mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-05-14 20:48:00 +00:00
a9ce941579
Description: Refine threading control options and move inter op thread pool to session state. Added thread_utils.h/cc to centralize the decision around the thread pool size under various conditions. Motivation and Context Currently the thread pool size of the parallel executor is hardcoded to 32 for some reason. This PR makes the options to configure the thread pool sizes clearer.
91 lines
3.1 KiB
C++
91 lines
3.1 KiB
C++
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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//
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#include "CppUnitTest.h"
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#include <assert.h>
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#include <onnxruntime_c_api.h>
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wchar_t* GetWideString(const char* c) {
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const size_t cSize = strlen(c) + 1;
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wchar_t* wc = new wchar_t[cSize];
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mbstowcs(wc, c, cSize);
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return wc;
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}
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#define ORT_ABORT_ON_ERROR(expr) \
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{ \
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OrtStatus* onnx_status = (expr); \
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if (onnx_status != NULL) { \
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const char* msg = OrtGetErrorMessage(onnx_status); \
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fprintf(stderr, "%s\n", msg); \
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OrtReleaseStatus(onnx_status); \
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wchar_t* wmsg = GetWideString(msg); \
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Assert::Fail(L"Failed on ORT_ABORT_ON_ERROR"); \
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free(wmsg); \
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} \
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}
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using namespace Microsoft::VisualStudio::CppUnitTestFramework;
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namespace UnitTest1 {
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TEST_CLASS(UnitTest1){
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public :
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int run_inference(OrtSession * session){
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size_t input_height = 224;
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size_t input_width = 224;
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float* model_input = (float*)malloc(sizeof(float) * 224 * 224 * 3);
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size_t model_input_ele_count = 224 * 224 * 3;
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// initialize to values between 0.0 and 1.0
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for (unsigned int i = 0; i < model_input_ele_count; i++)
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model_input[i] = (float)i / (float)(model_input_ele_count + 1);
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OrtMemoryInfo* memory_info;
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ORT_ABORT_ON_ERROR(OrtCreateCpuMemoryInfo(OrtArenaAllocator, OrtMemTypeDefault, &memory_info));
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const size_t input_shape[] = {1, 3, 224, 224};
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const size_t input_shape_len = sizeof(input_shape) / sizeof(input_shape[0]);
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const size_t model_input_len = model_input_ele_count * sizeof(float);
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OrtValue* input_tensor = NULL;
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ORT_ABORT_ON_ERROR(OrtCreateTensorWithDataAsOrtValue(memory_info, model_input, model_input_len, input_shape, input_shape_len, ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT, &input_tensor));
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assert(input_tensor != NULL);
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assert(OrtIsTensor(input_tensor));
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OrtReleaseMemoryInfo(memory_info);
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const char* input_names[] = {"data_0"};
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const char* output_names[] = {"softmaxout_1"};
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OrtValue* output_tensor = NULL;
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ORT_ABORT_ON_ERROR(OrtRun(session, NULL, input_names, (const OrtValue* const*)&input_tensor, 1, output_names, 1, &output_tensor));
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assert(output_tensor != NULL);
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assert(OrtIsTensor(output_tensor));
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OrtReleaseValue(output_tensor);
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OrtReleaseValue(input_tensor);
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free(model_input);
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return 0;
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} // namespace UnitTest1
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int test() {
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const wchar_t* model_path = L"squeezenet.onnx";
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OrtEnv* env;
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ORT_ABORT_ON_ERROR(OrtCreateEnv(ORT_LOGGING_LEVEL_WARNING, "test", &env));
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OrtSessionOptions* session_option = OrtCreateSessionOptions();
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OrtSession* session;
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OrtSetIntraOpNumThreads(session_option, 1);
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ORT_ABORT_ON_ERROR(OrtCreateSession(env, model_path, session_option, &session));
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OrtReleaseSessionOptions(session_option);
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int result = run_inference(session);
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OrtReleaseSession(session);
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OrtReleaseEnv(env);
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}
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TEST_METHOD(TestMethod1) {
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int res = test();
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Assert::AreEqual(res, 0);
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}
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}
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;
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}
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