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[Update Paddle inference]: update inference ut (PaddlePaddle#317)
* update inference ut * fix copyright
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paddle/fluid/inference/tests/api/ipu_ernie_fp16_test.cc
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| // Copyright (c) 2021 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. | ||
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| #include "paddle/fluid/inference/tests/api/tester_helper.h" | ||
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| namespace paddle { | ||
| namespace inference { | ||
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| using paddle::PaddleTensor; | ||
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| template <typename T> | ||
| void GetValueFromStream(std::stringstream *ss, T *t) { | ||
| (*ss) >> (*t); | ||
| } | ||
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| template <> | ||
| void GetValueFromStream<std::string>(std::stringstream *ss, std::string *t) { | ||
| *t = ss->str(); | ||
| } | ||
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| // Split string to vector | ||
| template <typename T> | ||
| void Split(const std::string &line, char sep, std::vector<T> *v) { | ||
| std::stringstream ss; | ||
| T t; | ||
| for (auto c : line) { | ||
| if (c != sep) { | ||
| ss << c; | ||
| } else { | ||
| GetValueFromStream<T>(&ss, &t); | ||
| v->push_back(std::move(t)); | ||
| ss.str({}); | ||
| ss.clear(); | ||
| } | ||
| } | ||
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| if (!ss.str().empty()) { | ||
| GetValueFromStream<T>(&ss, &t); | ||
| v->push_back(std::move(t)); | ||
| ss.str({}); | ||
| ss.clear(); | ||
| } | ||
| } | ||
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| // Parse tensor from string | ||
| template <typename T> | ||
| bool ParseTensor(const std::string &field, paddle::PaddleTensor *tensor) { | ||
| std::vector<std::string> data; | ||
| Split(field, ':', &data); | ||
| if (data.size() < 2) return false; | ||
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| std::string shape_str = data[0]; | ||
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| std::vector<int> shape; | ||
| Split(shape_str, ' ', &shape); | ||
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| std::string mat_str = data[1]; | ||
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| std::vector<T> mat; | ||
| Split(mat_str, ' ', &mat); | ||
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| tensor->shape = shape; | ||
| auto size = | ||
| std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>()) * | ||
| sizeof(T); | ||
| tensor->data.Resize(size); | ||
| std::copy(mat.begin(), mat.end(), static_cast<T *>(tensor->data.data())); | ||
| tensor->dtype = GetPaddleDType<T>(); | ||
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| return true; | ||
| } | ||
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| // Parse input tensors from string | ||
| bool ParseLine(const std::string &line, | ||
| std::vector<paddle::PaddleTensor> *tensors) { | ||
| std::vector<std::string> fields; | ||
| Split(line, ';', &fields); | ||
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| tensors->clear(); | ||
| tensors->reserve(4); | ||
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| int i = 0; | ||
| auto input_name = FLAGS_ernie_large ? "eval_placeholder_" : "placeholder_"; | ||
| for (; i < 3; i++) { | ||
| paddle::PaddleTensor temp; | ||
| ParseTensor<int64_t>(fields[i], &temp); | ||
| temp.name = input_name + std::to_string(i); | ||
| tensors->push_back(temp); | ||
| } | ||
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| // input_mask | ||
| paddle::PaddleTensor input_mask; | ||
| ParseTensor<float>(fields[i], &input_mask); | ||
| // fp32 to fp16 | ||
| ConvertFP32toFP16(input_mask); | ||
| input_mask.name = input_name + std::to_string(i); | ||
| tensors->push_back(input_mask); | ||
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| return true; | ||
| } | ||
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| bool LoadInputData(std::vector<std::vector<paddle::PaddleTensor>> *inputs, | ||
| int batch_size = 1) { | ||
| if (FLAGS_infer_data.empty()) { | ||
| LOG(ERROR) << "please set input data path"; | ||
| return false; | ||
| } | ||
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| std::ifstream fin(FLAGS_infer_data); | ||
| std::string line; | ||
| int sample = 0; | ||
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| // The unit-test dataset only have 10 samples, each sample have 5 feeds. | ||
| while (std::getline(fin, line)) { | ||
| std::vector<paddle::PaddleTensor> feed_data; | ||
| ParseLine(line, &feed_data); | ||
| inputs->push_back(std::move(feed_data)); | ||
| sample++; | ||
| if (!FLAGS_test_all_data && sample == batch_size) break; | ||
| } | ||
| LOG(INFO) << "number of samples: " << sample; | ||
| return true; | ||
| } | ||
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| void SetConfig(AnalysisConfig *cfg, int batch_size = 1) { | ||
| cfg->SetModel(FLAGS_infer_model); | ||
| // ipu_device_num, ipu_micro_batch_size, ipu_enable_pipelining | ||
| cfg->EnableIpu(1, batch_size, false); | ||
| // ipu_enable_fp16, ipu_replica_num, ipu_available_memory_proportion, | ||
| // ipu_enable_half_partial | ||
| cfg->SetIpuConfig(true, 1, 1.0, true); | ||
| } | ||
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| // Compare results | ||
| TEST(Analyzer_Ernie_ipu, compare_results) { | ||
| AnalysisConfig cfg; | ||
| SetConfig(&cfg); | ||
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| std::vector<std::vector<PaddleTensor>> input_slots_all; | ||
| LoadInputData(&input_slots_all); | ||
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| std::ifstream fin(FLAGS_refer_result); | ||
| std::string line; | ||
| std::vector<float> ref; | ||
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| while (std::getline(fin, line)) { | ||
| Split(line, ' ', &ref); | ||
| } | ||
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| auto predictor = CreateTestPredictor( | ||
| reinterpret_cast<const PaddlePredictor::Config *>(&cfg), | ||
| FLAGS_use_analysis); | ||
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| std::vector<PaddleTensor> outputs; | ||
| for (size_t i = 0; i < input_slots_all.size(); i++) { | ||
| outputs.clear(); | ||
| predictor->Run(input_slots_all[i], &outputs); | ||
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| auto output = outputs.front(); | ||
| ConvertFP16toFP32(output); | ||
| auto outputs_size = 1; | ||
| for (auto dim : output.shape) { | ||
| outputs_size *= dim; | ||
| } | ||
| float *fp32_data = reinterpret_cast<float *>(output.data.data()); | ||
| for (size_t j = 0; j < outputs_size; ++j) { | ||
| EXPECT_NEAR(ref[i * outputs_size + j], fp32_data[j], 5e-3); | ||
| } | ||
| } | ||
| } | ||
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| } // namespace inference | ||
| } // namespace paddle |
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86
paddle/fluid/inference/tests/api/ipu_resnet50_fp16_test.cc
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,86 @@ | ||
| /* Copyright (c) 2021 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. */ | ||
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| #include <glog/logging.h> | ||
| #include <gtest/gtest.h> | ||
| #include <cmath> | ||
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| #include "gflags/gflags.h" | ||
| #include "paddle/fluid/inference/tests/api/tester_helper.h" | ||
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| namespace paddle { | ||
| namespace inference { | ||
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| // Compare results with 1 batch | ||
| TEST(Analyzer_Resnet50_ipu, compare_results_1_batch) { | ||
| std::string model_dir = FLAGS_infer_model + "/" + "model"; | ||
| AnalysisConfig config; | ||
| // ipu_device_num, ipu_micro_batch_size, ipu_enable_pipelining | ||
| config.EnableIpu(1, 1, false); | ||
| // ipu_enable_fp16, ipu_replica_num, ipu_available_memory_proportion, | ||
| // ipu_enable_half_partial | ||
| config.SetIpuConfig(true, 1, 1.0, true); | ||
| config.SetModel(model_dir + "/model", model_dir + "/params"); | ||
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| std::vector<PaddleTensor> inputs; | ||
| auto predictor = CreatePaddlePredictor(config); | ||
| const int batch = 1; | ||
| const int channel = 3; | ||
| const int height = 318; | ||
| const int width = 318; | ||
| const int input_num = batch * channel * height * width; | ||
| std::vector<float> input(input_num, 1); | ||
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| PaddleTensor in; | ||
| in.shape = {batch, channel, height, width}; | ||
| in.data = | ||
| PaddleBuf(static_cast<void*>(input.data()), input_num * sizeof(float)); | ||
| in.dtype = PaddleDType::FLOAT32; | ||
| ConvertFP32toFP16(in); | ||
| inputs.emplace_back(in); | ||
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| std::vector<PaddleTensor> outputs; | ||
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| ASSERT_TRUE(predictor->Run(inputs, &outputs)); | ||
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| const std::vector<float> truth_values = { | ||
| 127.779f, 738.165f, 1013.22f, -438.17f, 366.401f, 927.659f, | ||
| 736.222f, -633.684f, -329.927f, -430.155f, -633.062f, -146.548f, | ||
| -1324.28f, -1349.36f, -242.675f, 117.448f, -801.723f, -391.514f, | ||
| -404.818f, 454.16f, 515.48f, -133.031f, 69.293f, 590.096f, | ||
| -1434.69f, -1070.89f, 307.074f, 400.525f, -316.12f, -587.125f, | ||
| -161.056f, 800.363f, -96.4708f, 748.706f, 868.174f, -447.938f, | ||
| 112.737f, 1127.2f, 47.4355f, 677.72f, 593.186f, -336.4f, | ||
| 551.362f, 397.823f, 78.3979f, -715.398f, 405.969f, 404.256f, | ||
| 246.019f, -8.42969f, 131.365f, -648.051f}; | ||
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| const size_t expected_size = 1; | ||
| EXPECT_EQ(outputs.size(), expected_size); | ||
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| auto output = outputs.front(); | ||
| ConvertFP16toFP32(output); | ||
| auto outputs_size = 1; | ||
| for (auto dim : output.shape) { | ||
| outputs_size *= dim; | ||
| } | ||
| float* fp32_data = reinterpret_cast<float*>(output.data.data()); | ||
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| for (size_t j = 0; j < outputs_size; j += 10) { | ||
| EXPECT_NEAR((fp32_data[j] - truth_values[j / 10]) / truth_values[j / 10], | ||
| 0., 9e-2); | ||
| } | ||
| } | ||
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| } // namespace inference | ||
| } // namespace paddle |
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