Add Bind GPU bound Tensor (#213)
* Add feature to bind GPU tensors * Add test to check for invalid arg * Spacing * Make heap properties and resource desc more simple * Moved GPU upload tensor code under if statement
This commit is contained in:
Ryan Lai
GitHub
Родитель
ba5885c74e
Коммит
f2a21529f1
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@ -284,7 +284,6 @@ public: TEST_CLASS_INITIALIZE(SetupClass) {
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// We need to expect one more line because of the header
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Assert::AreEqual(static_cast<size_t>(2), GetOutputCSVLineCount());
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}
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TEST_METHOD(GarbageInputCpuWinMLDeviceCpuBoundRGBImage)
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{
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const std::wstring modelPath = CURRENT_PATH + L"SqueezeNet.onnx";
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@ -320,7 +319,6 @@ public: TEST_CLASS_INITIALIZE(SetupClass) {
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// We need to expect one more line because of the header
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Assert::AreEqual(static_cast<size_t>(2), GetOutputCSVLineCount());
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}
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TEST_METHOD(GarbageInputCpuWinMLDeviceGpuBoundRGBImage)
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{
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const std::wstring modelPath = CURRENT_PATH + L"SqueezeNet.onnx";
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@ -344,19 +342,15 @@ public: TEST_CLASS_INITIALIZE(SetupClass) {
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// We need to expect one more line because of the header
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Assert::AreEqual(static_cast<size_t>(2), GetOutputCSVLineCount());
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}
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TEST_METHOD(GarbageInputCpuWinMLDeviceGpuBoundTensor)
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{
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const std::wstring modelPath = CURRENT_PATH + L"SqueezeNet.onnx";
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const std::wstring command =
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BuildCommand({ EXE_PATH, L"-model", modelPath, L"-PerfOutput", OUTPUT_PATH, L"-perf", L"-CPU",
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L"-GPUBoundInput", L"-tensor", L"-CreateDeviceInWinML" });
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Assert::AreEqual(S_OK, RunProc((wchar_t*)command.c_str()));
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// We need to expect one more line because of the header
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Assert::AreEqual(static_cast<size_t>(2), GetOutputCSVLineCount());
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// Binding GPU Tensor with Session created with CPU device isn't supported.
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Assert::AreEqual(E_INVALIDARG, RunProc((wchar_t*)command.c_str()));
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}
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TEST_METHOD(GarbageInputGpuClientDeviceCpuBoundRGBImage)
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{
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const std::wstring modelPath = CURRENT_PATH + L"SqueezeNet.onnx";
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@ -646,7 +640,7 @@ public: TEST_CLASS_INITIALIZE(SetupClass) {
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const std::wstring command = BuildCommand({ EXE_PATH, L"-model", modelPath, L"-input", inputPath });
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Assert::AreEqual(HRESULT_FROM_WIN32(ERROR_INVALID_PARAMETER), RunProc((wchar_t *)command.c_str()));
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}
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TEST_METHOD(ProvidedCSVInputGPUSaveTensor)
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TEST_METHOD(ProvidedCSVInputGPUSaveCpuBoundTensor)
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{
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const std::wstring modelPath = CURRENT_PATH + L"SqueezeNet.onnx";
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const std::wstring inputPath = CURRENT_PATH + L"fish.csv";
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@ -656,7 +650,17 @@ public: TEST_CLASS_INITIALIZE(SetupClass) {
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Assert::AreEqual(true, CompareTensors(L"OutputTensorData\\Squeezenet_fish_input_GPU.csv",
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TENSOR_DATA_PATH + L"\\softmaxout_1GpuIteration1.csv"));
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}
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TEST_METHOD(ProvidedCSVInputCPUSaveTensor)
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TEST_METHOD(ProvidedCSVInputGPUSaveGpuBoundTensor)
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{
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const std::wstring modelPath = CURRENT_PATH + L"SqueezeNet.onnx";
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const std::wstring inputPath = CURRENT_PATH + L"fish.csv";
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const std::wstring command = BuildCommand({ EXE_PATH, L"-model", modelPath, L"-input", inputPath,
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L"-SaveTensorData", L"First", TENSOR_DATA_PATH, L"-GPU", L"-GPUBoundInput" });
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Assert::AreEqual(S_OK, RunProc((wchar_t*)command.c_str()));
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Assert::AreEqual(true, CompareTensors(L"OutputTensorData\\Squeezenet_fish_input_GPU.csv",
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TENSOR_DATA_PATH + L"\\softmaxout_1GpuIteration1.csv"));
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}
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TEST_METHOD(ProvidedCSVInputCPUSaveCpuBoundTensor)
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{
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const std::wstring modelPath = CURRENT_PATH + L"SqueezeNet.onnx";
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const std::wstring inputPath = CURRENT_PATH + L"fish.csv";
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@ -666,7 +670,7 @@ public: TEST_CLASS_INITIALIZE(SetupClass) {
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Assert::AreEqual(true, CompareTensors(L"OutputTensorData\\Squeezenet_fish_input_CPU.csv",
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TENSOR_DATA_PATH + L"\\softmaxout_1CpuIteration1.csv"));
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}
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TEST_METHOD(ProvidedCSVInputGPUSaveTensorFp16)
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TEST_METHOD(ProvidedCSVInputGPUSaveCpuBoundTensorFp16)
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{
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const std::wstring modelPath = CURRENT_PATH + L"SqueezeNet_fp16.onnx";
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const std::wstring inputPath = CURRENT_PATH + L"fish.csv";
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@ -676,7 +680,7 @@ public: TEST_CLASS_INITIALIZE(SetupClass) {
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Assert::AreEqual(true, CompareTensorsFP16(L"OutputTensorData\\Squeezenet_fp16_fish_input_GPU.csv",
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TENSOR_DATA_PATH + L"\\softmaxout_1GpuIteration1.csv"));
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}
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TEST_METHOD(ProvidedCSVInputCPUSaveTensorFp16)
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TEST_METHOD(ProvidedCSVInputCPUSaveCpuBoundTensorFp16)
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{
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const std::wstring modelPath = CURRENT_PATH + L"SqueezeNet_fp16.onnx";
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const std::wstring inputPath = CURRENT_PATH + L"fish.csv";
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@ -687,7 +691,7 @@ public: TEST_CLASS_INITIALIZE(SetupClass) {
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TENSOR_DATA_PATH + L"\\softmaxout_1CpuIteration1.csv"));
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}
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TEST_METHOD(ProvidedCSVInputOnlyGpuSaveTensorImageDenotation)
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TEST_METHOD(ProvidedCSVInputOnlyGpuSaveCpuBoundTensorImageDenotation)
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{
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const std::wstring modelPath = CURRENT_PATH + L"mnist.onnx";
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const std::wstring inputPath = CURRENT_PATH + L"mnist_28.csv";
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@ -697,7 +701,7 @@ public: TEST_CLASS_INITIALIZE(SetupClass) {
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Assert::AreEqual(true, CompareTensors(L"OutputTensorData\\Mnist_8_input_GPU.csv",
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TENSOR_DATA_PATH + L"\\Plus214_Output_0GpuIteration1.csv"));
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}
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TEST_METHOD(ProvidedCSVInputOnlyCpuSaveTensorImageDenotation)
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TEST_METHOD(ProvidedCSVInputOnlyCpuSaveCpuBoundTensorImageDenotation)
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{
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const std::wstring modelPath = CURRENT_PATH + L"mnist.onnx";
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const std::wstring inputPath = CURRENT_PATH + L"mnist_28.csv";
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@ -31,7 +31,6 @@
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<ClInclude Include="src/CommandLineArgs.h" />
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<ClInclude Include="src/Common.h" />
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<ClInclude Include="src/Filehelper.h" />
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<ClInclude Include="src/ModelBinding.h" />
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<ClInclude Include="src/OutputHelper.h" />
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<ClInclude Include="src/Run.h" />
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<ClInclude Include="src/TimerHelper.h" />
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@ -234,6 +233,7 @@
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<LanguageStandard>stdcpp17</LanguageStandard>
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<PrecompiledHeaderFile />
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<PrecompiledHeaderOutputFile />
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<AdditionalIncludeDirectories>..\..\Samples\CustomTensorization\CustomTensorization;%(AdditionalIncludeDirectories)</AdditionalIncludeDirectories>
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</ClCompile>
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<Link>
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<SubSystem>Windows</SubSystem>
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@ -254,6 +254,7 @@
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<PrecompiledHeaderFile />
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<PrecompiledHeaderOutputFile />
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<RuntimeLibrary>MultiThreaded</RuntimeLibrary>
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<AdditionalIncludeDirectories>..\..\Samples\CustomTensorization\CustomTensorization;%(AdditionalIncludeDirectories)</AdditionalIncludeDirectories>
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</ClCompile>
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<Link>
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<SubSystem>Windows</SubSystem>
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@ -36,9 +36,6 @@
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<ClInclude Include="src/TypeHelper.h">
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<Filter>Header Files</Filter>
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</ClInclude>
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<ClInclude Include="src/ModelBinding.h">
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<Filter>Source Files</Filter>
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</ClInclude>
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<ClInclude Include="src/Run.h">
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<Filter>Header Files</Filter>
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</ClInclude>
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@ -3,7 +3,7 @@
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#include <time.h>
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#include "Common.h"
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#include "Windows.AI.Machinelearning.Native.h"
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#include "d3dx12.h"
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using namespace winrt::Windows::Media;
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using namespace winrt::Windows::Storage;
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using namespace winrt::Windows::AI::MachineLearning;
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@ -327,7 +327,7 @@ namespace BindingUtilities
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template <TensorKind T>
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static ITensor CreateTensor(const CommandLineArgs& args, const std::vector<std::string>& tensorStringInput,
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const IVectorView<int64_t>& tensorShape)
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const IVectorView<int64_t>& tensorShape, const InputBindingType inputBindingType)
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{
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using TensorValue = typename TensorKindToValue<T>::Type;
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using DataType = typename TensorKindToType<T>::Type;
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@ -372,11 +372,106 @@ namespace BindingUtilities
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// Creating Tensors for Input Images haven't been added yet.
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throw hresult_not_implemented(L"Creating Tensors for Input Images haven't been implemented yet!");
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}
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return tensorValue;
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if (inputBindingType == InputBindingType::CPU)
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{
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return tensorValue;
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}
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else // GPU Tensor
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{
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com_ptr<ID3D12Resource> pGPUResource = nullptr;
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try
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{
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// create the d3d device.
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com_ptr<ID3D12Device> pD3D12Device = nullptr;
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D3D12CreateDevice(nullptr, D3D_FEATURE_LEVEL::D3D_FEATURE_LEVEL_11_0, __uuidof(ID3D12Device),
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reinterpret_cast<void**>(&pD3D12Device));
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pD3D12Device->CreateCommittedResource(
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&CD3DX12_HEAP_PROPERTIES(D3D12_HEAP_TYPE_DEFAULT),
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D3D12_HEAP_FLAG_NONE,
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&CD3DX12_RESOURCE_DESC::Buffer(
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actualSizeInBytes,
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D3D12_RESOURCE_FLAG_ALLOW_UNORDERED_ACCESS),
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D3D12_RESOURCE_STATE_COMMON, nullptr,
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__uuidof(ID3D12Resource), pGPUResource.put_void());
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if (!args.IsGarbageInput())
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{
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com_ptr<ID3D12Resource> imageUploadHeap;
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// Create the GPU upload buffer.
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pD3D12Device->CreateCommittedResource(
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&CD3DX12_HEAP_PROPERTIES(D3D12_HEAP_TYPE_UPLOAD), D3D12_HEAP_FLAG_NONE,
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&CD3DX12_RESOURCE_DESC::Buffer(actualSizeInBytes), D3D12_RESOURCE_STATE_GENERIC_READ, nullptr,
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__uuidof(ID3D12Resource), imageUploadHeap.put_void());
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// create the command queue.
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com_ptr<ID3D12CommandQueue> dxQueue = nullptr;
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D3D12_COMMAND_QUEUE_DESC commandQueueDesc = {};
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commandQueueDesc.Type = D3D12_COMMAND_LIST_TYPE_DIRECT;
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pD3D12Device->CreateCommandQueue(&commandQueueDesc, __uuidof(ID3D12CommandQueue),
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reinterpret_cast<void**>(&dxQueue));
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com_ptr<ILearningModelDeviceFactoryNative> devicefactory =
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get_activation_factory<LearningModelDevice, ILearningModelDeviceFactoryNative>();
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com_ptr<::IUnknown> spUnk;
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devicefactory->CreateFromD3D12CommandQueue(dxQueue.get(), spUnk.put());
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// Create ID3D12GraphicsCommandList and Allocator
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D3D12_COMMAND_LIST_TYPE queuetype = dxQueue->GetDesc().Type;
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com_ptr<ID3D12CommandAllocator> alloctor;
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com_ptr<ID3D12GraphicsCommandList> cmdList;
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pD3D12Device->CreateCommandAllocator(queuetype, winrt::guid_of<ID3D12CommandAllocator>(),
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alloctor.put_void());
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pD3D12Device->CreateCommandList(0, queuetype, alloctor.get(), nullptr,
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winrt::guid_of<ID3D12CommandList>(), cmdList.put_void());
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// Copy from Cpu to GPU
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D3D12_SUBRESOURCE_DATA CPUData = {};
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CPUData.pData = actualData;
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CPUData.RowPitch = actualSizeInBytes;
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CPUData.SlicePitch = actualSizeInBytes;
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UpdateSubresources(cmdList.get(), pGPUResource.get(), imageUploadHeap.get(), 0, 0, 1, &CPUData);
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// Close the command list and execute it to begin the initial GPU setup.
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cmdList->Close();
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ID3D12CommandList* ppCommandLists[] = { cmdList.get() };
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dxQueue->ExecuteCommandLists(_countof(ppCommandLists), ppCommandLists);
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// Create Event
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HANDLE directEvent = CreateEvent(nullptr, FALSE, FALSE, nullptr);
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// Create Fence
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Microsoft::WRL::ComPtr<ID3D12Fence> spDirectFence = nullptr;
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THROW_IF_FAILED(pD3D12Device->CreateFence(0, D3D12_FENCE_FLAG_NONE,
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IID_PPV_ARGS(spDirectFence.ReleaseAndGetAddressOf())));
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// Adds fence to queue
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THROW_IF_FAILED(dxQueue->Signal(spDirectFence.Get(), 1));
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THROW_IF_FAILED(spDirectFence->SetEventOnCompletion(1, directEvent));
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// Wait for signal
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DWORD retVal = WaitForSingleObject(directEvent, INFINITE);
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if (retVal != WAIT_OBJECT_0)
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{
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THROW_IF_FAILED(E_UNEXPECTED);
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}
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}
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}
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catch (...)
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{
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std::cout << "Couldn't create and copy CPU tensor resource to GPU resource" << std::endl;
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throw;
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}
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com_ptr<ITensorStaticsNative> tensorfactory = get_activation_factory<TensorValue, ITensorStaticsNative>();
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com_ptr<::IUnknown> spUnkTensor;
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tensorfactory->CreateFromD3D12Resource(pGPUResource.get(), vecShape.data(), static_cast<int>(vecShape.size()), spUnkTensor.put());
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TensorValue returnTensor(nullptr);
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spUnkTensor.try_as(returnTensor);
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return returnTensor;
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}
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}
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// Binds tensor floats, ints, doubles from CSV data.
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ITensor CreateBindableTensor(const ILearningModelFeatureDescriptor& description, const CommandLineArgs& args)
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ITensor CreateBindableTensor(const ILearningModelFeatureDescriptor& description, const CommandLineArgs& args,
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const InputBindingType inputBindingType)
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{
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std::vector<std::string> elementStrings;
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if (!args.CsvPath().empty())
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@ -407,7 +502,7 @@ namespace BindingUtilities
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std::vector<int64_t> shape = { 1, channels, imageFeatureDescriptor.Height(),
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imageFeatureDescriptor.Width() };
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IVectorView<int64_t> shapeVectorView = single_threaded_vector(std::move(shape)).GetView();
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return CreateTensor<TensorKind::Float>(args, elementStrings, shapeVectorView);
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return CreateTensor<TensorKind::Float>(args, elementStrings, shapeVectorView, inputBindingType);
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}
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auto tensorDescriptor = description.try_as<TensorFeatureDescriptor>();
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@ -422,57 +517,68 @@ namespace BindingUtilities
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}
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case TensorKind::Float:
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{
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return CreateTensor<TensorKind::Float>(args, elementStrings, tensorDescriptor.Shape());
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return CreateTensor<TensorKind::Float>(args, elementStrings, tensorDescriptor.Shape(),
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inputBindingType);
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}
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break;
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case TensorKind::Float16:
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{
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return CreateTensor<TensorKind::Float16>(args, elementStrings, tensorDescriptor.Shape());
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return CreateTensor<TensorKind::Float16>(args, elementStrings, tensorDescriptor.Shape(),
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inputBindingType);
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}
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break;
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case TensorKind::Double:
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{
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return CreateTensor<TensorKind::Double>(args, elementStrings, tensorDescriptor.Shape());
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return CreateTensor<TensorKind::Double>(args, elementStrings, tensorDescriptor.Shape(),
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inputBindingType);
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}
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break;
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case TensorKind::Int8:
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{
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return CreateTensor<TensorKind::Int8>(args, elementStrings, tensorDescriptor.Shape());
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return CreateTensor<TensorKind::Int8>(args, elementStrings, tensorDescriptor.Shape(),
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inputBindingType);
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}
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break;
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case TensorKind::UInt8:
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{
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return CreateTensor<TensorKind::UInt8>(args, elementStrings, tensorDescriptor.Shape());
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return CreateTensor<TensorKind::UInt8>(args, elementStrings, tensorDescriptor.Shape(),
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inputBindingType);
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}
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break;
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case TensorKind::Int16:
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{
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return CreateTensor<TensorKind::Int16>(args, elementStrings, tensorDescriptor.Shape());
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return CreateTensor<TensorKind::Int16>(args, elementStrings, tensorDescriptor.Shape(),
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inputBindingType);
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}
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break;
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case TensorKind::UInt16:
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{
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return CreateTensor<TensorKind::UInt16>(args, elementStrings, tensorDescriptor.Shape());
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return CreateTensor<TensorKind::UInt16>(args, elementStrings, tensorDescriptor.Shape(),
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inputBindingType);
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}
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break;
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case TensorKind::Int32:
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{
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return CreateTensor<TensorKind::Int32>(args, elementStrings, tensorDescriptor.Shape());
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return CreateTensor<TensorKind::Int32>(args, elementStrings, tensorDescriptor.Shape(),
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inputBindingType);
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}
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break;
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case TensorKind::UInt32:
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{
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return CreateTensor<TensorKind::UInt32>(args, elementStrings, tensorDescriptor.Shape());
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return CreateTensor<TensorKind::UInt32>(args, elementStrings, tensorDescriptor.Shape(),
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inputBindingType);
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}
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break;
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case TensorKind::Int64:
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{
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return CreateTensor<TensorKind::Int64>(args, elementStrings, tensorDescriptor.Shape());
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return CreateTensor<TensorKind::Int64>(args, elementStrings, tensorDescriptor.Shape(),
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inputBindingType);
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}
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break;
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case TensorKind::UInt64:
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{
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return CreateTensor<TensorKind::UInt64>(args, elementStrings, tensorDescriptor.Shape());
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return CreateTensor<TensorKind::UInt64>(args, elementStrings, tensorDescriptor.Shape(),
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inputBindingType);
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}
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break;
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}
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@ -20,10 +20,10 @@ std::vector<ILearningModelFeatureValue> GenerateInputFeatures(const LearningMode
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{
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auto&& description = model.InputFeatures().GetAt(i);
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if (inputDataType == InputDataType::Tensor || i > 0)
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if (inputDataType == InputDataType::Tensor)
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{
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// If CSV data is provided, then every input will contain the same CSV data
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auto tensorFeature = BindingUtilities::CreateBindableTensor(description, args);
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auto tensorFeature = BindingUtilities::CreateBindableTensor(description, args, inputBindingType);
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inputFeatures.push_back(tensorFeature);
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}
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else
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@ -214,6 +214,11 @@ HRESULT BindInputs(LearningModelBinding &context, const LearningModel& model, co
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const IDirect3DDevice& winrtDevice, DeviceCreationLocation deviceCreationLocation, uint32_t iteration,
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Profiler<WINML_MODEL_TEST_PERF>& profiler)
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{
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if (deviceType == DeviceType::CPU && inputDataType == InputDataType::Tensor && inputBindingType == InputBindingType::GPU)
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{
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std::cout << "Cannot create D3D12 device on client if CPU device type is selected." << std::endl;
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return E_INVALIDARG;
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}
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bool useInputData = false;
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bool isGarbageData = args.IsGarbageInput();
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std::string completionString = "\n";
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@ -315,15 +320,6 @@ HRESULT CheckIfModelAndConfigurationsAreSupported(LearningModel& model, const st
|
|||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Creating D3D12 device on client doesn't make sense for CPU deviceType
|
||||
if (deviceType == DeviceType::CPU && std::any_of(deviceCreationLocations.begin(), deviceCreationLocations.end(),
|
||||
[](const DeviceCreationLocation deviceCreationLocation) {
|
||||
return deviceCreationLocation == DeviceCreationLocation::UserD3DDevice; }))
|
||||
{
|
||||
std::cout << "Cannot create D3D12 device on client if CPU device type is selected." << std::endl;
|
||||
return E_INVALIDARG;
|
||||
}
|
||||
return S_OK;
|
||||
}
|
||||
|
||||
|
|
@ -508,10 +504,10 @@ int run(CommandLineArgs& args, Profiler<WINML_MODEL_TEST_PERF>& profiler) try
|
|||
for (auto deviceType : deviceTypes)
|
||||
{
|
||||
lastHr = CheckIfModelAndConfigurationsAreSupported(model, path, deviceType, inputDataTypes,
|
||||
deviceCreationLocations);
|
||||
deviceCreationLocations);
|
||||
if (FAILED(lastHr))
|
||||
{
|
||||
continue;
|
||||
continue;
|
||||
}
|
||||
for (auto deviceCreationLocation : deviceCreationLocations)
|
||||
{
|
||||
|
|
@ -550,7 +546,10 @@ int run(CommandLineArgs& args, Profiler<WINML_MODEL_TEST_PERF>& profiler) try
|
|||
LearningModelBinding context(session);
|
||||
lastHr = BindInputs(context, model, session, output, deviceType, args, inputBindingType,
|
||||
inputDataType, winrtDevice, deviceCreationLocation, i, profiler);
|
||||
|
||||
if (FAILED(lastHr))
|
||||
{
|
||||
break;
|
||||
}
|
||||
LearningModelEvaluationResult result = nullptr;
|
||||
bool capture_perf = args.IsPerformanceCapture() || args.IsPerIterationCapture();
|
||||
lastHr = EvaluateModel(result, model, context, session, args, output,
|
||||
|
|
|
|||
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Ссылка в новой задаче