未验证 提交 2b633173 编写于 作者: T Tao Luo 提交者: GitHub

Merge pull request #15601 from luotao1/transformer_ut

add analyzer_transformer_test
......@@ -86,6 +86,13 @@ static void split_to_int64(const std::string &str, char sep,
std::transform(pieces.begin(), pieces.end(), std::back_inserter(*is),
[](const std::string &v) { return std::stoi(v); });
}
static void split_to_int(const std::string &str, char sep,
std::vector<int> *is) {
std::vector<std::string> pieces;
split(str, sep, &pieces);
std::transform(pieces.begin(), pieces.end(), std::back_inserter(*is),
[](const std::string &v) { return std::stoi(v); });
}
template <typename T>
std::string to_string(const std::vector<T> &vec) {
std::stringstream ss;
......
......@@ -105,6 +105,13 @@ set(SEQ_CONV1_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/seq_conv1")
download_model_and_data(${SEQ_CONV1_INSTALL_DIR} "seq_conv1_model.tar.gz" "seq_conv1_data.txt.tar.gz")
inference_analysis_api_test(test_analyzer_seq_conv1 ${SEQ_CONV1_INSTALL_DIR} analyzer_seq_conv1_tester.cc)
# transformer, the dataset only works on batch_size=8 now
set(TRANSFORMER_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/transformer")
download_model_and_data(${TRANSFORMER_INSTALL_DIR} "temp%2Ftransformer_model.tar.gz" "temp%2Ftransformer_data.txt.tar.gz")
inference_analysis_test(test_analyzer_transformer SRCS analyzer_transformer_tester.cc
EXTRA_DEPS ${INFERENCE_EXTRA_DEPS}
ARGS --infer_model=${TRANSFORMER_INSTALL_DIR}/model --infer_data=${TRANSFORMER_INSTALL_DIR}/data.txt --batch_size=8)
# ocr
set(OCR_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/ocr")
if (NOT EXISTS ${OCR_INSTALL_DIR})
......
// 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/tests/api/tester_helper.h"
namespace paddle {
namespace inference {
struct DataRecord {
std::vector<std::vector<int64_t>> src_word, src_pos, trg_word, init_idx;
std::vector<std::vector<float>> src_slf_attn_bias, init_score,
trg_src_attn_bias;
std::vector<std::vector<int32_t>> batch_data_shape;
std::vector<std::vector<size_t>> lod;
size_t batch_iter{0}, batch_size{1}, num_samples; // total number of samples
DataRecord() = default;
explicit DataRecord(const std::string &path, int batch_size = 1)
: batch_size(batch_size) {
Load(path);
}
DataRecord NextBatch() {
DataRecord data;
size_t batch_end = batch_iter + batch_size;
// NOTE skip the final batch, if no enough data is provided.
if (batch_end <= src_word.size()) {
data.src_word.assign(src_word.begin() + batch_iter,
src_word.begin() + batch_end);
data.src_pos.assign(src_pos.begin() + batch_iter,
src_pos.begin() + batch_end);
data.src_slf_attn_bias.assign(src_slf_attn_bias.begin() + batch_iter,
src_slf_attn_bias.begin() + batch_end);
data.trg_word.assign(trg_word.begin() + batch_iter,
trg_word.begin() + batch_end);
data.init_score.assign(init_score.begin() + batch_iter,
init_score.begin() + batch_end);
data.init_idx.assign(init_idx.begin() + batch_iter,
init_idx.begin() + batch_end);
data.trg_src_attn_bias.assign(trg_src_attn_bias.begin() + batch_iter,
trg_src_attn_bias.begin() + batch_end);
std::vector<int32_t> batch_shape =
*(batch_data_shape.begin() + batch_iter);
data.batch_data_shape.push_back(batch_shape);
data.lod.resize(2);
for (int i = 0; i < batch_shape[0] + 1; i++) {
data.lod[0].push_back(i);
data.lod[1].push_back(i);
}
}
batch_iter += batch_size;
return data;
}
void Load(const std::string &path) {
std::ifstream file(path);
std::string line;
size_t num_lines = 0;
while (std::getline(file, line)) {
num_lines++;
std::vector<std::string> data;
split(line, ',', &data);
CHECK_EQ(data.size(), static_cast<size_t>(8));
// load src_word
std::vector<int64_t> src_word_data;
split_to_int64(data[0], ' ', &src_word_data);
src_word.push_back(std::move(src_word_data));
// load src_pos
std::vector<int64_t> src_pos_data;
split_to_int64(data[1], ' ', &src_pos_data);
src_pos.push_back(std::move(src_pos_data));
// load src_slf_attn_bias
std::vector<float> src_slf_attn_bias_data;
split_to_float(data[2], ' ', &src_slf_attn_bias_data);
src_slf_attn_bias.push_back(std::move(src_slf_attn_bias_data));
// load trg_word
std::vector<int64_t> trg_word_data;
split_to_int64(data[3], ' ', &trg_word_data);
trg_word.push_back(std::move(trg_word_data));
// load init_score
std::vector<float> init_score_data;
split_to_float(data[4], ' ', &init_score_data);
init_score.push_back(std::move(init_score_data));
// load init_idx
std::vector<int64_t> init_idx_data;
split_to_int64(data[5], ' ', &init_idx_data);
init_idx.push_back(std::move(init_idx_data));
// load trg_src_attn_bias
std::vector<float> trg_src_attn_bias_data;
split_to_float(data[6], ' ', &trg_src_attn_bias_data);
trg_src_attn_bias.push_back(std::move(trg_src_attn_bias_data));
// load shape for variant data shape
std::vector<int> batch_data_shape_data;
split_to_int(data[7], ' ', &batch_data_shape_data);
batch_data_shape.push_back(std::move(batch_data_shape_data));
}
num_samples = num_lines;
}
};
void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
int batch_size) {
auto one_batch = data->NextBatch();
batch_size = one_batch.batch_data_shape[0][0];
auto n_head = one_batch.batch_data_shape[0][1];
auto trg_seq_len = one_batch.batch_data_shape[0][2]; // 1 for inference
auto src_seq_len = one_batch.batch_data_shape[0][3];
PaddleTensor src_word, src_pos, src_slf_attn_bias, trg_word, init_score,
init_idx, trg_src_attn_bias;
src_word.name = "src_word";
src_word.shape.assign({batch_size, src_seq_len, 1});
src_word.dtype = PaddleDType::INT64;
TensorAssignData<int64_t>(&src_word, one_batch.src_word);
src_pos.name = "src_pos";
src_pos.shape.assign({batch_size, src_seq_len, 1});
src_pos.dtype = PaddleDType::INT64;
TensorAssignData<int64_t>(&src_pos, one_batch.src_pos);
src_slf_attn_bias.name = "src_slf_attn_bias";
src_slf_attn_bias.shape.assign(
{batch_size, n_head, src_seq_len, src_seq_len});
src_slf_attn_bias.dtype = PaddleDType::FLOAT32;
TensorAssignData<float>(&src_slf_attn_bias, one_batch.src_slf_attn_bias);
trg_word.name = "trg_word";
trg_word.shape.assign({batch_size, 1});
trg_word.dtype = PaddleDType::INT64;
trg_word.lod.assign(one_batch.lod.begin(), one_batch.lod.end());
TensorAssignData<int64_t>(&trg_word, one_batch.trg_word);
init_score.name = "init_score";
init_score.shape.assign({batch_size, 1});
init_score.dtype = PaddleDType::FLOAT32;
init_score.lod.assign(one_batch.lod.begin(), one_batch.lod.end());
TensorAssignData<float>(&init_score, one_batch.init_score);
init_idx.name = "init_idx";
init_idx.shape.assign({batch_size});
init_idx.dtype = PaddleDType::INT32;
TensorAssignData<int64_t>(&init_idx, one_batch.init_idx);
trg_src_attn_bias.name = "trg_src_attn_bias";
trg_src_attn_bias.shape.assign(
{batch_size, n_head, trg_seq_len, src_seq_len});
trg_src_attn_bias.dtype = PaddleDType::FLOAT32;
TensorAssignData<float>(&trg_src_attn_bias, one_batch.trg_src_attn_bias);
input_slots->assign({src_word, src_pos, src_slf_attn_bias, trg_word,
init_score, init_idx, trg_src_attn_bias});
}
void SetConfig(AnalysisConfig *cfg) {
cfg->SetModel(FLAGS_infer_model + "/model", FLAGS_infer_model + "/params");
cfg->DisableGpu();
cfg->SwitchSpecifyInputNames();
cfg->SwitchIrOptim();
cfg->SetCpuMathLibraryNumThreads(FLAGS_paddle_num_threads);
}
void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
std::vector<PaddleTensor> input_slots;
int test_batch_num =
FLAGS_test_all_data ? data.num_samples / FLAGS_batch_size : 1;
LOG(INFO) << "The number of samples to be test: "
<< test_batch_num * FLAGS_batch_size;
for (int bid = 0; bid < test_batch_num; ++bid) {
input_slots.clear();
PrepareInputs(&input_slots, &data, FLAGS_batch_size);
(*inputs).emplace_back(input_slots);
}
}
// Easy for profiling independently.
TEST(Analyzer_Transformer, profile) {
AnalysisConfig cfg;
SetConfig(&cfg);
std::vector<PaddleTensor> outputs;
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
TestPrediction(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
input_slots_all, &outputs, FLAGS_num_threads);
}
// Check the fuse status
TEST(Analyzer_Transformer, fuse_statis) {
AnalysisConfig cfg;
SetConfig(&cfg);
int num_ops;
auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
auto fuse_statis = GetFuseStatis(
static_cast<AnalysisPredictor *>(predictor.get()), &num_ops);
}
// Compare result of NativeConfig and AnalysisConfig
TEST(Analyzer_Transformer, compare) {
AnalysisConfig cfg;
SetConfig(&cfg);
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
CompareNativeAndAnalysis(
reinterpret_cast<const PaddlePredictor::Config *>(&cfg), input_slots_all);
}
} // namespace inference
} // namespace paddle
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