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Paddle/paddle/fluid/inference/analysis/analyzer_tester.cc

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// 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/analysis/analyzer.h"
#include <google/protobuf/text_format.h>
#include <gtest/gtest.h>
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/inference/analysis/ut_helper.h"
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
DEFINE_string(infer_ditu_rnn_model, "", "model path for ditu RNN");
DEFINE_string(infer_ditu_rnn_data, "", "data path for ditu RNN");
DEFINE_int32(batch_size, 10, "batch size.");
DEFINE_int32(repeat, 1, "Running the inference program repeat times.");
namespace paddle {
namespace inference {
namespace analysis {
TEST(Analyzer, analysis_without_tensorrt) {
FLAGS_IA_enable_tensorrt_subgraph_engine = false;
Argument argument;
argument.fluid_model_dir.reset(new std::string(FLAGS_inference_model_dir));
Analyzer analyser;
analyser.Run(&argument);
}
TEST(Analyzer, analysis_with_tensorrt) {
FLAGS_IA_enable_tensorrt_subgraph_engine = true;
Argument argument;
argument.fluid_model_dir.reset(new std::string(FLAGS_inference_model_dir));
Analyzer analyser;
analyser.Run(&argument);
}
void TestWord2vecPrediction(const std::string &model_path) {
NativeConfig config;
config.model_dir = model_path;
config.use_gpu = false;
config.device = 0;
auto predictor =
::paddle::CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(
config);
// One single batch
int64_t data[4] = {1, 2, 3, 4};
PaddleTensor tensor;
tensor.shape = std::vector<int>({4, 1});
tensor.data = PaddleBuf(data, sizeof(data));
tensor.dtype = PaddleDType::INT64;
// For simplicity, we set all the slots with the same data.
std::vector<PaddleTensor> slots(4, tensor);
std::vector<PaddleTensor> outputs;
CHECK(predictor->Run(slots, &outputs));
PADDLE_ENFORCE(outputs.size(), 1UL);
// Check the output buffer size and result of each tid.
PADDLE_ENFORCE(outputs.front().data.length(), 33168UL);
float result[5] = {0.00129761, 0.00151112, 0.000423564, 0.00108815,
0.000932706};
const size_t num_elements = outputs.front().data.length() / sizeof(float);
// The outputs' buffers are in CPU memory.
for (size_t i = 0; i < std::min(5UL, num_elements); i++) {
LOG(INFO) << "data: "
<< static_cast<float *>(outputs.front().data.data())[i];
PADDLE_ENFORCE(static_cast<float *>(outputs.front().data.data())[i],
result[i]);
}
}
namespace {
struct DataRecord {
std::vector<std::vector<std::vector<float>>> link_step_data_all;
std::vector<std::vector<float>> week_data_all, minute_data_all;
std::vector<size_t> lod1, lod2, lod3;
std::vector<std::vector<float>> rnn_link_data, rnn_week_datas,
rnn_minute_datas;
size_t batch_iter{0};
size_t batch_size{1};
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 <= link_step_data_all.size()) {
data.link_step_data_all.assign(link_step_data_all.begin() + batch_iter,
link_step_data_all.begin() + batch_end);
data.week_data_all.assign(week_data_all.begin() + batch_iter,
week_data_all.begin() + batch_end);
data.minute_data_all.assign(minute_data_all.begin() + batch_iter,
minute_data_all.begin() + batch_end);
// Prepare LoDs
data.lod1.push_back(0);
data.lod2.push_back(0);
data.lod3.push_back(0);
CHECK(!data.link_step_data_all.empty()) << "empty";
CHECK(!data.week_data_all.empty());
CHECK(!data.minute_data_all.empty());
CHECK_EQ(data.link_step_data_all.size(), data.week_data_all.size());
CHECK_EQ(data.minute_data_all.size(), data.link_step_data_all.size());
for (size_t j = 0; j < data.link_step_data_all.size(); j++) {
for (const auto &d : data.link_step_data_all[j]) {
data.rnn_link_data.push_back(d);
}
data.rnn_week_datas.push_back(data.week_data_all[j]);
data.rnn_minute_datas.push_back(data.minute_data_all[j]);
// calculate lod
data.lod1.push_back(data.lod1.back() +
data.link_step_data_all[j].size());
data.lod3.push_back(data.lod3.back() + 1);
for (size_t i = 1; i < data.link_step_data_all[j].size() + 1; i++) {
data.lod2.push_back(data.lod2.back() +
data.link_step_data_all[j].size());
}
}
}
batch_iter += batch_size;
return data;
}
void Load(const std::string &path) {
std::ifstream file(path);
std::string line;
int num_lines = 0;
while (std::getline(file, line)) {
num_lines++;
std::vector<std::string> data;
split(line, ':', &data);
std::vector<std::vector<float>> link_step_data;
std::vector<std::string> link_datas;
split(data[0], '|', &link_datas);
for (auto &step_data : link_datas) {
std::vector<float> tmp;
split_to_float(step_data, ',', &tmp);
link_step_data.push_back(tmp);
}
// load week data
std::vector<float> week_data;
split_to_float(data[2], ',', &week_data);
// load minute data
std::vector<float> minute_data;
split_to_float(data[1], ',', &minute_data);
link_step_data_all.push_back(std::move(link_step_data));
week_data_all.push_back(std::move(week_data));
minute_data_all.push_back(std::move(minute_data));
}
}
};
void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
int batch_size) {
PaddleTensor lod_attention_tensor, init_zero_tensor, lod_tensor_tensor,
week_tensor, minute_tensor;
lod_attention_tensor.name = "data_lod_attention";
init_zero_tensor.name = "cell_init";
lod_tensor_tensor.name = "data";
week_tensor.name = "week";
minute_tensor.name = "minute";
auto one_batch = data->NextBatch();
std::vector<int> rnn_link_data_shape(
{static_cast<int>(one_batch.rnn_link_data.size()),
static_cast<int>(one_batch.rnn_link_data.front().size())});
lod_attention_tensor.shape.assign({1, 2});
lod_attention_tensor.lod.assign({one_batch.lod1, one_batch.lod2});
init_zero_tensor.shape.assign({batch_size, 15});
init_zero_tensor.lod.assign({one_batch.lod3});
lod_tensor_tensor.shape = rnn_link_data_shape;
lod_tensor_tensor.lod.assign({one_batch.lod1});
// clang-format off
week_tensor.shape.assign(
{static_cast<int>(one_batch.rnn_week_datas.size()),
static_cast<int>(one_batch.rnn_week_datas.front().size())});
week_tensor.lod.assign({one_batch.lod3});
minute_tensor.shape.assign(
{static_cast<int>(one_batch.rnn_minute_datas.size()),
static_cast<int>(one_batch.rnn_minute_datas.front().size())});
minute_tensor.lod.assign({one_batch.lod3});
// clang-format on
// assign data
TensorAssignData(&lod_attention_tensor,
std::vector<std::vector<float>>({{0, 0}}));
std::vector<float> tmp_zeros(batch_size * 15, 0.);
TensorAssignData(&init_zero_tensor, {tmp_zeros});
TensorAssignData(&lod_tensor_tensor, one_batch.rnn_link_data);
TensorAssignData(&week_tensor, one_batch.rnn_week_datas);
TensorAssignData(&minute_tensor, one_batch.rnn_minute_datas);
// Set inputs.
auto init_zero_tensor1 = init_zero_tensor;
init_zero_tensor1.name = "hidden_init";
input_slots->assign({week_tensor, init_zero_tensor, minute_tensor,
init_zero_tensor1, lod_attention_tensor,
lod_tensor_tensor});
for (auto &tensor : *input_slots) {
tensor.dtype = PaddleDType::FLOAT32;
}
}
std::string DescribeTensor(const PaddleTensor &tensor) {
std::stringstream os;
os << "Tensor [" << tensor.name << "]\n";
os << " - type: ";
switch (tensor.dtype) {
case PaddleDType::FLOAT32:
os << "float32";
break;
case PaddleDType::INT64:
os << "int64";
break;
default:
os << "unset";
}
os << '\n';
os << " - shape: " << to_string(tensor.shape) << '\n';
os << " - lod: ";
for (auto &l : tensor.lod) {
os << to_string(l) << "; ";
}
os << "\n";
os << " - data: ";
int dim = std::accumulate(tensor.shape.begin(), tensor.shape.end(), 1,
[](int a, int b) { return a * b; });
for (int i = 0; i < dim; i++) {
os << static_cast<float *>(tensor.data.data())[i] << " ";
}
os << '\n';
return os.str();
}
} // namespace
const float ditu_rnn_target_data[] = {
104.711, 11.2431, 1.35422, 0, 0, 0, 0, 0,
27.7039, 1.41486, 7.09526, 0, 0, 0, 0, 0,
7.6481, 6.5324, 56.383, 2.88018, 8.92918, 132.007, 4.27429, 2.02934,
14.1727, 10.7461, 25.0616, 16.0197, 14.4163, 16.9199, 6.75517, 0,
80.0249, 4.77739, 0, 0, 0, 0, 0, 0,
47.5643, 2.67029, 8.76252, 0, 0, 0, 0, 0,
51.8822, 4.4411, 0, 0, 0, 0, 0, 0,
10.7286, 12.0595, 10.6672, 0, 0, 0, 0, 0,
93.5771, 3.84641, 0, 0, 0, 0, 0, 0,
169.426, 0, 0, 0, 0, 0, 0, 0};
// Test with a really complicate model.
void TestDituRNNPrediction(const std::string &model_path,
const std::string &data_path, int batch_size,
bool use_analysis, bool activate_ir,
int num_times = 1) {
FLAGS_IA_enable_ir = activate_ir;
FLAGS_IA_enable_tensorrt_subgraph_engine = false;
FLAGS_IA_output_storage_path = "./analysis.out";
std::string model_out;
if (use_analysis) {
Argument argument(model_path);
argument.model_output_store_path.reset(new std::string("./analysis.out"));
Analyzer analyzer;
analyzer.Run(&argument);
// Should get the transformed model stored to ./analysis.out
model_out = "./analysis.out";
ASSERT_TRUE(PathExists(model_out));
} else {
model_out = FLAGS_infer_ditu_rnn_model;
}
NativeConfig config;
config.prog_file = model_out + "/__model__";
config.param_file = model_out + "/param";
config.use_gpu = false;
config.device = 0;
config.specify_input_name = true;
auto predictor =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
std::vector<PaddleTensor> input_slots;
DataRecord data(data_path, batch_size);
// Prepare inputs.
PrepareInputs(&input_slots, &data, batch_size);
std::vector<PaddleTensor> outputs;
Timer timer;
timer.tic();
for (int i = 0; i < num_times; i++) {
predictor->Run(input_slots, &outputs);
}
LOG(INFO) << "===========profile result===========";
LOG(INFO) << "batch_size: " << batch_size << ", repeat: " << num_times
<< ", latency: " << timer.toc() / num_times << "ms";
LOG(INFO) << "=====================================";
for (auto &out : outputs) {
size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1,
[](int a, int b) { return a * b; });
float *data = static_cast<float *>(out.data.data());
for (size_t i = 0;
i < std::min(sizeof(ditu_rnn_target_data) / sizeof(float), size);
i++) {
EXPECT_NEAR(data[i], ditu_rnn_target_data[i], 1e-3);
}
}
}
// Turn on the IR pass supportion, run a real inference and check the result.
TEST(Analyzer, SupportIRPass) {
FLAGS_IA_enable_ir = true;
FLAGS_IA_enable_tensorrt_subgraph_engine = false;
FLAGS_IA_output_storage_path = "./analysis.out";
Argument argument(FLAGS_inference_model_dir);
argument.model_output_store_path.reset(new std::string("./analysis.out"));
Analyzer analyzer;
analyzer.Run(&argument);
// Should get the transformed model stored to ./analysis.out
ASSERT_TRUE(PathExists("./analysis.out"));
// Inference from this path.
TestWord2vecPrediction("./analysis.out");
}
// Directly infer with the original model.
TEST(Analyzer, DituRNN_without_analysis) {
TestDituRNNPrediction(FLAGS_infer_ditu_rnn_model, FLAGS_infer_ditu_rnn_data,
FLAGS_batch_size, false, false, FLAGS_repeat);
}
// Inference with the original model with the analysis turned on, the analysis
// module will transform the program to a data flow graph.
TEST(Analyzer, DituRNN_with_analysis) {
LOG(INFO) << "ditu rnn with analysis";
TestDituRNNPrediction(FLAGS_infer_ditu_rnn_model, FLAGS_infer_ditu_rnn_data,
FLAGS_batch_size, true, false, FLAGS_repeat);
}
// Inference with analysis and IR. The IR module will fuse some large kernels.
TEST(Analyzer, DituRNN_with_analysis_with_IR) {
LOG(INFO) << "ditu rnn with analysis and IR fuse";
TestDituRNNPrediction(FLAGS_infer_ditu_rnn_model, FLAGS_infer_ditu_rnn_data,
FLAGS_batch_size, true, true, FLAGS_repeat);
}
} // namespace analysis
} // namespace inference
} // namespace paddle
USE_PASS(fc_fuse_pass);
USE_PASS(graph_viz_pass);
USE_PASS(infer_clean_graph_pass);
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