提交 db1747a5 编写于 作者: T tensor-tang

enable word2vec multi-threads ut

上级 9dd99395
...@@ -15,6 +15,8 @@ limitations under the License. */ ...@@ -15,6 +15,8 @@ limitations under the License. */
#include <glog/logging.h> #include <glog/logging.h>
#include <gtest/gtest.h> #include <gtest/gtest.h>
#include <thread>
#include "gflags/gflags.h" #include "gflags/gflags.h"
#include "paddle/contrib/inference/paddle_inference_api_impl.h" #include "paddle/contrib/inference/paddle_inference_api_impl.h"
#include "paddle/fluid/inference/tests/test_helper.h" #include "paddle/fluid/inference/tests/test_helper.h"
...@@ -45,7 +47,11 @@ NativeConfig GetConfig() { ...@@ -45,7 +47,11 @@ NativeConfig GetConfig() {
config.model_dir = FLAGS_dirname + "word2vec.inference.model"; config.model_dir = FLAGS_dirname + "word2vec.inference.model";
LOG(INFO) << "dirname " << config.model_dir; LOG(INFO) << "dirname " << config.model_dir;
config.fraction_of_gpu_memory = 0.15; config.fraction_of_gpu_memory = 0.15;
#ifdef PADDLE_WITH_CUDA
config.use_gpu = true; config.use_gpu = true;
#else
config.use_gpu = false;
#endif
config.device = 0; config.device = 0;
return config; return config;
} }
...@@ -149,4 +155,67 @@ TEST(paddle_inference_api_impl, image_classification) { ...@@ -149,4 +155,67 @@ TEST(paddle_inference_api_impl, image_classification) {
free(data); free(data);
} }
TEST(paddle_inference_api_native_multithreads, word2vec) {
NativeConfig config = GetConfig();
config.use_gpu = false;
auto main_predictor = CreatePaddlePredictor<NativeConfig>(config);
// prepare inputs data
constexpr int num_jobs = 3;
std::vector<std::vector<framework::LoDTensor>> jobs(num_jobs);
std::vector<std::vector<PaddleTensor>> paddle_tensor_feeds(num_jobs);
std::vector<framework::LoDTensor> refs(num_jobs);
for (size_t i = 0; i < jobs.size(); ++i) {
// each job has 4 words
jobs[i].resize(4);
for (size_t j = 0; j < 4; ++j) {
framework::LoD lod{{0, 1}};
int64_t dict_size = 2073; // The size of dictionary
SetupLoDTensor(&jobs[i][j], lod, static_cast<int64_t>(0), dict_size - 1);
paddle_tensor_feeds[i].push_back(LodTensorToPaddleTensor(&jobs[i][j]));
}
// get reference result of each job
std::vector<paddle::framework::LoDTensor*> ref_feeds;
std::vector<paddle::framework::LoDTensor*> ref_fetches(1, &refs[i]);
for (auto& word : jobs[i]) {
ref_feeds.push_back(&word);
}
TestInference<platform::CPUPlace>(config.model_dir, ref_feeds, ref_fetches);
}
// create threads and each thread run 1 job
std::vector<std::thread> threads;
for (int tid = 0; tid < num_jobs; ++tid) {
threads.emplace_back([&, tid]() {
auto predictor = main_predictor->Clone();
auto& local_inputs = paddle_tensor_feeds[tid];
std::vector<PaddleTensor> local_outputs;
ASSERT_TRUE(predictor->Run(local_inputs, &local_outputs));
// check outputs range
ASSERT_EQ(local_outputs.size(), 1UL);
const size_t len = local_outputs[0].data.length;
float* data = static_cast<float*>(local_outputs[0].data.data);
for (size_t j = 0; j < len / sizeof(float); ++j) {
ASSERT_LT(data[j], 1.0);
ASSERT_GT(data[j], -1.0);
}
// check outputs correctness
float* ref_data = refs[tid].data<float>();
EXPECT_EQ(refs[tid].numel(), len / sizeof(float));
for (int i = 0; i < refs[tid].numel(); ++i) {
EXPECT_LT(ref_data[i] - data[i], 1e-3);
EXPECT_GT(ref_data[i] - data[i], -1e-3);
}
free(local_outputs[0].data.data);
});
}
for (int i = 0; i < num_jobs; ++i) {
threads[i].join();
}
}
} // namespace paddle } // namespace paddle
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