-
Notifications
You must be signed in to change notification settings - Fork 5.7k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
* 裁剪transformer模型trt支持；修复tensorRT不支持DeletePass的bug (#28517) * skip_layernorm_op done * add unittest * slice op convertor support trt < 6 * skip_layernorm only work in ernie * fix unittest * fix unittest
- Loading branch information
1 parent
0a42986
commit ec672e8
Showing
16 changed files
with
353 additions
and
19 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
139 changes: 139 additions & 0 deletions
139
paddle/fluid/inference/tests/api/trt_dynamic_shape_transformer_prune_test.cc
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,139 @@ | ||
| /* 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 <gflags/gflags.h> | ||
| #include <glog/logging.h> | ||
| #include <gtest/gtest.h> | ||
|
|
||
| #include "paddle/fluid/inference/tests/api/trt_test_helper.h" | ||
|
|
||
| namespace paddle { | ||
| namespace inference { | ||
|
|
||
| void run(const AnalysisConfig& config, std::vector<float>* out_data) { | ||
| auto predictor = CreatePaddlePredictor(config); | ||
| auto input_names = predictor->GetInputNames(); | ||
|
|
||
| int run_batch = 1; | ||
| const int run_seq_len = 128; | ||
|
|
||
| std::vector<int64_t> tmp_input; | ||
| std::vector<float> tmp_four_input; | ||
| tmp_input.reserve(run_batch * run_seq_len); | ||
| tmp_four_input.reserve(run_batch * run_seq_len); | ||
|
|
||
| int64_t i0[run_seq_len] = { | ||
| 1, 3558, 4, 75, 491, 89, 340, 313, 93, 4, 255, 10, 75, 321, | ||
| 4095, 1902, 4, 134, 49, 75, 311, 14, 44, 178, 543, 15, 12043, 2, | ||
| 75, 201, 340, 9, 14, 44, 486, 218, 1140, 279, 12043, 2}; | ||
| int64_t i1[run_seq_len] = { | ||
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, | ||
| 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, | ||
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, | ||
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, | ||
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, | ||
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; | ||
| int64_t i2[run_seq_len] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, | ||
| 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, | ||
| 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, | ||
| 30, 31, 32, 33, 34, 35, 36, 37, 38, 39}; | ||
| float i3[run_seq_len] = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, | ||
| 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, | ||
| 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, | ||
| 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0}; | ||
|
|
||
| // first input | ||
| auto input_t = predictor->GetInputTensor(input_names[0]); | ||
| input_t->Reshape({run_batch, run_seq_len, 1}); | ||
| input_t->copy_from_cpu(i0); | ||
|
|
||
| // second input | ||
| auto input_t2 = predictor->GetInputTensor(input_names[1]); | ||
| input_t2->Reshape({run_batch, run_seq_len, 1}); | ||
| input_t2->copy_from_cpu(i1); | ||
|
|
||
| // third input. | ||
| auto input_t3 = predictor->GetInputTensor(input_names[2]); | ||
| input_t3->Reshape({run_batch, run_seq_len, 1}); | ||
| input_t3->copy_from_cpu(i2); | ||
|
|
||
| auto input_t4 = predictor->GetInputTensor(input_names[3]); | ||
| input_t4->Reshape({run_batch, run_seq_len, 1}); | ||
| input_t4->copy_from_cpu(i3); | ||
|
|
||
| ASSERT_TRUE(predictor->ZeroCopyRun()); | ||
|
|
||
| auto output_names = predictor->GetOutputNames(); | ||
| auto output_t = predictor->GetOutputTensor(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->copy_to_cpu(out_data->data()); | ||
| } | ||
|
|
||
| void trt_ernie(bool with_fp16, std::vector<float> result) { | ||
| AnalysisConfig config; | ||
| std::string model_dir = FLAGS_infer_model; | ||
| SetConfig(&config, model_dir, true); | ||
|
|
||
| config.SwitchUseFeedFetchOps(false); | ||
|
|
||
| int batch = 32; | ||
| int min_seq_len = 1; | ||
| int max_seq_len = 128; | ||
| int opt_seq_len = 128; | ||
|
|
||
| std::vector<int> min_shape = {1, min_seq_len, 1}; | ||
| std::vector<int> max_shape = {batch, max_seq_len, 1}; | ||
| std::vector<int> opt_shape = {batch, opt_seq_len, 1}; | ||
| // Set the input's min, max, opt shape | ||
| std::map<std::string, std::vector<int>> min_input_shape = { | ||
| {"read_file_0.tmp_0", min_shape}, | ||
| {"read_file_0.tmp_1", min_shape}, | ||
| {"read_file_0.tmp_2", min_shape}, | ||
| {"read_file_0.tmp_3", min_shape}}; | ||
| std::map<std::string, std::vector<int>> max_input_shape = { | ||
| {"read_file_0.tmp_0", max_shape}, | ||
| {"read_file_0.tmp_1", max_shape}, | ||
| {"read_file_0.tmp_2", max_shape}, | ||
| {"read_file_0.tmp_3", max_shape}}; | ||
| std::map<std::string, std::vector<int>> opt_input_shape = { | ||
| {"read_file_0.tmp_0", opt_shape}, | ||
| {"read_file_0.tmp_1", opt_shape}, | ||
| {"read_file_0.tmp_2", opt_shape}, | ||
| {"read_file_0.tmp_3", opt_shape}}; | ||
|
|
||
| auto precision = AnalysisConfig::Precision::kFloat32; | ||
| if (with_fp16) { | ||
| precision = AnalysisConfig::Precision::kHalf; | ||
| } | ||
| config.EnableTensorRtEngine(1 << 30, 1, 12, precision, false, false); | ||
| config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape, | ||
| opt_input_shape); | ||
| std::vector<float> out_data; | ||
| run(config, &out_data); | ||
|
|
||
| for (size_t i = 0; i < out_data.size(); i++) { | ||
| EXPECT_NEAR(result[i], out_data[i], 1e-4); | ||
| } | ||
| } | ||
|
|
||
| TEST(AnalysisPredictor, no_fp16) { | ||
| std::vector<float> result = {0.498667, 0.501333}; | ||
| trt_ernie(false, result); | ||
| } | ||
|
|
||
| } // namespace inference | ||
| } // namespace paddle |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.