_deps/ruy-build/ruy/profiler) + find_library(TfLite_pthread_pool_LIB "libpthreadpool.a" PATH + ${TFLITE_LIB_ROOT}/pthreadpool) ## Set TFLITE_FOUND if all libraries are satisfied for static lib find_package_handle_standard_args(TfLite DEFAULT_MSG TfLite_LIB TfLite_abseilstrings_LIB TfLite_farmhash_LIB TfLite_fftsg_LIB TfLite_fftsg2d_LIB @@ -110,8 +110,8 @@ if (TfLite_LIB MATCHES .a$) TfLite_ruy_kernel_avx2_fma_LIB TfLite_ruy_kernel_avx512_LIB TfLite_ruy_kernel_avx_LIB TfLite_ruy_pack_arm_LIB TfLite_ruy_pack_avx2_fma_LIB TfLite_ruy_pack_avx512_LIB TfLite_ruy_pack_avx_LIB TfLite_ruy_prepacked_cache_LIB TfLite_ruy_prepare_packed_matrices_LIB TfLite_ruy_system_aligned_alloc_LIB TfLite_ruy_threadpool_LIB - TfLite_ruy_trmul_LIB TfLite_ruy_tune_LIB TfLite_ruy_wait_LIB TfLite_ruy_profiler_LIB TfLite_cpuinfo_LIB TfLite_clog_LIB - TfLite_abseil_synchronization_LIB) + TfLite_ruy_trmul_LIB TfLite_ruy_tune_LIB TfLite_ruy_wait_LIB TfLite_ruy_profiler_LIB TfLite_cpuinfo_LIB + TfLite_abseil_synchronization_LIB TfLite_pthread_pool_LIB) # Set external variables for usage in CMakeLists.txt if (TFLITE_FOUND) # WARNING! The order of these libraries is critical. Moving them @@ -126,7 +126,7 @@ if (TfLite_LIB MATCHES .a$) ${TfLite_ruy_pack_avx2_fma_LIB} ${TfLite_ruy_pack_avx512_LIB} ${TfLite_ruy_pack_avx_LIB} ${TfLite_ruy_prepacked_cache_LIB} ${TfLite_ruy_prepare_packed_matrices_LIB} ${TfLite_ruy_system_aligned_alloc_LIB} ${TfLite_ruy_tune_LIB} ${TfLite_ruy_wait_LIB} ${TfLite_ruy_profiler_LIB} - ${TfLite_cpuinfo_LIB} ${TfLite_clog_LIB} ${TfLite_abseil_synchronization_LIB}) + ${TfLite_cpuinfo_LIB} ${TfLite_abseil_synchronization_LIB} ${TfLite_pthread_pool_LIB}) endif () elseif (TfLite_LIB MATCHES .so$) message("-- Dynamic tensorflow lite library found, using for ArmNN build") diff --git a/delegate/include/armnn_delegate.hpp b/delegate/include/armnn_delegate.hpp index 159d590423..9cfc397550 100644 --- a/delegate/include/armnn_delegate.hpp +++ b/delegate/include/armnn_delegate.hpp @@ -81,6 +81,7 @@ private: nullptr, // .CopyToBufferHandle nullptr, // .FreeBufferHandle kTfLiteDelegateFlagsNone, // .flags + nullptr, // .opaque_delegate_builder }; /// ArmNN Runtime pointer diff --git a/delegate/src/test/ActivationTestHelper.hpp b/delegate/src/test/ActivationTestHelper.hpp index 0f4d944685..6475083da0 100644 --- a/delegate/src/test/ActivationTestHelper.hpp +++ b/delegate/src/test/ActivationTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -29,7 +29,7 @@ std::vector<char> CreateActivationTfLiteModel(tflite::BuiltinOperator activation flatbuffers::FlatBufferBuilder flatBufferBuilder; std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers; - buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); + buffers[0] = CreateBuffer(flatBufferBuilder); std::array<flatbuffers::Offset<Tensor>, 2> tensors; tensors[0] = CreateTensor(flatBufferBuilder, diff --git a/delegate/src/test/ArgMinMaxTestHelper.hpp b/delegate/src/test/ArgMinMaxTestHelper.hpp index a734c819f9..3e607d6b2b 100644 --- a/delegate/src/test/ArgMinMaxTestHelper.hpp +++ b/delegate/src/test/ArgMinMaxTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -46,7 +46,7 @@ std::vector<char> CreateArgMinMaxTfLiteModel(tflite::BuiltinOperator argMinMaxOp flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input"), quantizationParameters); @@ -54,26 +54,27 @@ std::vector<char> CreateArgMinMaxTfLiteModel(tflite::BuiltinOperator argMinMaxOp flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(), axisTensorShape.size()), tflite::TensorType_INT32, - 1, + 2, flatBufferBuilder.CreateString("axis")); auto outputTensor = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), outputTensorShape.size()), outputType, - 2, + 3, flatBufferBuilder.CreateString("output"), quantizationParameters); std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, axisTensor, outputTensor }; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); buffers.push_back( CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisValue.data()), sizeof(OutputT)))); - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); std::vector<int32_t> operatorInputs = {{ 0, 1 }}; std::vector<int> subgraphInputs = {{ 0, 1 }}; diff --git a/delegate/src/test/BatchMatMulTestHelper.hpp b/delegate/src/test/BatchMatMulTestHelper.hpp index 42c1ed6a1e..7437064a42 100644 --- a/delegate/src/test/BatchMatMulTestHelper.hpp +++ b/delegate/src/test/BatchMatMulTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -20,184 +20,186 @@ namespace { - - std::vector<char> CreateBatchMatMulTfLiteModel( - tflite::BuiltinOperator bmmOperatorCode, - tflite::TensorType tensorType, - const std::vector <int32_t>& LHSInputTensorShape, - const std::vector <int32_t>& RHSInputTensorShape, - const std::vector <int32_t>& outputTensorShape, - bool adjX = false, - bool adjY = false, - float quantScale = 1.0f, - int quantOffset = 0) +std::vector<char> CreateBatchMatMulTfLiteModel( + tflite::BuiltinOperator bmmOperatorCode, + tflite::TensorType tensorType, + const std::vector <int32_t>& LHSInputTensorShape, + const std::vector <int32_t>& RHSInputTensorShape, + const std::vector <int32_t>& outputTensorShape, + bool adjX = false, + bool adjY = false, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + + std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + + auto quantizationParameters = + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({ quantScale }), + flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); + + std::array<flatbuffers::Offset<Tensor>, 3> tensors; + tensors[0] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(LHSInputTensorShape.data(), + LHSInputTensorShape.size()), + tensorType, + 1, + flatBufferBuilder.CreateString("LHSInput"), + quantizationParameters); + + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(RHSInputTensorShape.data(), + RHSInputTensorShape.size()), + tensorType, + 2, + flatBufferBuilder.CreateString("RHSInput"), + quantizationParameters); + + tensors[2] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), + outputTensorShape.size()), + tensorType, + 3, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + + // create operator + tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_BatchMatMulOptions; + flatbuffers::Offset<void> operatorBuiltinOptions = CreateBatchMatMulOptions(flatBufferBuilder, + adjX, + adjY).Union(); + + const std::vector<int32_t> operatorInputs{{0, 1}}; + const std::vector<int32_t> operatorOutputs{2}; + flatbuffers::Offset <Operator> bmmOperator = + CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), + operatorOutputs.size()), + operatorBuiltinOptionsType, + operatorBuiltinOptions); + + const std::vector<int> subgraphInputs{{0, 1}}; + const std::vector<int> subgraphOutputs{2}; + flatbuffers::Offset <SubGraph> subgraph = + CreateSubGraph(flatBufferBuilder, + flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), + flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), + subgraphOutputs.size()), + flatBufferBuilder.CreateVector(&bmmOperator, 1)); + + flatbuffers::Offset <flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: BatchMatMul Operator Model"); + flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, bmmOperatorCode); + + flatbuffers::Offset <Model> flatbufferModel = + CreateModel(flatBufferBuilder, + TFLITE_SCHEMA_VERSION, + flatBufferBuilder.CreateVector(&operatorCode, 1), + flatBufferBuilder.CreateVector(&subgraph, 1), + modelDescription, + flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); + + flatBufferBuilder.Finish(flatbufferModel); + + return std::vector<char>(flatBufferBuilder.GetBufferPointer(), + flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); +} + +template <typename T> +void BatchMatMulTest(tflite::BuiltinOperator bmmOperatorCode, + tflite::TensorType tensorType, + std::vector<armnn::BackendId>& backends, + std::vector<int32_t>& LHSInputShape, + std::vector<int32_t>& RHSInputShape, + std::vector<int32_t>& outputShape, + std::vector<T>& LHSInputValues, + std::vector<T>& RHSInputValues, + std::vector<T>& expectedOutputValues, + bool adjX = false, + bool adjY = false, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + std::vector<char> modelBuffer = CreateBatchMatMulTfLiteModel(bmmOperatorCode, + tensorType, + LHSInputShape, + RHSInputShape, + outputShape, + adjX, + adjY, + quantScale, + quantOffset); + + const Model* tfLiteModel = GetModel(modelBuffer.data()); + CHECK(tfLiteModel != nullptr); + // Create TfLite Interpreters + std::unique_ptr<Interpreter> armnnDelegateInterpreter; + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&armnnDelegateInterpreter) == kTfLiteOk); + CHECK(armnnDelegateInterpreter != nullptr); + CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); + + std::unique_ptr<Interpreter> tfLiteInterpreter; + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&tfLiteInterpreter) == kTfLiteOk); + CHECK(tfLiteInterpreter != nullptr); + CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk); + + // Create the ArmNN Delegate + armnnDelegate::DelegateOptions delegateOptions(backends); + std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> + theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), + armnnDelegate::TfLiteArmnnDelegateDelete); + CHECK(theArmnnDelegate != nullptr); + // Modify armnnDelegateInterpreter to use armnnDelegate + CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); + + // Set input data + auto tfLiteDelegateLHSInputId = tfLiteInterpreter->inputs()[0]; + auto tfLiteDelegateLHSInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateLHSInputId); + auto tfLiteDelegateRHSInputId = tfLiteInterpreter->inputs()[1]; + auto tfLiteDelegateRHSInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateRHSInputId); + for (unsigned int i = 0; i < LHSInputValues.size(); ++i) + { + tfLiteDelegateLHSInputData[i] = LHSInputValues[i]; + } + for (unsigned int i = 0; i < RHSInputValues.size(); ++i) { - using namespace tflite; - flatbuffers::FlatBufferBuilder flatBufferBuilder; - - std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); - - std::array<flatbuffers::Offset<Tensor>, 3> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(LHSInputTensorShape.data(), - LHSInputTensorShape.size()), - tensorType, - 0, - flatBufferBuilder.CreateString("LHSInput"), - quantizationParameters); - - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(RHSInputTensorShape.data(), - RHSInputTensorShape.size()), - tensorType, - 0, - flatBufferBuilder.CreateString("RHSInput"), - quantizationParameters); - - tensors[2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 0, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_BatchMatMulOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = CreateBatchMatMulOptions(flatBufferBuilder, - adjX, - adjY).Union(); - - const std::vector<int32_t> operatorInputs{{0, 1}}; - const std::vector<int32_t> operatorOutputs{2}; - flatbuffers::Offset <Operator> bmmOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), - operatorOutputs.size()), - operatorBuiltinOptionsType, - operatorBuiltinOptions); - - const std::vector<int> subgraphInputs{{0, 1}}; - const std::vector<int> subgraphOutputs{2}; - flatbuffers::Offset <SubGraph> subgraph = - CreateSubGraph(flatBufferBuilder, - flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), - flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), - subgraphOutputs.size()), - flatBufferBuilder.CreateVector(&bmmOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: BatchMatMul Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, bmmOperatorCode); - - flatbuffers::Offset <Model> flatbufferModel = - CreateModel(flatBufferBuilder, - TFLITE_SCHEMA_VERSION, - flatBufferBuilder.CreateVector(&operatorCode, 1), - flatBufferBuilder.CreateVector(&subgraph, 1), - modelDescription, - flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); - - flatBufferBuilder.Finish(flatbufferModel); - - return std::vector<char>(flatBufferBuilder.GetBufferPointer(), - flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); + tfLiteDelegateRHSInputData[i] = RHSInputValues[i]; } - template <typename T> - void BatchMatMulTest(tflite::BuiltinOperator bmmOperatorCode, - tflite::TensorType tensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& LHSInputShape, - std::vector<int32_t>& RHSInputShape, - std::vector<int32_t>& outputShape, - std::vector<T>& LHSInputValues, - std::vector<T>& RHSInputValues, - std::vector<T>& expectedOutputValues, - bool adjX = false, - bool adjY = false, - float quantScale = 1.0f, - int quantOffset = 0) + auto armnnDelegateLHSInputId = armnnDelegateInterpreter->inputs()[0]; + auto armnnDelegateLHSInputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateLHSInputId); + auto armnnDelegateRHSInputId = armnnDelegateInterpreter->inputs()[1]; + auto armnnDelegateRHSInputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateRHSInputId); + for (unsigned int i = 0; i < LHSInputValues.size(); ++i) + { + armnnDelegateLHSInputData[i] = LHSInputValues[i]; + } + for (unsigned int i = 0; i < RHSInputValues.size(); ++i) { - using namespace tflite; - std::vector<char> modelBuffer = CreateBatchMatMulTfLiteModel(bmmOperatorCode, - tensorType, - LHSInputShape, - RHSInputShape, - outputShape, - adjX, - adjY, - quantScale, - quantOffset); - - const Model* tfLiteModel = GetModel(modelBuffer.data()); - CHECK(tfLiteModel != nullptr); - // Create TfLite Interpreters - std::unique_ptr<Interpreter> armnnDelegateInterpreter; - CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&armnnDelegateInterpreter) == kTfLiteOk); - CHECK(armnnDelegateInterpreter != nullptr); - CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); - - std::unique_ptr<Interpreter> tfLiteInterpreter; - CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&tfLiteInterpreter) == kTfLiteOk); - CHECK(tfLiteInterpreter != nullptr); - CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk); - - // Create the ArmNN Delegate - armnnDelegate::DelegateOptions delegateOptions(backends); - std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> - theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), - armnnDelegate::TfLiteArmnnDelegateDelete); - CHECK(theArmnnDelegate != nullptr); - // Modify armnnDelegateInterpreter to use armnnDelegate - CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); - - // Set input data - auto tfLiteDelegateLHSInputId = tfLiteInterpreter->inputs()[0]; - auto tfLiteDelegateLHSInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateLHSInputId); - auto tfLiteDelegateRHSInputId = tfLiteInterpreter->inputs()[1]; - auto tfLiteDelegateRHSInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateRHSInputId); - for (unsigned int i = 0; i < LHSInputValues.size(); ++i) - { - tfLiteDelegateLHSInputData[i] = LHSInputValues[i]; - } - for (unsigned int i = 0; i < RHSInputValues.size(); ++i) - { - tfLiteDelegateRHSInputData[i] = RHSInputValues[i]; - } - - auto armnnDelegateLHSInputId = armnnDelegateInterpreter->inputs()[0]; - auto armnnDelegateLHSInputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateLHSInputId); - auto armnnDelegateRHSInputId = armnnDelegateInterpreter->inputs()[1]; - auto armnnDelegateRHSInputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateRHSInputId); - for (unsigned int i = 0; i < LHSInputValues.size(); ++i) - { - armnnDelegateLHSInputData[i] = LHSInputValues[i]; - } - for (unsigned int i = 0; i < RHSInputValues.size(); ++i) - { - armnnDelegateRHSInputData[i] = RHSInputValues[i]; - } - // Run EnqueueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - armnnDelegate::CompareOutputData(tfLiteInterpreter, armnnDelegateInterpreter, - outputShape, expectedOutputValues); + armnnDelegateRHSInputData[i] = RHSInputValues[i]; } + // Run EnqueueWorkload + CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); + CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); + + armnnDelegate::CompareOutputData(tfLiteInterpreter, armnnDelegateInterpreter, + outputShape, expectedOutputValues); +} } // anonymous namespace diff --git a/delegate/src/test/BatchSpaceTestHelper.hpp b/delegate/src/test/BatchSpaceTestHelper.hpp index 464a5d9cbe..d4fa9837e8 100644 --- a/delegate/src/test/BatchSpaceTestHelper.hpp +++ b/delegate/src/test/BatchSpaceTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -33,14 +33,16 @@ std::vector<char> CreateBatchSpaceTfLiteModel(tflite::BuiltinOperator batchSpace using namespace tflite; flatbuffers::FlatBufferBuilder flatBufferBuilder; - std::array<flatbuffers::Offset<tflite::Buffer>, 3> buffers; - buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); - buffers[1] = CreateBuffer(flatBufferBuilder, + std::array<flatbuffers::Offset<tflite::Buffer>, 5> buffers; + buffers[0] = CreateBuffer(flatBufferBuilder); + buffers[1] = CreateBuffer(flatBufferBuilder); + buffers[2] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(blockData.data()), sizeof(int32_t) * blockData.size())); - buffers[2] = CreateBuffer(flatBufferBuilder, + buffers[3] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(cropsPadData.data()), sizeof(int64_t) * cropsPadData.size())); + buffers[4] = CreateBuffer(flatBufferBuilder); auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, @@ -60,7 +62,7 @@ std::vector<char> CreateBatchSpaceTfLiteModel(tflite::BuiltinOperator batchSpace flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input"), quantizationParameters); @@ -68,7 +70,7 @@ std::vector<char> CreateBatchSpaceTfLiteModel(tflite::BuiltinOperator batchSpace flatBufferBuilder.CreateVector<int32_t>(blockShape.data(), blockShape.size()), ::tflite::TensorType_INT32, - 1, + 2, flatBufferBuilder.CreateString("block"), quantizationParameters); @@ -76,7 +78,7 @@ std::vector<char> CreateBatchSpaceTfLiteModel(tflite::BuiltinOperator batchSpace flatBufferBuilder.CreateVector<int32_t>(cropsOrPaddingShape.data(), cropsOrPaddingShape.size()), ::tflite::TensorType_INT32, - 2, + 3, flatBufferBuilder.CreateString(cropsOrPadding), quantizationParameters); @@ -85,7 +87,7 @@ std::vector<char> CreateBatchSpaceTfLiteModel(tflite::BuiltinOperator batchSpace flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), outputTensorShape.size()), tensorType, - 0, + 4, flatBufferBuilder.CreateString("output"), quantizationParameters); diff --git a/delegate/src/test/CastTestHelper.hpp b/delegate/src/test/CastTestHelper.hpp index 6b1d5ee947..0448e65856 100644 --- a/delegate/src/test/CastTestHelper.hpp +++ b/delegate/src/test/CastTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -30,7 +30,9 @@ std::vector<char> CreateCastTfLiteModel(tflite::TensorType inputTensorType, flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, @@ -44,14 +46,14 @@ std::vector<char> CreateCastTfLiteModel(tflite::TensorType inputTensorType, flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()), inputTensorType, - 0, + 1, flatBufferBuilder.CreateString("input"), quantizationParameters); tensors[1] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()), outputTensorType, - 0, + 2, flatBufferBuilder.CreateString("output"), quantizationParameters); diff --git a/delegate/src/test/ComparisonTestHelper.hpp b/delegate/src/test/ComparisonTestHelper.hpp index c9ccb778ac..db337f9f8a 100644 --- a/delegate/src/test/ComparisonTestHelper.hpp +++ b/delegate/src/test/ComparisonTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -33,7 +33,10 @@ std::vector<char> CreateComparisonTfLiteModel(tflite::BuiltinOperator comparison flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, @@ -47,21 +50,21 @@ std::vector<char> CreateComparisonTfLiteModel(tflite::BuiltinOperator comparison flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(), input0TensorShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input_0"), quantizationParameters); tensors[1] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(), input1TensorShape.size()), tensorType, - 0, + 2, flatBufferBuilder.CreateString("input_1"), quantizationParameters); tensors[2] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), outputTensorShape.size()), ::tflite::TensorType_BOOL, - 0); + 3); // create operator tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_EqualOptions;; diff --git a/delegate/src/test/ControlTestHelper.hpp b/delegate/src/test/ControlTestHelper.hpp index 0c9796170d..3e427e60c5 100644 --- a/delegate/src/test/ControlTestHelper.hpp +++ b/delegate/src/test/ControlTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -36,7 +36,9 @@ std::vector<char> CreateConcatTfLiteModel(tflite::BuiltinOperator controlOperato flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, @@ -57,7 +59,7 @@ std::vector<char> CreateConcatTfLiteModel(tflite::BuiltinOperator controlOperato flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input" + std::to_string(i)), quantizationParameters); @@ -71,7 +73,7 @@ std::vector<char> CreateConcatTfLiteModel(tflite::BuiltinOperator controlOperato flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), outputTensorShape.size()), tensorType, - 0, + 2, flatBufferBuilder.CreateString("output"), quantizationParameters); @@ -126,7 +128,7 @@ std::vector<char> CreateMeanTfLiteModel(tflite::BuiltinOperator controlOperatorC flatbuffers::FlatBufferBuilder flatBufferBuilder; std::array<flatbuffers::Offset<tflite::Buffer>, 2> buffers; - buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); + buffers[0] = CreateBuffer(flatBufferBuilder); buffers[1] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()), sizeof(int32_t) * axisData.size())); diff --git a/delegate/src/test/ConvolutionTestHelper.hpp b/delegate/src/test/ConvolutionTestHelper.hpp index ce1f951d21..70c1da6dce 100644 --- a/delegate/src/test/ConvolutionTestHelper.hpp +++ b/delegate/src/test/ConvolutionTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -50,15 +50,17 @@ std::vector<char> CreateConv2dTfLiteModel(tflite::BuiltinOperator convolutionOpe using namespace tflite; flatbuffers::FlatBufferBuilder flatBufferBuilder; - std::array<flatbuffers::Offset<tflite::Buffer>, 3> buffers; - buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); - buffers[1] = CreateBuffer(flatBufferBuilder, + std::array<flatbuffers::Offset<tflite::Buffer>, 5> buffers; + buffers[0] = CreateBuffer(flatBufferBuilder); + buffers[1] = CreateBuffer(flatBufferBuilder); + buffers[2] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(filterData.data()), sizeof(T) * filterData.size())); - buffers[2] = CreateBuffer(flatBufferBuilder, + buffers[3] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()), sizeof(B) * biasData.size())); + buffers[4] = CreateBuffer(flatBufferBuilder); auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, @@ -95,14 +97,14 @@ std::vector<char> CreateConv2dTfLiteModel(tflite::BuiltinOperator convolutionOpe flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input"), quantizationParameters); tensors[1] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(filterTensorShape.data(), filterTensorShape.size()), tensorType, - 1, + 2, flatBufferBuilder.CreateString("filter"), filterQuantizationParameters); @@ -114,14 +116,14 @@ std::vector<char> CreateConv2dTfLiteModel(tflite::BuiltinOperator convolutionOpe tensors[2] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(biasTensorShape.data(), biasTensorShape.size()), biasTensorType, - 2, + 3, flatBufferBuilder.CreateString("bias"), biasQuantizationParameters); tensors[3] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), outputTensorShape.size()), tensorType, - 0, + 4, flatBufferBuilder.CreateString("output"), outputQuantizationParameters); @@ -334,7 +336,7 @@ std::vector<char> CreateConv3dTfLiteModel(tflite::BuiltinOperator convolutionOpe flatbuffers::FlatBufferBuilder flatBufferBuilder; std::array<flatbuffers::Offset<tflite::Buffer>, 3> buffers; - buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); + buffers[0] = CreateBuffer(flatBufferBuilder); buffers[1] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(filterData.data()), sizeof(T) * filterData.size())); @@ -581,7 +583,7 @@ std::vector<char> CreateTransposeConvTfLiteModel(tflite::TensorType tensorType, flatbuffers::FlatBufferBuilder flatBufferBuilder; std::array<flatbuffers::Offset<tflite::Buffer>, 3> buffers; - buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); + buffers[0] = CreateBuffer(flatBufferBuilder); buffers[1] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(transposeData.data()), sizeof(int32_t) * transposeData.size())); diff --git a/delegate/src/test/DelegateOptionsTestHelper.hpp b/delegate/src/test/DelegateOptionsTestHelper.hpp index 7e147de31f..00a3d95904 100644 --- a/delegate/src/test/DelegateOptionsTestHelper.hpp +++ b/delegate/src/test/DelegateOptionsTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -44,7 +44,12 @@ std::vector<char> CreateAddDivTfLiteModel(tflite::TensorType tensorType, flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, @@ -59,35 +64,35 @@ std::vector<char> CreateAddDivTfLiteModel(tflite::TensorType tensorType, flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input_0"), quantizationParameters); tensors[1] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()), tensorType, - 0, + 2, flatBufferBuilder.CreateString("input_1"), quantizationParameters); tensors[2] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()), tensorType, - 0, + 3, flatBufferBuilder.CreateString("input_2"), quantizationParameters); tensors[3] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()), tensorType, - 0, + 4, flatBufferBuilder.CreateString("add"), quantizationParameters); tensors[4] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()), tensorType, - 0, + 5, flatBufferBuilder.CreateString("output"), quantizationParameters); @@ -157,7 +162,7 @@ std::vector<char> CreateCeilTfLiteModel(tflite::TensorType tensorType, flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, diff --git a/delegate/src/test/ElementwiseBinaryTestHelper.hpp b/delegate/src/test/ElementwiseBinaryTestHelper.hpp index 69b0c88dc8..09a715e7f1 100644 --- a/delegate/src/test/ElementwiseBinaryTestHelper.hpp +++ b/delegate/src/test/ElementwiseBinaryTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -37,7 +37,8 @@ std::vector<char> CreateElementwiseBinaryTfLiteModel(tflite::BuiltinOperator bin flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); if (constantInput) { buffers.push_back( @@ -47,9 +48,9 @@ std::vector<char> CreateElementwiseBinaryTfLiteModel(tflite::BuiltinOperator bin } else { - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); } - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, @@ -64,21 +65,21 @@ std::vector<char> CreateElementwiseBinaryTfLiteModel(tflite::BuiltinOperator bin flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(), input0TensorShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input_0"), quantizationParameters); tensors[1] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(), input1TensorShape.size()), tensorType, - 1, + 2, flatBufferBuilder.CreateString("input_1"), quantizationParameters); tensors[2] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), outputTensorShape.size()), tensorType, - 2, + 3, flatBufferBuilder.CreateString("output"), quantizationParameters); diff --git a/delegate/src/test/ElementwiseUnaryTestHelper.hpp b/delegate/src/test/ElementwiseUnaryTestHelper.hpp index dcc7074753..230d0fcca5 100644 --- a/delegate/src/test/ElementwiseUnaryTestHelper.hpp +++ b/delegate/src/test/ElementwiseUnaryTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -29,7 +29,7 @@ std::vector<char> CreateElementwiseUnaryTfLiteModel(tflite::BuiltinOperator unar flatbuffers::FlatBufferBuilder flatBufferBuilder; std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers; - buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); + buffers[0] = CreateBuffer(flatBufferBuilder); std::array<flatbuffers::Offset<Tensor>, 2> tensors; tensors[0] = CreateTensor(flatBufferBuilder, diff --git a/delegate/src/test/FillTestHelper.hpp b/delegate/src/test/FillTestHelper.hpp index e6890a2b2d..8479b72730 100644 --- a/delegate/src/test/FillTestHelper.hpp +++ b/delegate/src/test/FillTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -32,9 +32,7 @@ std::vector<char> CreateFillTfLiteModel(tflite::BuiltinOperator fillOperatorCode flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); buffers.push_back( CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(tensorShape.data()), @@ -43,6 +41,7 @@ std::vector<char> CreateFillTfLiteModel(tflite::BuiltinOperator fillOperatorCode CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(fillValue.data()), sizeof(T) * fillValue.size()))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); std::array<flatbuffers::Offset<Tensor>, 3> tensors; tensors[0] = CreateTensor(flatBufferBuilder, @@ -64,7 +63,7 @@ std::vector<char> CreateFillTfLiteModel(tflite::BuiltinOperator fillOperatorCode flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()), tensorType, - 0, + 3, flatBufferBuilder.CreateString("output")); tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_FillOptions; diff --git a/delegate/src/test/FullyConnectedTestHelper.hpp b/delegate/src/test/FullyConnectedTestHelper.hpp index 37062c3400..a3f009a863 100644 --- a/delegate/src/test/FullyConnectedTestHelper.hpp +++ b/delegate/src/test/FullyConnectedTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -37,8 +37,9 @@ std::vector<char> CreateFullyConnectedTfLiteModel(tflite::TensorType tensorType, { using namespace tflite; flatbuffers::FlatBufferBuilder flatBufferBuilder; - std::array<flatbuffers::Offset<tflite::Buffer>, 3> buffers; - buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); + std::array<flatbuffers::Offset<tflite::Buffer>, 5> buffers; + buffers[0] = CreateBuffer(flatBufferBuilder); + buffers[1] = CreateBuffer(flatBufferBuilder); auto biasTensorType = ::tflite::TensorType_FLOAT32; if (tensorType == ::tflite::TensorType_INT8) @@ -47,14 +48,14 @@ std::vector<char> CreateFullyConnectedTfLiteModel(tflite::TensorType tensorType, } if (constantWeights) { - buffers[1] = CreateBuffer(flatBufferBuilder, + buffers[2] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(weightsData.data()), sizeof(T) * weightsData.size())); if (tensorType == ::tflite::TensorType_INT8) { std::vector<int32_t> biasData = { 10 }; - buffers[2] = CreateBuffer(flatBufferBuilder, + buffers[3] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()), sizeof(int32_t) * biasData.size())); @@ -62,16 +63,17 @@ std::vector<char> CreateFullyConnectedTfLiteModel(tflite::TensorType tensorType, else { std::vector<float> biasData = { 10 }; - buffers[2] = CreateBuffer(flatBufferBuilder, + buffers[3] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()), sizeof(float) * biasData.size())); } } else { - buffers[1] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); - buffers[2] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); + buffers[2] = CreateBuffer(flatBufferBuilder); + buffers[3] = CreateBuffer(flatBufferBuilder); } + buffers[4] = CreateBuffer(flatBufferBuilder); auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, @@ -92,21 +94,21 @@ std::vector<char> CreateFullyConnectedTfLiteModel(tflite::TensorType tensorType, flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input_0"), quantizationParameters); tensors[1] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(weightsTensorShape.data(), weightsTensorShape.size()), tensorType, - 1, + 2, flatBufferBuilder.CreateString("weights"), quantizationParameters); tensors[2] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(biasTensorShape.data(), biasTensorShape.size()), biasTensorType, - 2, + 3, flatBufferBuilder.CreateString("bias"), quantizationParameters); @@ -114,7 +116,7 @@ std::vector<char> CreateFullyConnectedTfLiteModel(tflite::TensorType tensorType, flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), outputTensorShape.size()), tensorType, - 0, + 4, flatBufferBuilder.CreateString("output"), outputQuantizationParameters); diff --git a/delegate/src/test/GatherNdTestHelper.hpp b/delegate/src/test/GatherNdTestHelper.hpp index f475584dc5..c2cf9ffe9d 100644 --- a/delegate/src/test/GatherNdTestHelper.hpp +++ b/delegate/src/test/GatherNdTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -32,7 +32,10 @@ std::vector<char> CreateGatherNdTfLiteModel(tflite::TensorType tensorType, flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, @@ -46,21 +49,21 @@ std::vector<char> CreateGatherNdTfLiteModel(tflite::TensorType tensorType, flatBufferBuilder.CreateVector<int32_t>(paramsShape.data(), paramsShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("params"), quantizationParameters); tensors[1] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(indicesShape.data(), indicesShape.size()), ::tflite::TensorType_INT32, - 0, + 2, flatBufferBuilder.CreateString("indices"), quantizationParameters); tensors[2] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(expectedOutputShape.data(), expectedOutputShape.size()), tensorType, - 0, + 3, flatBufferBuilder.CreateString("output"), quantizationParameters); diff --git a/delegate/src/test/GatherTestHelper.hpp b/delegate/src/test/GatherTestHelper.hpp index fcacf04134..4763e06c73 100644 --- a/delegate/src/test/GatherTestHelper.hpp +++ b/delegate/src/test/GatherTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -33,7 +33,10 @@ std::vector<char> CreateGatherTfLiteModel(tflite::TensorType tensorType, flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, @@ -47,21 +50,21 @@ std::vector<char> CreateGatherTfLiteModel(tflite::TensorType tensorType, flatBufferBuilder.CreateVector<int32_t>(paramsShape.data(), paramsShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("params"), quantizationParameters); tensors[1] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(indicesShape.data(), indicesShape.size()), ::tflite::TensorType_INT32, - 0, + 2, flatBufferBuilder.CreateString("indices"), quantizationParameters); tensors[2] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(expectedOutputShape.data(), expectedOutputShape.size()), tensorType, - 0, + 3, flatBufferBuilder.CreateString("output"), quantizationParameters); diff --git a/delegate/src/test/LogicalTestHelper.hpp b/delegate/src/test/LogicalTestHelper.hpp index d08a1af388..2a1ff2b996 100644 --- a/delegate/src/test/LogicalTestHelper.hpp +++ b/delegate/src/test/LogicalTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -33,7 +33,10 @@ std::vector<char> CreateLogicalBinaryTfLiteModel(tflite::BuiltinOperator logical flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, @@ -48,21 +51,21 @@ std::vector<char> CreateLogicalBinaryTfLiteModel(tflite::BuiltinOperator logical flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(), input0TensorShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input_0"), quantizationParameters); tensors[1] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(), input1TensorShape.size()), tensorType, - 0, + 2, flatBufferBuilder.CreateString("input_1"), quantizationParameters); tensors[2] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), outputTensorShape.size()), tensorType, - 0, + 3, flatBufferBuilder.CreateString("output"), quantizationParameters); diff --git a/delegate/src/test/LstmTestHelper.hpp b/delegate/src/test/LstmTestHelper.hpp index 36a606119a..082d5dea91 100644 --- a/delegate/src/test/LstmTestHelper.hpp +++ b/delegate/src/test/LstmTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -101,7 +101,7 @@ std::vector<char> CreateLstmTfLiteModel(tflite::TensorType tensorType, flatBufferBuilder.CreateVector<float>({ outputQuantScale }), flatBufferBuilder.CreateVector<int64_t>({ outputQuantOffset })); - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(inputShape.data(), inputShape.size()), @@ -388,7 +388,7 @@ std::vector<char> CreateLstmTfLiteModel(tflite::TensorType tensorType, operatorInputs.push_back(kTfLiteOptionalTensor); } - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(outputStateInDimensions.data(), outputStateInDimensions.size()), @@ -399,7 +399,7 @@ std::vector<char> CreateLstmTfLiteModel(tflite::TensorType tensorType, true)); operatorInputs.push_back(buffers.size() - 1); - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(cellStateInDimensions.data(), cellStateInDimensions.size()), @@ -493,7 +493,7 @@ std::vector<char> CreateLstmTfLiteModel(tflite::TensorType tensorType, operatorInputs.push_back(kTfLiteOptionalTensor); } int outputBufferId = buffers.size(); - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(outputShape.data(), outputShape.size()), diff --git a/delegate/src/test/NormalizationTestHelper.hpp b/delegate/src/test/NormalizationTestHelper.hpp index ebdfdc1a25..510b578c02 100644 --- a/delegate/src/test/NormalizationTestHelper.hpp +++ b/delegate/src/test/NormalizationTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -46,7 +46,7 @@ std::vector<char> CreateNormalizationTfLiteModel(tflite::BuiltinOperator normali flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input"), quantizationParameters); @@ -54,15 +54,16 @@ std::vector<char> CreateNormalizationTfLiteModel(tflite::BuiltinOperator normali flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), outputTensorShape.size()), tensorType, - 1, + 2, flatBufferBuilder.CreateString("output"), quantizationParameters); std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, outputTensor }; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); std::vector<int32_t> operatorInputs = { 0 }; std::vector<int> subgraphInputs = { 0 }; diff --git a/delegate/src/test/PackTestHelper.hpp b/delegate/src/test/PackTestHelper.hpp index 0869228326..a9e2ee17bc 100644 --- a/delegate/src/test/PackTestHelper.hpp +++ b/delegate/src/test/PackTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -36,7 +36,8 @@ std::vector<char> CreatePackTfLiteModel(tflite::BuiltinOperator packOperatorCode flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, @@ -57,7 +58,7 @@ std::vector<char> CreatePackTfLiteModel(tflite::BuiltinOperator packOperatorCode flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input" + std::to_string(i)), quantizationParameters); diff --git a/delegate/src/test/PadTestHelper.hpp b/delegate/src/test/PadTestHelper.hpp index 5b9a1bcc36..e96bc4bfe3 100644 --- a/delegate/src/test/PadTestHelper.hpp +++ b/delegate/src/test/PadTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -70,12 +70,12 @@ std::vector<char> CreatePadTfLiteModel( std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, paddingTensor, outputTensor}; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); buffers.push_back( CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(paddingDim.data()), sizeof(int32_t) * paddingDim.size()))); - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); std::vector<int32_t> operatorInputs; std::vector<int> subgraphInputs; diff --git a/delegate/src/test/Pooling2dTestHelper.hpp b/delegate/src/test/Pooling2dTestHelper.hpp index b5d36b0231..c7457dbb22 100644 --- a/delegate/src/test/Pooling2dTestHelper.hpp +++ b/delegate/src/test/Pooling2dTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -38,8 +38,9 @@ std::vector<char> CreatePooling2dTfLiteModel( using namespace tflite; flatbuffers::FlatBufferBuilder flatBufferBuilder; - std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + flatbuffers::Offset<tflite::Buffer> buffers[3] = {CreateBuffer(flatBufferBuilder), + CreateBuffer(flatBufferBuilder), + CreateBuffer(flatBufferBuilder)}; auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, @@ -48,22 +49,21 @@ std::vector<char> CreatePooling2dTfLiteModel( flatBufferBuilder.CreateVector<float>({ quantScale }), flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); - std::array<flatbuffers::Offset<Tensor>, 2> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), - inputTensorShape.size()), + flatbuffers::Offset<Tensor> tensors[2] { + CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(inputTensorShape), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input"), - quantizationParameters); + quantizationParameters), - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), + CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShape), tensorType, - 0, + 2, flatBufferBuilder.CreateString("output"), - quantizationParameters); + quantizationParameters) + }; // create operator tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_Pool2DOptions; @@ -80,18 +80,18 @@ std::vector<char> CreatePooling2dTfLiteModel( flatbuffers::Offset <Operator> poolingOperator = CreateOperator(flatBufferBuilder, 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorInputs), + flatBufferBuilder.CreateVector<int32_t>(operatorOutputs), operatorBuiltinOptionsType, operatorBuiltinOptions); - const std::vector<int> subgraphInputs{0}; - const std::vector<int> subgraphOutputs{1}; + const int subgraphInputs[1] = {0}; + const int subgraphOutputs[1] = {1}; flatbuffers::Offset <SubGraph> subgraph = CreateSubGraph(flatBufferBuilder, - flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), - flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), + flatBufferBuilder.CreateVector(tensors, 2), + flatBufferBuilder.CreateVector<int32_t>(subgraphInputs, 1), + flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs, 1), flatBufferBuilder.CreateVector(&poolingOperator, 1)); flatbuffers::Offset <flatbuffers::String> modelDescription = @@ -104,7 +104,7 @@ std::vector<char> CreatePooling2dTfLiteModel( flatBufferBuilder.CreateVector(&operatorCode, 1), flatBufferBuilder.CreateVector(&subgraph, 1), modelDescription, - flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); + flatBufferBuilder.CreateVector(buffers, 3)); flatBufferBuilder.Finish(flatbufferModel); diff --git a/delegate/src/test/Pooling3dTestHelper.hpp b/delegate/src/test/Pooling3dTestHelper.hpp index f5f5cc3809..47e00f7b7f 100644 --- a/delegate/src/test/Pooling3dTestHelper.hpp +++ b/delegate/src/test/Pooling3dTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -46,7 +46,10 @@ std::vector<char> CreatePooling3dTfLiteModel( flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, diff --git a/delegate/src/test/PreluTestHelper.hpp b/delegate/src/test/PreluTestHelper.hpp index b6c18ccdfb..b50c37763f 100644 --- a/delegate/src/test/PreluTestHelper.hpp +++ b/delegate/src/test/PreluTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -33,10 +33,12 @@ std::vector<char> CreatePreluTfLiteModel(tflite::BuiltinOperator preluOperatorCo flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); - + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector( reinterpret_cast<const uint8_t *>(alphaData.data()), sizeof(float) * alphaData.size()))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, @@ -49,7 +51,7 @@ std::vector<char> CreatePreluTfLiteModel(tflite::BuiltinOperator preluOperatorCo flatBufferBuilder.CreateVector<int32_t>(inputShape.data(), inputShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input"), quantizationParameters); @@ -57,7 +59,7 @@ std::vector<char> CreatePreluTfLiteModel(tflite::BuiltinOperator preluOperatorCo flatBufferBuilder.CreateVector<int32_t>(alphaShape.data(), alphaShape.size()), tensorType, - 1, + 2, flatBufferBuilder.CreateString("alpha"), quantizationParameters); @@ -65,7 +67,7 @@ std::vector<char> CreatePreluTfLiteModel(tflite::BuiltinOperator preluOperatorCo flatBufferBuilder.CreateVector<int32_t>(outputShape.data(), outputShape.size()), tensorType, - 0, + 3, flatBufferBuilder.CreateString("output"), quantizationParameters); diff --git a/delegate/src/test/QuantizationTestHelper.hpp b/delegate/src/test/QuantizationTestHelper.hpp index e4155040cd..a8b102271a 100644 --- a/delegate/src/test/QuantizationTestHelper.hpp +++ b/delegate/src/test/QuantizationTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -31,7 +31,10 @@ std::vector<char> CreateQuantizationTfLiteModel(tflite::BuiltinOperator quantiza flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, @@ -46,14 +49,14 @@ std::vector<char> CreateQuantizationTfLiteModel(tflite::BuiltinOperator quantiza flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()), inputTensorType, - 0, + 1, flatBufferBuilder.CreateString("input"), quantizationParameters); tensors[1] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), outputTensorShape.size()), outputTensorType, - 0, + 2, flatBufferBuilder.CreateString("output"), quantizationParameters); diff --git a/delegate/src/test/RedefineTestHelper.hpp b/delegate/src/test/RedefineTestHelper.hpp index 6f061572b4..7f811d56dd 100644 --- a/delegate/src/test/RedefineTestHelper.hpp +++ b/delegate/src/test/RedefineTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -22,44 +22,36 @@ namespace { std::vector<char> CreateRedefineTfLiteModel( - tflite::BuiltinOperator redefineOperatorCode, - tflite::TensorType tensorType, - const std::vector<int32_t>& inputTensorShape, - const std::vector<int32_t>& outputTensorShape, - const std::vector<int32_t>& targetShape, - bool useOption = true, - float quantScale = 1.0f, - int quantOffset = 0) + tflite::BuiltinOperator redefineOperatorCode, + tflite::TensorType tensorType, + const std::vector<int32_t>& inputTensorShape, + const std::vector<int32_t>& outputTensorShape, + const std::vector<int32_t>& targetShape, + bool useOption = true, + float quantScale = 1.0f, + int quantOffset = 0) { using namespace tflite; flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({ quantScale }), + flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); auto inputTensor = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input"), quantizationParameters); - auto outputTensor = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - std::vector<flatbuffers::Offset<Tensor>> tensors; std::vector<int32_t> operatorInputs; std::vector<int> subgraphInputs; @@ -67,25 +59,43 @@ std::vector<char> CreateRedefineTfLiteModel( if (useOption) { + buffers.push_back(CreateBuffer(flatBufferBuilder)); + auto outputTensor = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), + outputTensorShape.size()), + tensorType, + 2, + flatBufferBuilder.CreateString("output"), + quantizationParameters); tensors = { inputTensor, outputTensor}; operatorInputs = {0}; subgraphInputs = {0}; operatorBuiltinOptions = CreateReshapeOptions( - flatBufferBuilder, - flatBufferBuilder.CreateVector(targetShape.data(), targetShape.size())).Union(); + flatBufferBuilder, + flatBufferBuilder.CreateVector(targetShape.data(), targetShape.size())).Union(); } else { buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(targetShape.data()), - sizeof(int32_t) * targetShape.size()))); + CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(targetShape.data()), + sizeof(int32_t) * targetShape.size()))); int32_t size = static_cast<int32_t>(targetShape.size()); auto shapeTensor = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>( { size } ), tflite::TensorType_INT32, 2, flatBufferBuilder.CreateString("shape")); + + buffers.push_back(CreateBuffer(flatBufferBuilder)); + auto outputTensor = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), + outputTensorShape.size()), + tensorType, + 3, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + tensors = { inputTensor, outputTensor, shapeTensor }; operatorInputs = {0, 2}; subgraphInputs = {0, 2}; @@ -97,33 +107,33 @@ std::vector<char> CreateRedefineTfLiteModel( const std::vector<int32_t> operatorOutputs{1}; flatbuffers::Offset <Operator> redefineOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), - operatorBuiltinOptionsType, - operatorBuiltinOptions); + CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), + operatorBuiltinOptionsType, + operatorBuiltinOptions); const std::vector<int> subgraphOutputs{1}; flatbuffers::Offset <SubGraph> subgraph = - CreateSubGraph(flatBufferBuilder, - flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), - flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), - flatBufferBuilder.CreateVector(&redefineOperator, 1)); + CreateSubGraph(flatBufferBuilder, + flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), + flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), + flatBufferBuilder.CreateVector(&redefineOperator, 1)); flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Reshape Operator Model"); + flatBufferBuilder.CreateString("ArmnnDelegate: Reshape Operator Model"); flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, redefineOperatorCode); flatbuffers::Offset <Model> flatbufferModel = - CreateModel(flatBufferBuilder, - TFLITE_SCHEMA_VERSION, - flatBufferBuilder.CreateVector(&operatorCode, 1), - flatBufferBuilder.CreateVector(&subgraph, 1), - modelDescription, - flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); + CreateModel(flatBufferBuilder, + TFLITE_SCHEMA_VERSION, + flatBufferBuilder.CreateVector(&operatorCode, 1), + flatBufferBuilder.CreateVector(&subgraph, 1), + modelDescription, + flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); flatBufferBuilder.Finish(flatbufferModel); @@ -172,8 +182,8 @@ void RedefineTest(tflite::BuiltinOperator redefineOperatorCode, // Create the ArmNN Delegate armnnDelegate::DelegateOptions delegateOptions(backends); std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> - theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), - armnnDelegate::TfLiteArmnnDelegateDelete); + theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), + armnnDelegate::TfLiteArmnnDelegateDelete); CHECK(theArmnnDelegate != nullptr); // Modify armnnDelegateInterpreter to use armnnDelegate CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); diff --git a/delegate/src/test/ReduceTestHelper.hpp b/delegate/src/test/ReduceTestHelper.hpp index 5457adbd0f..f500736080 100644 --- a/delegate/src/test/ReduceTestHelper.hpp +++ b/delegate/src/test/ReduceTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -37,14 +37,17 @@ std::vector<char> CreateReduceTfLiteModel(tflite::BuiltinOperator reduceOperator using namespace tflite; flatbuffers::FlatBufferBuilder flatBufferBuilder; - std::array<flatbuffers::Offset<tflite::Buffer>, 2> buffers; - buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); - buffers[1] = CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()), - sizeof(int32_t) * axisData.size())); + flatbuffers::Offset<tflite::Buffer> buffers[4] = { + CreateBuffer(flatBufferBuilder), + CreateBuffer(flatBufferBuilder), + CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()), + sizeof(int32_t) * axisData.size())), + CreateBuffer(flatBufferBuilder) + }; flatbuffers::Offset<tflite::QuantizationParameters> quantizationParametersAxis - = CreateQuantizationParameters(flatBufferBuilder); + = CreateQuantizationParameters(flatBufferBuilder); flatbuffers::Offset<tflite::QuantizationParameters> quantizationParameters; @@ -81,7 +84,7 @@ std::vector<char> CreateReduceTfLiteModel(tflite::BuiltinOperator reduceOperator flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(), input0TensorShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input"), quantizationParameters); @@ -89,7 +92,7 @@ std::vector<char> CreateReduceTfLiteModel(tflite::BuiltinOperator reduceOperator flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(), input1TensorShape.size()), ::tflite::TensorType_INT32, - 1, + 2, flatBufferBuilder.CreateString("axis"), quantizationParametersAxis); @@ -98,7 +101,7 @@ std::vector<char> CreateReduceTfLiteModel(tflite::BuiltinOperator reduceOperator flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), outputTensorShape.size()), tensorType, - 0, + 3, flatBufferBuilder.CreateString("output"), quantizationParameters); @@ -135,7 +138,7 @@ std::vector<char> CreateReduceTfLiteModel(tflite::BuiltinOperator reduceOperator flatBufferBuilder.CreateVector(&operatorCode, 1), flatBufferBuilder.CreateVector(&subgraph, 1), modelDescription, - flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); + flatBufferBuilder.CreateVector(buffers, 4)); flatBufferBuilder.Finish(flatbufferModel); diff --git a/delegate/src/test/ResizeTestHelper.hpp b/delegate/src/test/ResizeTestHelper.hpp index 030b2a7a4a..6937a4ba43 100644 --- a/delegate/src/test/ResizeTestHelper.hpp +++ b/delegate/src/test/ResizeTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -32,31 +32,33 @@ std::vector<char> CreateResizeTfLiteModel(tflite::BuiltinOperator operatorCode, flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector( reinterpret_cast<const uint8_t*>(sizeTensorData.data()), sizeof(int32_t) * sizeTensorData.size()))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); std::array<flatbuffers::Offset<Tensor>, 3> tensors; tensors[0] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()), inputTensorType, - 0, + 1, flatBufferBuilder.CreateString("input_tensor")); tensors[1] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(sizeTensorShape.data(), sizeTensorShape.size()), TensorType_INT32, - 1, + 2, flatBufferBuilder.CreateString("size_input_tensor")); tensors[2] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), outputTensorShape.size()), inputTensorType, - 0, + 3, flatBufferBuilder.CreateString("output_tensor")); // Create Operator diff --git a/delegate/src/test/RoundTestHelper.hpp b/delegate/src/test/RoundTestHelper.hpp index 3a35ee0764..6638607dcf 100644 --- a/delegate/src/test/RoundTestHelper.hpp +++ b/delegate/src/test/RoundTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -30,7 +30,9 @@ std::vector<char> CreateRoundTfLiteModel(tflite::BuiltinOperator roundOperatorCo flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, @@ -44,14 +46,14 @@ std::vector<char> CreateRoundTfLiteModel(tflite::BuiltinOperator roundOperatorCo flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input"), quantizationParameters); tensors[1] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()), tensorType, - 0, + 2, flatBufferBuilder.CreateString("output"), quantizationParameters); diff --git a/delegate/src/test/ShapeTestHelper.hpp b/delegate/src/test/ShapeTestHelper.hpp index 854c5084aa..9b3d574e23 100644 --- a/delegate/src/test/ShapeTestHelper.hpp +++ b/delegate/src/test/ShapeTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -31,7 +31,9 @@ std::vector<char> CreateShapeTfLiteModel(tflite::TensorType inputTensorType, flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, @@ -45,14 +47,14 @@ std::vector<char> CreateShapeTfLiteModel(tflite::TensorType inputTensorType, flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()), inputTensorType, - 0, + 1, flatBufferBuilder.CreateString("input"), quantizationParameters); tensors[1] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), outputTensorShape.size()), outputTensorType, - 0, + 2, flatBufferBuilder.CreateString("output"), quantizationParameters); diff --git a/delegate/src/test/SliceTestHelper.hpp b/delegate/src/test/SliceTestHelper.hpp index 4a2537feec..94c076b4f7 100644 --- a/delegate/src/test/SliceTestHelper.hpp +++ b/delegate/src/test/SliceTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -35,39 +35,42 @@ std::vector<char> CreateSliceTfLiteModel(tflite::TensorType tensorType, using namespace tflite; flatbuffers::FlatBufferBuilder flatBufferBuilder; - std::array<flatbuffers::Offset<tflite::Buffer>, 3> buffers; - buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); - buffers[1] = CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(beginTensorData.data()), - sizeof(int32_t) * beginTensorData.size())); - buffers[2] = CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(sizeTensorData.data()), - sizeof(int32_t) * sizeTensorData.size())); + flatbuffers::Offset<tflite::Buffer> buffers[5] = { + CreateBuffer(flatBufferBuilder), + CreateBuffer(flatBufferBuilder), + CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(beginTensorData.data()), + sizeof(int32_t) * beginTensorData.size())), + CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(sizeTensorData.data()), + sizeof(int32_t) * sizeTensorData.size())), + CreateBuffer(flatBufferBuilder) + }; std::array<flatbuffers::Offset<Tensor>, 4> tensors; tensors[0] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input")); tensors[1] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(beginTensorShape.data(), beginTensorShape.size()), ::tflite::TensorType_INT32, - 1, + 2, flatBufferBuilder.CreateString("begin_tensor")); tensors[2] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(sizeTensorShape.data(), sizeTensorShape.size()), ::tflite::TensorType_INT32, - 2, + 3, flatBufferBuilder.CreateString("size_tensor")); tensors[3] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), outputTensorShape.size()), tensorType, - 0, + 4, flatBufferBuilder.CreateString("output")); @@ -105,7 +108,7 @@ std::vector<char> CreateSliceTfLiteModel(tflite::TensorType tensorType, flatBufferBuilder.CreateVector(&operatorCode, 1), flatBufferBuilder.CreateVector(&subgraph, 1), modelDescription, - flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); + flatBufferBuilder.CreateVector(buffers, 5)); flatBufferBuilder.Finish(flatbufferModel); diff --git a/delegate/src/test/SoftmaxTestHelper.hpp b/delegate/src/test/SoftmaxTestHelper.hpp index bd32c212e9..f3367f9d24 100644 --- a/delegate/src/test/SoftmaxTestHelper.hpp +++ b/delegate/src/test/SoftmaxTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -28,19 +28,21 @@ std::vector<char> CreateSoftmaxTfLiteModel(tflite::BuiltinOperator softmaxOperat flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); std::array<flatbuffers::Offset<Tensor>, 2> tensors; tensors[0] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()), tensorType, - 0); + 1); tensors[1] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()), tensorType, - 0); + 2); const std::vector<int32_t> operatorInputs({0}); const std::vector<int32_t> operatorOutputs({1}); diff --git a/delegate/src/test/SpaceDepthTestHelper.hpp b/delegate/src/test/SpaceDepthTestHelper.hpp index d9a783c6a7..737e199ef7 100644 --- a/delegate/src/test/SpaceDepthTestHelper.hpp +++ b/delegate/src/test/SpaceDepthTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -37,21 +37,23 @@ std::vector<char> CreateSpaceDepthTfLiteModel(tflite::BuiltinOperator spaceDepth flatBufferBuilder.CreateVector<int64_t>({ 0 })); std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); std::array<flatbuffers::Offset<Tensor>, 2> tensors; tensors[0] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input"), quantizationParameters); tensors[1] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), outputTensorShape.size()), tensorType, - 0, + 2, flatBufferBuilder.CreateString("output"), quantizationParameters); diff --git a/delegate/src/test/SplitTestHelper.hpp b/delegate/src/test/SplitTestHelper.hpp index 31fc7d5e46..3c5f50ffac 100644 --- a/delegate/src/test/SplitTestHelper.hpp +++ b/delegate/src/test/SplitTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -35,11 +35,12 @@ std::vector<char> CreateSplitTfLiteModel(tflite::TensorType tensorType, using namespace tflite; flatbuffers::FlatBufferBuilder flatBufferBuilder; - std::array<flatbuffers::Offset<tflite::Buffer>, 2> buffers; - buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); - buffers[1] = CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()), - sizeof(int32_t) * axisData.size())); + std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()), + sizeof(int32_t) * axisData.size()))); auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, @@ -53,27 +54,28 @@ std::vector<char> CreateSplitTfLiteModel(tflite::TensorType tensorType, flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(), axisTensorShape.size()), ::tflite::TensorType_INT32, - 1, + 2, flatBufferBuilder.CreateString("axis"), quantizationParameters); tensors[1] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input"), quantizationParameters); // Create output tensor for (unsigned int i = 0; i < outputTensorShapes.size(); ++i) { + buffers.push_back(CreateBuffer(flatBufferBuilder)); tensors[i + 2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShapes[i].data(), - outputTensorShapes[i].size()), - tensorType, - 0, - flatBufferBuilder.CreateString("output"), - quantizationParameters); + flatBufferBuilder.CreateVector<int32_t>(outputTensorShapes[i].data(), + outputTensorShapes[i].size()), + tensorType, + (i+3), + flatBufferBuilder.CreateString("output"), + quantizationParameters); } // create operator. Mean uses ReducerOptions. @@ -109,7 +111,7 @@ std::vector<char> CreateSplitTfLiteModel(tflite::TensorType tensorType, flatBufferBuilder.CreateVector(&operatorCode, 1), flatBufferBuilder.CreateVector(&subgraph, 1), modelDescription, - flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); + flatBufferBuilder.CreateVector(buffers)); flatBufferBuilder.Finish(flatbufferModel); @@ -144,21 +146,21 @@ void SplitTest(tflite::TensorType tensorType, // Create TfLite Interpreters std::unique_ptr<Interpreter> armnnDelegate; CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&armnnDelegate) == kTfLiteOk); + (&armnnDelegate) == kTfLiteOk); CHECK(armnnDelegate != nullptr); CHECK(armnnDelegate->AllocateTensors() == kTfLiteOk); std::unique_ptr<Interpreter> tfLiteDelegate; CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&tfLiteDelegate) == kTfLiteOk); + (&tfLiteDelegate) == kTfLiteOk); CHECK(tfLiteDelegate != nullptr); CHECK(tfLiteDelegate->AllocateTensors() == kTfLiteOk); // Create the ArmNN Delegate armnnDelegate::DelegateOptions delegateOptions(backends); std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> - theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), - armnnDelegate::TfLiteArmnnDelegateDelete); + theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), + armnnDelegate::TfLiteArmnnDelegateDelete); CHECK(theArmnnDelegate != nullptr); // Modify armnnDelegateInterpreter to use armnnDelegate @@ -210,11 +212,11 @@ std::vector<char> CreateSplitVTfLiteModel(tflite::TensorType tensorType, sizeof(int32_t) * axisData.size())); auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({ quantScale }), + flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); std::array<flatbuffers::Offset<Tensor>, 5> tensors; tensors[0] = CreateTensor(flatBufferBuilder, @@ -258,33 +260,33 @@ std::vector<char> CreateSplitVTfLiteModel(tflite::TensorType tensorType, const std::vector<int> operatorInputs{ {0, 1, 2} }; const std::vector<int> operatorOutputs{ {3, 4} }; flatbuffers::Offset <Operator> controlOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), - operatorBuiltinOptionsType, - operatorBuiltinOptions); + CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), + operatorBuiltinOptionsType, + operatorBuiltinOptions); const std::vector<int> subgraphInputs{ {0, 1, 2} }; const std::vector<int> subgraphOutputs{ {3, 4} }; flatbuffers::Offset <SubGraph> subgraph = - CreateSubGraph(flatBufferBuilder, - flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), - flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), - flatBufferBuilder.CreateVector(&controlOperator, 1)); + CreateSubGraph(flatBufferBuilder, + flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), + flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), + flatBufferBuilder.CreateVector(&controlOperator, 1)); flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: SPLIT_V Operator Model"); + flatBufferBuilder.CreateString("ArmnnDelegate: SPLIT_V Operator Model"); flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, BuiltinOperator_SPLIT_V); flatbuffers::Offset <Model> flatbufferModel = - CreateModel(flatBufferBuilder, - TFLITE_SCHEMA_VERSION, - flatBufferBuilder.CreateVector(&operatorCode, 1), - flatBufferBuilder.CreateVector(&subgraph, 1), - modelDescription, - flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); + CreateModel(flatBufferBuilder, + TFLITE_SCHEMA_VERSION, + flatBufferBuilder.CreateVector(&operatorCode, 1), + flatBufferBuilder.CreateVector(&subgraph, 1), + modelDescription, + flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); flatBufferBuilder.Finish(flatbufferModel); diff --git a/delegate/src/test/StridedSliceTestHelper.hpp b/delegate/src/test/StridedSliceTestHelper.hpp index 2bca4fdc35..ef944d7e7a 100644 --- a/delegate/src/test/StridedSliceTestHelper.hpp +++ b/delegate/src/test/StridedSliceTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -43,48 +43,51 @@ std::vector<char> CreateStridedSliceTfLiteModel(tflite::TensorType tensorType, using namespace tflite; flatbuffers::FlatBufferBuilder flatBufferBuilder; - std::array<flatbuffers::Offset<tflite::Buffer>, 4> buffers; - buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); - buffers[1] = CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(beginTensorData.data()), - sizeof(int32_t) * beginTensorData.size())); - buffers[2] = CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(endTensorData.data()), - sizeof(int32_t) * endTensorData.size())); - buffers[3] = CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(strideTensorData.data()), - sizeof(int32_t) * strideTensorData.size())); + flatbuffers::Offset<tflite::Buffer> buffers[6] = { + CreateBuffer(flatBufferBuilder), + CreateBuffer(flatBufferBuilder), + CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(beginTensorData.data()), + sizeof(int32_t) * beginTensorData.size())), + CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(endTensorData.data()), + sizeof(int32_t) * endTensorData.size())), + CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(strideTensorData.data()), + sizeof(int32_t) * strideTensorData.size())), + CreateBuffer(flatBufferBuilder) + }; std::array<flatbuffers::Offset<Tensor>, 5> tensors; tensors[0] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input")); tensors[1] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(beginTensorShape.data(), beginTensorShape.size()), ::tflite::TensorType_INT32, - 1, + 2, flatBufferBuilder.CreateString("begin_tensor")); tensors[2] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(endTensorShape.data(), endTensorShape.size()), ::tflite::TensorType_INT32, - 2, + 3, flatBufferBuilder.CreateString("end_tensor")); tensors[3] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(strideTensorShape.data(), strideTensorShape.size()), ::tflite::TensorType_INT32, - 3, + 4, flatBufferBuilder.CreateString("stride_tensor")); tensors[4] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), outputTensorShape.size()), tensorType, - 0, + 5, flatBufferBuilder.CreateString("output")); @@ -127,7 +130,7 @@ std::vector<char> CreateStridedSliceTfLiteModel(tflite::TensorType tensorType, flatBufferBuilder.CreateVector(&operatorCode, 1), flatBufferBuilder.CreateVector(&subgraph, 1), modelDescription, - flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); + flatBufferBuilder.CreateVector(buffers, 6)); flatBufferBuilder.Finish(flatbufferModel); @@ -177,21 +180,21 @@ void StridedSliceTestImpl(std::vector<armnn::BackendId>& backends, // Create TfLite Interpreters std::unique_ptr<Interpreter> armnnDelegate; CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&armnnDelegate) == kTfLiteOk); + (&armnnDelegate) == kTfLiteOk); CHECK(armnnDelegate != nullptr); CHECK(armnnDelegate->AllocateTensors() == kTfLiteOk); std::unique_ptr<Interpreter> tfLiteDelegate; CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&tfLiteDelegate) == kTfLiteOk); + (&tfLiteDelegate) == kTfLiteOk); CHECK(tfLiteDelegate != nullptr); CHECK(tfLiteDelegate->AllocateTensors() == kTfLiteOk); // Create the ArmNN Delegate armnnDelegate::DelegateOptions delegateOptions(backends); std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> - theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), - armnnDelegate::TfLiteArmnnDelegateDelete); + theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), + armnnDelegate::TfLiteArmnnDelegateDelete); CHECK(theArmnnDelegate != nullptr); // Modify armnnDelegateInterpreter to use armnnDelegate diff --git a/delegate/src/test/TransposeTestHelper.hpp b/delegate/src/test/TransposeTestHelper.hpp index 1d55273b9f..4479c486cb 100644 --- a/delegate/src/test/TransposeTestHelper.hpp +++ b/delegate/src/test/TransposeTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -26,53 +26,56 @@ std::vector<char> CreateTransposeTfLiteModel(tflite::TensorType tensorType, { using namespace tflite; flatbuffers::FlatBufferBuilder flatBufferBuilder; - std::array<flatbuffers::Offset<tflite::Buffer>, 2> buffers; - buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); - buffers[1] = CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(inputPermVec.data()), - sizeof(int32_t) * inputPermVec.size())); + flatbuffers::Offset<tflite::Buffer> buffers[4]{ + CreateBuffer(flatBufferBuilder), + CreateBuffer(flatBufferBuilder), + CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(inputPermVec.data()), + sizeof(int32_t) * inputPermVec.size())), + CreateBuffer(flatBufferBuilder) + }; std::array<flatbuffers::Offset<Tensor>, 3> tensors; tensors[0] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(), input0TensorShape.size()), - tensorType, 0); + tensorType, 1); tensors[1] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(inputPermVecShape.data(), inputPermVecShape.size()), - tflite::TensorType_INT32, 1, + tflite::TensorType_INT32, 2, flatBufferBuilder.CreateString("permutation_vector")); tensors[2] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), outputTensorShape.size()), - tensorType); + tensorType,3); const std::vector<int32_t> operatorInputs{0, 1}; const std::vector<int32_t> operatorOutputs{2}; flatbuffers::Offset <Operator> transposeOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), - BuiltinOptions_TransposeOptions, - CreateTransposeOptions(flatBufferBuilder).Union()); + CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), + BuiltinOptions_TransposeOptions, + CreateTransposeOptions(flatBufferBuilder).Union()); const std::vector<int> subgraphInputs{0, 1}; const std::vector<int> subgraphOutputs{2}; flatbuffers::Offset <SubGraph> subgraph = - CreateSubGraph(flatBufferBuilder, - flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), - flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), - flatBufferBuilder.CreateVector(&transposeOperator, 1)); + CreateSubGraph(flatBufferBuilder, + flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), + flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), + flatBufferBuilder.CreateVector(&transposeOperator, 1)); flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Transpose Operator Model"); + flatBufferBuilder.CreateString("ArmnnDelegate: Transpose Operator Model"); flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_TRANSPOSE); flatbuffers::Offset <Model> flatbufferModel = - CreateModel(flatBufferBuilder, - TFLITE_SCHEMA_VERSION, - flatBufferBuilder.CreateVector(&operatorCode, 1), - flatBufferBuilder.CreateVector(&subgraph, 1), - modelDescription, - flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); + CreateModel(flatBufferBuilder, + TFLITE_SCHEMA_VERSION, + flatBufferBuilder.CreateVector(&operatorCode, 1), + flatBufferBuilder.CreateVector(&subgraph, 1), + modelDescription, + flatBufferBuilder.CreateVector(buffers, 4)); flatBufferBuilder.Finish(flatbufferModel); return std::vector<char>(flatBufferBuilder.GetBufferPointer(), flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); @@ -104,21 +107,21 @@ void TransposeFP32Test(std::vector<armnn::BackendId>& backends) // Create TfLite Interpreters std::unique_ptr<Interpreter> armnnDelegateInterpreter; CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&armnnDelegateInterpreter) == kTfLiteOk); + (&armnnDelegateInterpreter) == kTfLiteOk); CHECK(armnnDelegateInterpreter != nullptr); CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); std::unique_ptr<Interpreter> tfLiteInterpreter; CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&tfLiteInterpreter) == kTfLiteOk); + (&tfLiteInterpreter) == kTfLiteOk); CHECK(tfLiteInterpreter != nullptr); CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk); // Create the ArmNN Delegate armnnDelegate::DelegateOptions delegateOptions(backends); std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> - theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), - armnnDelegate::TfLiteArmnnDelegateDelete); + theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), + armnnDelegate::TfLiteArmnnDelegateDelete); CHECK(theArmnnDelegate != nullptr); // Modify armnnDelegateInterpreter to use armnnDelegate CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); diff --git a/delegate/src/test/UnidirectionalSequenceLstmTestHelper.hpp b/delegate/src/test/UnidirectionalSequenceLstmTestHelper.hpp index 9d6ef87e3f..10555aca1a 100644 --- a/delegate/src/test/UnidirectionalSequenceLstmTestHelper.hpp +++ b/delegate/src/test/UnidirectionalSequenceLstmTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -19,7 +19,6 @@ #include <doctest/doctest.h> - #include <armnn/utility/IgnoreUnused.hpp> #include <armnn/utility/NumericCast.hpp> #include <armnn/TypesUtils.hpp> @@ -33,7 +32,7 @@ namespace { -template <typename T> +template<typename T> std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType tensorType, int32_t batchSize, int32_t timeSize, @@ -78,7 +77,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType float clippingThresProj, bool isTimeMajor, float quantScale, - int quantOffset = 0) + int quantOffset = 0) { std::vector<int32_t> tensorInfo0{}; @@ -105,39 +104,41 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType std::vector<int> operatorInputs; using namespace tflite; - flatbuffers::FlatBufferBuilder flatBufferBuilder; + flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - std::vector<flatbuffers::Offset<Tensor>> tensors; + std::vector<flatbuffers::Offset<Tensor>> tensors; auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ 1.0f }), - flatBufferBuilder.CreateVector<int64_t>({ 0 })); + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({1.0f}), + flatBufferBuilder.CreateVector<int64_t>({0})); auto weightQuantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); - - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({quantScale}), + flatBufferBuilder.CreateVector<int64_t>({quantOffset})); + + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(inputShape.data(), inputShape.size()), ::tflite::TensorType_FLOAT32, buffers.size() - 1, flatBufferBuilder.CreateString("input_0"))); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); if (hasInputToInputWeights) { buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(inputToInputWeights.data()), - sizeof(T) * inputToInputWeights.size()))); + flatBufferBuilder.CreateVector( + reinterpret_cast<const uint8_t*>(inputToInputWeights.data()), + sizeof(T) * inputToInputWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoInputSize.data(), tensorInfoInputSize.size()), @@ -145,7 +146,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("inputToInputWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { @@ -154,8 +155,9 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(inputToForgetWeights.data()), - sizeof(T) * inputToForgetWeights.size()))); + flatBufferBuilder.CreateVector( + reinterpret_cast<const uint8_t*>(inputToForgetWeights.data()), + sizeof(T) * inputToForgetWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoInputSize.data(), tensorInfoInputSize.size()), @@ -163,12 +165,13 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("inputToForgetWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(inputToCellWeights.data()), - sizeof(T) * inputToCellWeights.size()))); + flatBufferBuilder.CreateVector( + reinterpret_cast<const uint8_t*>(inputToCellWeights.data()), + sizeof(T) * inputToCellWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoInputSize.data(), tensorInfoInputSize.size()), @@ -176,12 +179,13 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("inputToCellWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(inputToOutputWeights.data()), - sizeof(T) * inputToOutputWeights.size()))); + flatBufferBuilder.CreateVector( + reinterpret_cast<const uint8_t*>(inputToOutputWeights.data()), + sizeof(T) * inputToOutputWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoInputSize.data(), tensorInfoInputSize.size()), @@ -189,7 +193,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("inputToOutputWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); if (hasRecurrentToInputWeights) { @@ -204,7 +208,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("recurrentToInputWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { @@ -213,7 +217,8 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(recurrentToForgetWeights.data()), + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( + recurrentToForgetWeights.data()), sizeof(T) * recurrentToForgetWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoOutputSize.data(), @@ -222,11 +227,12 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("recurrentToForgetWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(recurrentToCellWeights.data()), + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( + recurrentToCellWeights.data()), sizeof(T) * recurrentToCellWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoOutputSize.data(), @@ -235,26 +241,28 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("recurrentToCellWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(recurrentToOutputWeights.data()), + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( + recurrentToOutputWeights.data()), sizeof(T) * recurrentToOutputWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoOutputSize.data(), tensorInfoOutputSize.size()), tensorType, - buffers.size() - 1 , + buffers.size() - 1, flatBufferBuilder.CreateString("recurrentToOutputWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); if (hasCellToInputWeights) { buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(cellToInputWeights.data()), + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( + cellToInputWeights.data()), sizeof(T) * cellToInputWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), @@ -263,7 +271,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("cellToInputWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { @@ -274,7 +282,8 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType { buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(cellToForgetWeights.data()), + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( + cellToForgetWeights.data()), sizeof(T) * cellToForgetWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), @@ -283,7 +292,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("cellToForgetWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { @@ -294,7 +303,8 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType { buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(cellToOutputWeights.data()), + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( + cellToOutputWeights.data()), sizeof(T) * cellToOutputWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), @@ -303,7 +313,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("cellToOutputWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { @@ -322,7 +332,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType ::tflite::TensorType_FLOAT32, buffers.size() - 1, flatBufferBuilder.CreateString("inputGateBias"))); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { @@ -331,7 +341,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(forgetGateBias.data()), + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(forgetGateBias.data()), sizeof(float) * forgetGateBias.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), @@ -339,11 +349,11 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType ::tflite::TensorType_FLOAT32, buffers.size() - 1, flatBufferBuilder.CreateString("forgetGateBias"))); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(cellBias.data()), + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(cellBias.data()), sizeof(float) * cellBias.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), @@ -351,11 +361,11 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType ::tflite::TensorType_FLOAT32, buffers.size() - 1, flatBufferBuilder.CreateString("cellBias"))); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(outputGateBias.data()), + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(outputGateBias.data()), sizeof(float) * outputGateBias.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), @@ -363,14 +373,15 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType ::tflite::TensorType_FLOAT32, buffers.size() - 1, flatBufferBuilder.CreateString("outputGateBias"))); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); if (hasProjectionWeights) { buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(projectionWeights.data()), - sizeof(T) * projectionWeights.size()))); + flatBufferBuilder.CreateVector( + reinterpret_cast<const uint8_t*>(projectionWeights.data()), + sizeof(T) * projectionWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(projectionWeightDimensions.data(), projectionWeightDimensions.size()), @@ -378,7 +389,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("projectionWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { @@ -389,22 +400,23 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType { buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(projectionBias.data()), - sizeof(float) * projectionBias.size()))); + flatBufferBuilder.CreateVector( + reinterpret_cast<const uint8_t*>(projectionBias.data()), + sizeof(float) * projectionBias.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(projectionBiasDimensions.data(), projectionBiasDimensions.size()), ::tflite::TensorType_FLOAT32, buffers.size() - 1, flatBufferBuilder.CreateString("projectionBias"))); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { operatorInputs.push_back(kTfLiteOptionalTensor); } - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(outputStateInDimensions.data(), outputStateInDimensions.size()), @@ -413,9 +425,9 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType flatBufferBuilder.CreateString("outputStateInInfo"), quantizationParameters, true)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(cellStateInDimensions.data(), cellStateInDimensions.size()), @@ -424,22 +436,22 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType flatBufferBuilder.CreateString("cellStateInInfo"), quantizationParameters, true)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); if (hasInputLayerNormWeights) { buffers.push_back( CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector( - reinterpret_cast<const uint8_t *>(inputLayerNormWeights.data()), - sizeof(float) * inputLayerNormWeights.size()))); + reinterpret_cast<const uint8_t*>(inputLayerNormWeights.data()), + sizeof(float) * inputLayerNormWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), tensorInfoNumUnits.size()), ::tflite::TensorType_FLOAT32, buffers.size() - 1, flatBufferBuilder.CreateString("inputLayerNormWeights"))); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { @@ -451,15 +463,15 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.push_back( CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector( - reinterpret_cast<const uint8_t *>(forgetLayerNormWeights.data()), - sizeof(float) * forgetLayerNormWeights.size()))); + reinterpret_cast<const uint8_t*>(forgetLayerNormWeights.data()), + sizeof(float) * forgetLayerNormWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), tensorInfoNumUnits.size()), ::tflite::TensorType_FLOAT32, buffers.size() - 1, flatBufferBuilder.CreateString("forgetLayerNormWeights"))); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { @@ -470,7 +482,8 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType { buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(cellLayerNormWeights.data()), + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( + cellLayerNormWeights.data()), sizeof(float) * cellLayerNormWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), @@ -478,7 +491,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType ::tflite::TensorType_FLOAT32, buffers.size() - 1, flatBufferBuilder.CreateString("cellLayerNormWeights"))); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { @@ -490,7 +503,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.push_back( CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector( - reinterpret_cast<const uint8_t *>(outputLayerNormWeights.data()), + reinterpret_cast<const uint8_t*>(outputLayerNormWeights.data()), sizeof(float) * outputLayerNormWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), @@ -498,58 +511,63 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType ::tflite::TensorType_FLOAT32, buffers.size() - 1, flatBufferBuilder.CreateString("outputLayerNormWeights"))); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { operatorInputs.push_back(kTfLiteOptionalTensor); } - int outputBufferId = buffers.size(); - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(outputShape.data(), outputShape.size()), ::tflite::TensorType_FLOAT32, - outputBufferId, + buffers.size() - 1, flatBufferBuilder.CreateString("output"))); std::vector<int> operatorOutputs; - operatorOutputs.push_back(buffers.size() - 1); + operatorOutputs.push_back(tensors.size() - 1); // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_UnidirectionalSequenceLSTMOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = - CreateUnidirectionalSequenceLSTMOptions(flatBufferBuilder, - activationFunction, - clippingThresCell, - clippingThresProj, - isTimeMajor).Union(); + tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_UnidirectionalSequenceLSTMOptions; + flatbuffers::Offset<void> operatorBuiltinOptions = + CreateUnidirectionalSequenceLSTMOptions(flatBufferBuilder, + activationFunction, + clippingThresCell, + clippingThresProj, + isTimeMajor).Union(); flatbuffers::Offset<Operator> lstmOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), - operatorBuiltinOptionsType, operatorBuiltinOptions); - - flatbuffers::Offset <SubGraph> subgraph = - CreateSubGraph(flatBufferBuilder, - flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), - flatBufferBuilder.CreateVector(&lstmOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: UnidirectionalSequenceLSTM Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = - CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM); - - flatbuffers::Offset <Model> flatbufferModel = - CreateModel(flatBufferBuilder, - TFLITE_SCHEMA_VERSION, - flatBufferBuilder.CreateVector(&operatorCode, 1), - flatBufferBuilder.CreateVector(&subgraph, 1), - modelDescription, - flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); + CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), + operatorInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), + operatorOutputs.size()), + operatorBuiltinOptionsType, operatorBuiltinOptions); + + flatbuffers::Offset<SubGraph> subgraph = + CreateSubGraph(flatBufferBuilder, + flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), + operatorInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), + operatorOutputs.size()), + flatBufferBuilder.CreateVector(&lstmOperator, 1)); + + flatbuffers::Offset<flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString( + "ArmnnDelegate: UnidirectionalSequenceLSTM Operator Model"); + flatbuffers::Offset<OperatorCode> operatorCode = + CreateOperatorCode(flatBufferBuilder, + tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM); + + flatbuffers::Offset<Model> flatbufferModel = + CreateModel(flatBufferBuilder, + TFLITE_SCHEMA_VERSION, + flatBufferBuilder.CreateVector(&operatorCode, 1), + flatBufferBuilder.CreateVector(&subgraph, 1), + modelDescription, + flatBufferBuilder.CreateVector(buffers)); flatBufferBuilder.Finish(flatbufferModel); @@ -557,7 +575,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); } -template <typename T> +template<typename T> void UnidirectionalSequenceLstmTestImpl(std::vector<armnn::BackendId>& backends, tflite::TensorType tensorType, int32_t batchSize, @@ -609,69 +627,69 @@ void UnidirectionalSequenceLstmTestImpl(std::vector<armnn::BackendId>& backends, using namespace tflite; std::vector<char> modelBuffer = CreateUnidirectionalSequenceLstmTfLiteModel(tensorType, - batchSize, - timeSize, - inputSize, - outputSize, - numUnits, - hasInputToInputWeights, - inputToInputWeights, - inputToForgetWeights, - inputToCellWeights, - inputToOutputWeights, - hasRecurrentToInputWeights, - recurrentToInputWeights, - recurrentToForgetWeights, - recurrentToCellWeights, - recurrentToOutputWeights, - hasCellToInputWeights, - cellToInputWeights, - hasCellToForgetWeights, - cellToForgetWeights, - hasCellToOutputWeights, - cellToOutputWeights, - hasInputGateBias, - inputGateBias, - forgetGateBias, - cellBias, - outputGateBias, - hasProjectionWeights, - projectionWeights, - hasProjectionBias, - projectionBias, - hasInputLayerNormWeights, - inputLayerNormWeights, - hasForgetLayerNormWeights, - forgetLayerNormWeights, - hasCellLayerNormWeights, - cellLayerNormWeights, - hasOutputLayerNormWeights, - outputLayerNormWeights, - activationFunction, - clippingThresCell, - clippingThresProj, - isTimeMajor, - quantScale); + batchSize, + timeSize, + inputSize, + outputSize, + numUnits, + hasInputToInputWeights, + inputToInputWeights, + inputToForgetWeights, + inputToCellWeights, + inputToOutputWeights, + hasRecurrentToInputWeights, + recurrentToInputWeights, + recurrentToForgetWeights, + recurrentToCellWeights, + recurrentToOutputWeights, + hasCellToInputWeights, + cellToInputWeights, + hasCellToForgetWeights, + cellToForgetWeights, + hasCellToOutputWeights, + cellToOutputWeights, + hasInputGateBias, + inputGateBias, + forgetGateBias, + cellBias, + outputGateBias, + hasProjectionWeights, + projectionWeights, + hasProjectionBias, + projectionBias, + hasInputLayerNormWeights, + inputLayerNormWeights, + hasForgetLayerNormWeights, + forgetLayerNormWeights, + hasCellLayerNormWeights, + cellLayerNormWeights, + hasOutputLayerNormWeights, + outputLayerNormWeights, + activationFunction, + clippingThresCell, + clippingThresProj, + isTimeMajor, + quantScale); const Model* tfLiteModel = GetModel(modelBuffer.data()); // Create TfLite Interpreters std::unique_ptr<Interpreter> armnnDelegateInterpreter; CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&armnnDelegateInterpreter) == kTfLiteOk); + (&armnnDelegateInterpreter) == kTfLiteOk); CHECK(armnnDelegateInterpreter != nullptr); CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); std::unique_ptr<Interpreter> tfLiteInterpreter; CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&tfLiteInterpreter) == kTfLiteOk); + (&tfLiteInterpreter) == kTfLiteOk); CHECK(tfLiteInterpreter != nullptr); CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk); // Create the ArmNN Delegate armnnDelegate::DelegateOptions delegateOptions(backends); std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> - theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), - armnnDelegate::TfLiteArmnnDelegateDelete); + theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), + armnnDelegate::TfLiteArmnnDelegateDelete); CHECK(theArmnnDelegate != nullptr); // Modify armnnDelegateInterpreter to use armnnDelegate CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); @@ -684,7 +702,7 @@ void UnidirectionalSequenceLstmTestImpl(std::vector<armnn::BackendId>& backends, tfLiteDelageInputData[i] = inputValues[i]; } - auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0]; + auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0]; auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInputId); for (unsigned int i = 0; i < inputValues.size(); ++i) { @@ -696,10 +714,10 @@ void UnidirectionalSequenceLstmTestImpl(std::vector<armnn::BackendId>& backends, CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); // Compare output data - auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; + auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; auto tfLiteDelagateOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId); - auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; - auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId); + auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; + auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId); if (tensorType == ::tflite::TensorType_INT8) { @@ -713,8 +731,10 @@ void UnidirectionalSequenceLstmTestImpl(std::vector<armnn::BackendId>& backends, } else { - armnnDelegate::CompareData(expectedOutputValues.data(), armnnDelegateOutputData, expectedOutputValues.size()); - armnnDelegate::CompareData(expectedOutputValues.data(), tfLiteDelagateOutputData, expectedOutputValues.size()); + armnnDelegate::CompareData(expectedOutputValues.data(), armnnDelegateOutputData, + expectedOutputValues.size()); + armnnDelegate::CompareData(expectedOutputValues.data(), tfLiteDelagateOutputData, + expectedOutputValues.size()); armnnDelegate::CompareData(tfLiteDelagateOutputData, armnnDelegateOutputData, expectedOutputValues.size()); } } diff --git a/delegate/src/test/UnpackTestHelper.hpp b/delegate/src/test/UnpackTestHelper.hpp index 848713498f..0e12d72279 100644 --- a/delegate/src/test/UnpackTestHelper.hpp +++ b/delegate/src/test/UnpackTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -36,7 +36,9 @@ std::vector<char> CreateUnpackTfLiteModel(tflite::BuiltinOperator unpackOperator flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); + auto quantizationParameters = CreateQuantizationParameters(flatBufferBuilder, @@ -57,7 +59,7 @@ std::vector<char> CreateUnpackTfLiteModel(tflite::BuiltinOperator unpackOperator flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()), tensorType, - 0, + 1, flatBufferBuilder.CreateString("input"), quantizationParameters); @@ -67,10 +69,11 @@ std::vector<char> CreateUnpackTfLiteModel(tflite::BuiltinOperator unpackOperator flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), outputTensorShape.size()), tensorType, - 0, + (i + 2), flatBufferBuilder.CreateString("output" + std::to_string(i)), quantizationParameters); + buffers.push_back(CreateBuffer(flatBufferBuilder)); operatorOutputs.push_back(i + 1); subgraphOutputs.push_back(i + 1); } @@ -105,7 +108,7 @@ std::vector<char> CreateUnpackTfLiteModel(tflite::BuiltinOperator unpackOperator flatBufferBuilder.CreateVector(&operatorCode, 1), flatBufferBuilder.CreateVector(&subgraph, 1), modelDescription, - flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); + flatBufferBuilder.CreateVector(buffers)); flatBufferBuilder.Finish(flatbufferModel); |