提交 4dbc0184 编写于 作者: Z Zhen Wang 提交者: Tao Luo

Nlp dam (#14248)

* add dam test

* update fuse_statis

* use separated dam model.

* Revert "use separated dam model."

This reverts commit 13e775c86f909b164b7cc1d35a8a24b964ec622e.

* test=develop

* modify the cmake file about infer test, test=develop.

* remove one comment, test=develop.
上级 ff9e531b
......@@ -29,6 +29,15 @@ set(RNN2_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/rnn2")
download_model_and_data(${RNN2_INSTALL_DIR} "rnn2_model.tar.gz" "rnn2_data.txt.tar.gz")
inference_analysis_api_test(test_analyzer_rnn2 ${RNN2_INSTALL_DIR} analyzer_rnn2_tester.cc)
# DAM
set(DAM_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/dam")
download_model_and_data(${DAM_INSTALL_DIR} "DAM_model.tar.gz" "DAM_data.txt.tar.gz")
inference_analysis_test(test_analyzer_dam SRCS analyzer_dam_tester.cc
EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} ARGS
--infer_model=${DAM_INSTALL_DIR}/model
--infer_data=${DAM_INSTALL_DIR}/data.txt
--use_analysis=0)
# chinese_ner
set(CHINESE_NER_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/chinese_ner")
download_model_and_data(${CHINESE_NER_INSTALL_DIR} "chinese_ner_model.tar.gz" "chinese_ner-data.txt.tar.gz")
......
// 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 {
using contrib::AnalysisConfig;
#define MAX_TURN_NUM 9
#define MAX_TURN_LEN 50
static std::vector<float> result_data;
struct DataRecord {
std::vector<std::vector<int64_t>>
turns[MAX_TURN_NUM]; // turns data : MAX_TURN_NUM
std::vector<std::vector<float>>
turns_mask[MAX_TURN_NUM]; // turns mask data : MAX_TURN_NUM
std::vector<std::vector<int64_t>> response; // response data : 1
std::vector<std::vector<float>> response_mask; // response mask data : 1
size_t batch_iter{0};
size_t batch_size{1};
size_t 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 <= response.size()) {
for (int i = 0; i < MAX_TURN_NUM; ++i) {
data.turns[i].assign(turns[i].begin() + batch_iter,
turns[i].begin() + batch_end);
}
for (int i = 0; i < MAX_TURN_NUM; ++i) {
data.turns_mask[i].assign(turns_mask[i].begin() + batch_iter,
turns_mask[i].begin() + batch_end);
}
data.response.assign(response.begin() + batch_iter,
response.begin() + batch_end);
data.response_mask.assign(response_mask.begin() + batch_iter,
response_mask.begin() + batch_end);
CHECK(!data.response.empty());
CHECK(!data.response_mask.empty());
CHECK_EQ(data.response.size(), data.response_mask.size());
}
batch_iter += batch_size;
return data;
}
void Load(const std::string &path) {
std::ifstream file(path);
std::string line;
size_t num_lines = 0;
result_data.clear();
while (std::getline(file, line)) {
num_lines++;
std::vector<std::string> data;
split(line, ',', &data);
CHECK_EQ(data.size(), 2 * MAX_TURN_NUM + 3);
// load turn data
std::vector<int64_t> turns_tmp[MAX_TURN_NUM];
for (int i = 0; i < MAX_TURN_NUM; ++i) {
split_to_int64(data[i], ' ', &turns_tmp[i]);
turns[i].push_back(std::move(turns_tmp[i]));
}
// load turn_mask data
std::vector<float> turns_mask_tmp[MAX_TURN_NUM];
for (int i = 0; i < MAX_TURN_NUM; ++i) {
split_to_float(data[MAX_TURN_NUM + i], ' ', &turns_mask_tmp[i]);
turns_mask[i].push_back(std::move(turns_mask_tmp[i]));
}
// load response data
std::vector<int64_t> response_tmp;
split_to_int64(data[2 * MAX_TURN_NUM], ' ', &response_tmp);
response.push_back(std::move(response_tmp));
// load response_mask data
std::vector<float> response_mask_tmp;
split_to_float(data[2 * MAX_TURN_NUM + 1], ' ', &response_mask_tmp);
response_mask.push_back(std::move(response_mask_tmp));
// load result data
float result_tmp;
result_tmp = std::stof(data[2 * MAX_TURN_NUM + 2]);
result_data.push_back(result_tmp);
}
num_samples = num_lines;
}
};
void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
int batch_size) {
PaddleTensor turns_tensor[MAX_TURN_NUM];
PaddleTensor turns_mask_tensor[MAX_TURN_NUM];
PaddleTensor response_tensor;
PaddleTensor response_mask_tensor;
std::string turn_pre = "turn_";
std::string turn_mask_pre = "turn_mask_";
auto one_batch = data->NextBatch();
int size = one_batch.response[0].size();
CHECK_EQ(size, MAX_TURN_LEN);
// turn tensor assignment
for (int i = 0; i < MAX_TURN_NUM; ++i) {
turns_tensor[i].name = turn_pre + std::to_string(i);
turns_tensor[i].shape.assign({batch_size, size, 1});
turns_tensor[i].dtype = PaddleDType::INT64;
TensorAssignData<int64_t>(&turns_tensor[i], one_batch.turns[i]);
}
// turn mask tensor assignment
for (int i = 0; i < MAX_TURN_NUM; ++i) {
turns_mask_tensor[i].name = turn_mask_pre + std::to_string(i);
turns_mask_tensor[i].shape.assign({batch_size, size, 1});
turns_mask_tensor[i].dtype = PaddleDType::FLOAT32;
TensorAssignData<float>(&turns_mask_tensor[i], one_batch.turns_mask[i]);
}
// response tensor assignment
response_tensor.name = "response";
response_tensor.shape.assign({batch_size, size, 1});
response_tensor.dtype = PaddleDType::INT64;
TensorAssignData<int64_t>(&response_tensor, one_batch.response);
// response mask tensor assignment
response_mask_tensor.name = "response_mask";
response_mask_tensor.shape.assign({batch_size, size, 1});
response_mask_tensor.dtype = PaddleDType::FLOAT32;
TensorAssignData<float>(&response_mask_tensor, one_batch.response_mask);
// Set inputs.
for (int i = 0; i < MAX_TURN_NUM; ++i) {
input_slots->push_back(std::move(turns_tensor[i]));
}
for (int i = 0; i < MAX_TURN_NUM; ++i) {
input_slots->push_back(std::move(turns_mask_tensor[i]));
}
input_slots->push_back(std::move(response_tensor));
input_slots->push_back(std::move(response_mask_tensor));
}
void SetConfig(contrib::AnalysisConfig *cfg) {
cfg->prog_file = FLAGS_infer_model + "/__model__";
cfg->param_file = FLAGS_infer_model + "/param";
cfg->use_gpu = false;
cfg->device = 0;
cfg->specify_input_name = true;
cfg->enable_ir_optim = true;
}
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_dam, profile) {
contrib::AnalysisConfig cfg;
SetConfig(&cfg);
std::vector<PaddleTensor> outputs;
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
TestPrediction(cfg, input_slots_all, &outputs, FLAGS_num_threads);
if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
PADDLE_ENFORCE_GT(outputs.size(), 0);
size_t size = GetSize(outputs[0]);
PADDLE_ENFORCE_GT(size, 0);
float *result = static_cast<float *>(outputs[0].data.data());
for (size_t i = 0; i < size; i++) {
EXPECT_NEAR(result[i], result_data[i], 1e-3);
}
}
}
// Check the fuse status
TEST(Analyzer_dam, fuse_statis) {
contrib::AnalysisConfig cfg;
SetConfig(&cfg);
if (FLAGS_use_analysis) {
int num_ops;
auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
auto fuse_statis = GetFuseStatis(
static_cast<AnalysisPredictor *>(predictor.get()), &num_ops);
ASSERT_TRUE(fuse_statis.count("fc_fuse"));
EXPECT_EQ(fuse_statis.at("fc_fuse"), 317);
EXPECT_EQ(num_ops, 2020);
}
}
// Compare result of NativeConfig and AnalysisConfig
TEST(Analyzer_dam, compare) {
contrib::AnalysisConfig cfg;
SetConfig(&cfg);
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
if (FLAGS_use_analysis) {
CompareNativeAndAnalysis(cfg, input_slots_all);
}
}
} // namespace inference
} // namespace paddle
......@@ -20,7 +20,6 @@ using contrib::AnalysisConfig;
struct DataRecord {
std::vector<std::vector<int64_t>> word_data_all, mention_data_all;
std::vector<std::vector<int64_t>> rnn_word_datas, rnn_mention_datas;
std::vector<size_t> lod; // two inputs have the same lod info.
size_t batch_iter{0};
size_t batch_size{1};
......@@ -45,8 +44,6 @@ struct DataRecord {
CHECK(!data.mention_data_all.empty());
CHECK_EQ(data.word_data_all.size(), data.mention_data_all.size());
for (size_t j = 0; j < data.word_data_all.size(); j++) {
data.rnn_word_datas.push_back(data.word_data_all[j]);
data.rnn_mention_datas.push_back(data.mention_data_all[j]);
// calculate lod
data.lod.push_back(data.lod.back() + data.word_data_all[j].size());
}
......@@ -87,8 +84,8 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
lod_mention_tensor.shape.assign({size, 1});
lod_mention_tensor.lod.assign({one_batch.lod});
// assign data
TensorAssignData<int64_t>(&lod_word_tensor, one_batch.rnn_word_datas);
TensorAssignData<int64_t>(&lod_mention_tensor, one_batch.rnn_mention_datas);
TensorAssignData<int64_t>(&lod_word_tensor, one_batch.word_data_all);
TensorAssignData<int64_t>(&lod_mention_tensor, one_batch.mention_data_all);
// Set inputs.
input_slots->assign({lod_word_tensor, lod_mention_tensor});
for (auto &tensor : *input_slots) {
......
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