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support ernie-int8 test and prune op attribute test (#35890)
* support ernie-int8 test and prune op attribute test * remove using and use namespace * remove macro and use shell instead * Revert "remove macro and use shell instead" This reverts commit 615964b. * fix grammar error * fix shell error
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paddle/fluid/inference/tests/infer_ut/test_ernie_xnli_int8.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 "test_helper.h" // NOLINT | ||
| #include "test_suite.h" // NOLINT | ||
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| DEFINE_string(modeldir, "", "Directory of the inference model."); | ||
| DEFINE_string(datadir, "", "dataset."); | ||
| DEFINE_string(truth_data, "", "Directory of the inference data truth result"); | ||
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| namespace paddle_infer { | ||
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| std::shared_ptr<Predictor> InitPredictor() { | ||
| Config config; | ||
| config.SetModel(FLAGS_modeldir + "/__model__", | ||
| FLAGS_modeldir + "/__params__"); | ||
| config.EnableUseGpu(1000, 0); | ||
| // Open the memory optim. | ||
| config.EnableMemoryOptim(); | ||
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| int max_batch = 32; | ||
| int max_single_seq_len = 128; | ||
| int opt_single_seq_len = 64; | ||
| int min_batch_seq_len = 1; | ||
| int max_batch_seq_len = 512; | ||
| int opt_batch_seq_len = 256; | ||
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| std::string input_name0 = "eval_placeholder_0"; | ||
| std::string input_name1 = "eval_placeholder_1"; | ||
| std::string input_name2 = "eval_placeholder_2"; | ||
| std::string input_name3 = "eval_placeholder_3"; | ||
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| std::vector<int> min_shape = {min_batch_seq_len}; | ||
| std::vector<int> max_shape = {max_batch_seq_len}; | ||
| std::vector<int> opt_shape = {opt_batch_seq_len}; | ||
| // Set the input's min, max, opt shape | ||
| std::map<std::string, std::vector<int>> min_input_shape = { | ||
| {input_name0, min_shape}, | ||
| {input_name1, min_shape}, | ||
| {input_name2, {1}}, | ||
| {input_name3, {1, min_batch_seq_len, 1}}}; | ||
| std::map<std::string, std::vector<int>> max_input_shape = { | ||
| {input_name0, max_shape}, | ||
| {input_name1, max_shape}, | ||
| {input_name2, {max_batch + 1}}, | ||
| {input_name3, {1, max_single_seq_len, 1}}}; | ||
| std::map<std::string, std::vector<int>> opt_input_shape = { | ||
| {input_name0, opt_shape}, | ||
| {input_name1, opt_shape}, | ||
| {input_name2, {max_batch + 1}}, | ||
| {input_name3, {1, opt_single_seq_len, 1}}}; | ||
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| // only kHalf supported | ||
| config.EnableTensorRtEngine(1 << 30, 1, 5, Config::Precision::kInt8, false, | ||
| false); | ||
| // erinie varlen must be used with dynamic shape | ||
| config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape, | ||
| opt_input_shape); | ||
| // erinie varlen must be used with oss | ||
| config.EnableTensorRtOSS(); | ||
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| return CreatePredictor(config); | ||
| } | ||
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| // Parse tensor from string | ||
| template <typename T> | ||
| std::vector<T> ParseTensor(const std::string &field) { | ||
| std::string mat_str = field; | ||
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| std::vector<T> mat; | ||
| paddle::test::Split(mat_str, ' ', &mat); | ||
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| return mat; | ||
| } | ||
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| void run(Predictor *predictor, std::vector<float> *out_data) { | ||
| clock_t start, end; | ||
| start = clock(); | ||
| CHECK(predictor->Run()); | ||
| end = clock(); | ||
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| auto output_names = predictor->GetOutputNames(); | ||
| auto output_t = predictor->GetOutputHandle(output_names[0]); | ||
| std::vector<int> output_shape = output_t->shape(); | ||
| int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1, | ||
| std::multiplies<int>()); | ||
| out_data->resize(out_num); | ||
| output_t->CopyToCpu(out_data->data()); | ||
| return; | ||
| } | ||
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| auto PrepareOutput(std::string input_file) -> std::deque<float> { | ||
| std::ifstream fin(input_file); | ||
| std::string line; | ||
| std::vector<std::string> buffer; | ||
| while (std::getline(fin, line)) { | ||
| buffer.emplace_back(line); | ||
| } | ||
| std::deque<float> resDeque(buffer.size()); | ||
| std::transform(buffer.begin(), buffer.end(), resDeque.begin(), | ||
| [](const std::string &val) { return std::stof(val); }); | ||
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| return resDeque; | ||
| } // PrepareOutput | ||
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| TEST(tensorrt_tester_ernie_xnli, oss_varlen_truth_data_int8) { | ||
| auto resDeque = PrepareOutput(FLAGS_truth_data); | ||
| auto predictor = InitPredictor(); | ||
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| ASSERT_FALSE(FLAGS_datadir.empty()); | ||
| std::ifstream fin(FLAGS_datadir); | ||
| std::string line; | ||
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| int lineno = 0; | ||
| int max_seq_len = 128; | ||
| const int run_batch = 1; | ||
| int correct_num = 0; | ||
| while (std::getline(fin, line)) { | ||
| std::vector<std::string> fields; | ||
| paddle::test::Split(line, ';', &fields); | ||
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| auto src_ids = ParseTensor<int32_t>(fields[0]); | ||
| auto sent_ids = ParseTensor<int32_t>(fields[1]); | ||
| auto pos_ids = ParseTensor<int64_t>(fields[2]); | ||
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| int run_seq_len = src_ids.size(); | ||
| int32_t i3[2] = {0, run_seq_len}; | ||
| int32_t i4[max_seq_len] = {0}; | ||
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| auto input_names = predictor->GetInputNames(); | ||
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| // first input | ||
| auto input_t1 = predictor->GetInputHandle(input_names[0]); | ||
| input_t1->Reshape({run_seq_len}); | ||
| input_t1->CopyFromCpu(src_ids.data()); | ||
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| // second input | ||
| auto input_t2 = predictor->GetInputHandle(input_names[1]); | ||
| input_t2->Reshape({run_seq_len}); | ||
| input_t2->CopyFromCpu(sent_ids.data()); | ||
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| // third input | ||
| auto input_t3 = predictor->GetInputHandle(input_names[2]); | ||
| input_t3->Reshape({run_batch + 1}); | ||
| input_t3->CopyFromCpu(i3); | ||
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| // fourth input | ||
| auto input_t4 = predictor->GetInputHandle(input_names[3]); | ||
| input_t4->Reshape({1, max_seq_len, 1}); | ||
| input_t4->CopyFromCpu(i4); | ||
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| std::vector<float> out_data; | ||
| run(predictor.get(), &out_data); | ||
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| lineno++; | ||
| int maxPosition = | ||
| max_element(out_data.begin(), out_data.end()) - out_data.begin(); | ||
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| if (maxPosition == resDeque[0]) { | ||
| correct_num += 1; | ||
| } | ||
| resDeque.pop_front(); | ||
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| VLOG(2) << "predict result: " << maxPosition; | ||
| for (auto r : out_data) { | ||
| VLOG(2) << r; | ||
| } | ||
| } | ||
| ASSERT_GT(correct_num, | ||
| 4741); // total input 5010, int8 res should greater than 4741 | ||
| LOG(INFO) << "=== finish oss test ==="; | ||
| } | ||
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| } // namespace paddle_infer | ||
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| int main(int argc, char **argv) { | ||
| ::testing::InitGoogleTest(&argc, argv); | ||
| ::google::ParseCommandLineFlags(&argc, &argv, true); | ||
| #if IS_TRT_VERSION_GE(7200) | ||
| return RUN_ALL_TESTS(); | ||
| #endif | ||
| return 0; | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,79 @@ | ||
| // 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. | ||
| #pragma once | ||
| #include <sstream> | ||
| #include <string> | ||
| #include <vector> | ||
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| namespace paddle { | ||
| namespace test { | ||
|
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| // split string to vector<string> by sep | ||
| static void split(const std::string &str, char sep, | ||
| std::vector<std::string> *pieces, bool ignore_null = true) { | ||
| pieces->clear(); | ||
| if (str.empty()) { | ||
| if (!ignore_null) { | ||
| pieces->push_back(str); | ||
| } | ||
| return; | ||
| } | ||
| size_t pos = 0; | ||
| size_t next = str.find(sep, pos); | ||
| while (next != std::string::npos) { | ||
| pieces->push_back(str.substr(pos, next - pos)); | ||
| pos = next + 1; | ||
| next = str.find(sep, pos); | ||
| } | ||
| if (!str.substr(pos).empty()) { | ||
| pieces->push_back(str.substr(pos)); | ||
| } | ||
| } | ||
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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 multiple 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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| } // namespace test | ||
| } // namespace paddle |
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