You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
101 lines
3.4 KiB
101 lines
3.4 KiB
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
|
//
|
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
|
// you may not use this file except in compliance with the License.
|
|
// You may obtain a copy of the License at
|
|
//
|
|
// http://www.apache.org/licenses/LICENSE-2.0
|
|
//
|
|
// Unless required by applicable law or agreed to in writing, software
|
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
// See the License for the specific language governing permissions and
|
|
// limitations under the License.
|
|
|
|
#include "paddle/fluid/inference/analysis/analyzer.h"
|
|
|
|
#include <google/protobuf/text_format.h>
|
|
#include <gtest/gtest.h>
|
|
#include "paddle/fluid/inference/analysis/ut_helper.h"
|
|
#include "paddle/fluid/inference/api/paddle_inference_api.h"
|
|
#include "paddle/fluid/inference/api/paddle_inference_pass.h"
|
|
#include "paddle/fluid/platform/port.h"
|
|
|
|
namespace paddle {
|
|
namespace inference {
|
|
namespace analysis {
|
|
|
|
using namespace framework; // NOLINT
|
|
|
|
TEST(Analyzer, analysis_without_tensorrt) {
|
|
Argument argument;
|
|
argument.SetDisableLogs(false);
|
|
argument.SetModelDir(FLAGS_inference_model_dir);
|
|
argument.SetEnableAnalysisOptim(false);
|
|
argument.SetUseGPU(false);
|
|
argument.SetAnalysisPasses({"ir_graph_build_pass", "ir_analysis_pass",
|
|
"ir_params_sync_among_devices_pass"});
|
|
|
|
Analyzer analyser;
|
|
analyser.Run(&argument);
|
|
}
|
|
|
|
TEST(Analyzer, analysis_with_tensorrt) {
|
|
Argument argument;
|
|
argument.SetDisableLogs(false);
|
|
argument.SetEnableAnalysisOptim(false);
|
|
argument.SetTensorRtMaxBatchSize(3);
|
|
argument.SetTensorRtWorkspaceSize(1 << 20);
|
|
argument.SetModelDir(FLAGS_inference_model_dir);
|
|
argument.SetUseGPU(false);
|
|
argument.SetAnalysisPasses({"ir_graph_build_pass", "ir_analysis_pass",
|
|
"ir_params_sync_among_devices_pass"});
|
|
|
|
Analyzer analyser;
|
|
analyser.Run(&argument);
|
|
}
|
|
|
|
void TestWord2vecPrediction(const std::string& model_path) {
|
|
NativeConfig config;
|
|
config.model_dir = model_path;
|
|
config.use_gpu = false;
|
|
config.device = 0;
|
|
auto predictor = ::paddle::CreatePaddlePredictor<NativeConfig>(config);
|
|
|
|
// One single batch
|
|
|
|
int64_t data[4] = {1, 2, 3, 4};
|
|
PaddleTensor tensor;
|
|
tensor.shape = std::vector<int>({4, 1});
|
|
tensor.data = PaddleBuf(data, sizeof(data));
|
|
tensor.dtype = PaddleDType::INT64;
|
|
|
|
// For simplicity, we set all the slots with the same data.
|
|
std::vector<PaddleTensor> slots(4, tensor);
|
|
std::vector<PaddleTensor> outputs;
|
|
CHECK(predictor->Run(slots, &outputs));
|
|
|
|
PADDLE_ENFORCE_EQ(outputs.size(), 1UL);
|
|
// Check the output buffer size and result of each tid.
|
|
PADDLE_ENFORCE_EQ(outputs.front().data.length(), 33168UL);
|
|
float result[5] = {0.00129761, 0.00151112, 0.000423564, 0.00108815,
|
|
0.000932706};
|
|
const size_t num_elements = outputs.front().data.length() / sizeof(float);
|
|
// The outputs' buffers are in CPU memory.
|
|
for (size_t i = 0; i < std::min(static_cast<size_t>(5UL), num_elements);
|
|
i++) {
|
|
LOG(INFO) << "data: " << static_cast<float*>(outputs.front().data.data())[i]
|
|
<< " result: " << result[i];
|
|
EXPECT_NEAR(static_cast<float*>(outputs.front().data.data())[i], result[i],
|
|
1e-3);
|
|
}
|
|
}
|
|
|
|
TEST(Analyzer, word2vec_without_analysis) {
|
|
TestWord2vecPrediction(FLAGS_inference_model_dir);
|
|
}
|
|
|
|
} // namespace analysis
|
|
} // namespace inference
|
|
} // namespace paddle
|