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add multi-thread ut for ditu-rnn

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fix-develop-build.sh
luotao1 7 years ago
parent d0c65bff04
commit 0fbe0a7a28
6 changed files with 119 additions and 86 deletions
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2
paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc
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@ -13,10 +13,10 @@
// limitations under the License.
#include "paddle/fluid/framework/ir/attention_lstm_fuse_pass.h"
#include <string>
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/framework/ir/graph_viz_pass.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/inference/api/helper.h"
namespace paddle {
namespace framework {

8
paddle/fluid/framework/ir/fc_lstm_fuse_pass.cc
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@ -35,7 +35,6 @@ std::unique_ptr<ir::Graph> FCLstmFusePass::ApplyImpl(
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
auto* id = subgraph.at(gpd.pattern().RetrieveNode("any_node"));
marked_nodes.insert(id);
};
@ -89,7 +88,6 @@ std::unique_ptr<ir::Graph> FCLstmFusePass::ApplyImpl(
LINK_TO(op, hidden_n);
#undef LINK_TO
return op;
};
lstm_creator(16, 12, 14, 18, 17, 22, 21, 19);
@ -105,14 +103,16 @@ std::unique_ptr<ir::Graph> FCLstmFusePass::ApplyImpl(
for (auto it = node->inputs.begin(); it != node->inputs.end();) {
if (marked_nodes.count(*it)) {
it = const_cast<Node*>(node)->inputs.erase(it);
} else
} else {
it++;
}
}
for (auto it = node->outputs.begin(); it != node->outputs.end();) {
if (marked_nodes.count(*it)) {
it = const_cast<Node*>(node)->outputs.erase(it);
} else
} else {
it++;
}
}
}

2
paddle/fluid/framework/ir/graph_pattern_detector.cc
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@ -81,7 +81,7 @@ void GraphPatternDetector::operator()(Graph* graph,
LOG(INFO) << "detect " << subgraphs.size() << " subgraph matches the pattern";
int id = 0;
for (auto& g : subgraphs) {
LOG(INFO) << "optimizing #" << id++ << " subgraph";
VLOG(3) << "optimizing #" << id++ << " subgraph";
handler(g, graph);
}
}

145
paddle/fluid/inference/analysis/analyzer_tester.cc
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@ -16,6 +16,7 @@
#include <google/protobuf/text_format.h>
#include <gtest/gtest.h>
#include <thread> // NOLINT
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/inference/analysis/ut_helper.h"
@ -23,19 +24,17 @@
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/utils/singleton.h"
#include "paddle/fluid/platform/profiler.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.");
DEFINE_int32(num_threads, 1, "Running the inference program in multi-threads.");
namespace paddle {
namespace inference {
namespace analysis {
using namespace framework;
TEST(Analyzer, analysis_without_tensorrt) {
FLAGS_IA_enable_tensorrt_subgraph_engine = false;
Argument argument;
@ -219,39 +218,6 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
}
}
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[] = {
@ -266,58 +232,92 @@ const float ditu_rnn_target_data[] = {
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) {
void TestDituRNNPrediction(bool use_analysis_and_activate_ir = false,
int num_threads = FLAGS_num_threads) {
NativeConfig config;
config.prog_file = FLAGS_infer_ditu_rnn_model + "/__model__";
config.param_file = FLAGS_infer_ditu_rnn_model + "/param";
config.use_gpu = false;
config.device = 0;
config.specify_input_name = true;
int batch_size = FLAGS_batch_size;
int num_times = FLAGS_repeat;
auto base_predictor =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
auto predictor =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kAnalysis>(config);
std::vector<PaddleTensor> input_slots;
DataRecord data(data_path, batch_size);
DataRecord data(FLAGS_infer_ditu_rnn_data, batch_size);
// Prepare inputs.
PrepareInputs(&input_slots, &data, batch_size);
std::vector<PaddleTensor> outputs, base_outputs;
base_predictor->Run(input_slots, &base_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";
if (num_threads == 1) {
std::vector<PaddleTensor> input_slots;
// Prepare inputs.
DataRecord data(FLAGS_infer_ditu_rnn_data, batch_size);
PrepareInputs(&input_slots, &data, batch_size);
Timer timer;
timer.tic();
for (int i = 0; i < num_times; i++) {
predictor->Run(input_slots, &outputs);
}
print_time(batch_size, num_times, 1, 0, timer.toc() / num_times);
} else {
std::vector<std::thread> threads;
std::vector<PaddleTensor> input_slots;
// Prepare inputs.
PrepareInputs(&input_slots, &data, batch_size);
std::vector<PaddleTensor> outputs;
for (int tid = 0; tid < num_threads; ++tid) {
threads.emplace_back([&, tid]() {
auto predictor_tid =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kAnalysis>(
config);
DataRecord data(FLAGS_infer_ditu_rnn_data, batch_size);
Timer timer;
timer.tic();
for (int i = 0; i < num_times; i++) {
predictor_tid->Run(input_slots, &outputs);
}
print_time(batch_size, num_times, num_threads, tid,
timer.toc() / num_times);
});
}
for (int i = 0; i < num_threads; ++i) {
threads[i].join();
}
}
LOG(INFO) << "=====================================";
PADDLE_ENFORCE_GT(outputs.size(), 0);
PADDLE_ENFORCE_EQ(outputs.size(), base_outputs.size());
for (size_t i = 0; i < outputs.size(); i++) {
auto &out = outputs[i];
auto &base_out = base_outputs[i];
size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1,
[](int a, int b) { return a * b; });
size_t size1 = std::accumulate(base_out.shape.begin(), base_out.shape.end(),
1, [](int a, int b) { return a * b; });
PADDLE_ENFORCE_EQ(size, size1);
PADDLE_ENFORCE_GT(size, 0);
float *data = static_cast<float *>(out.data.data());
float *base_data = static_cast<float *>(base_out.data.data());
for (size_t i = 0; i < size; i++) {
EXPECT_NEAR(data[i], base_data[i], 1e-3);
if (num_threads == 1) {
PADDLE_ENFORCE_GT(outputs.size(), 0);
PADDLE_ENFORCE_EQ(outputs.size(), base_outputs.size());
for (size_t i = 0; i < outputs.size(); i++) {
auto &out = outputs[i];
auto &base_out = base_outputs[i];
size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1,
[](int a, int b) { return a * b; });
size_t size1 =
std::accumulate(base_out.shape.begin(), base_out.shape.end(), 1,
[](int a, int b) { return a * b; });
PADDLE_ENFORCE_EQ(size, size1);
PADDLE_ENFORCE_GT(size, 0);
float *data = static_cast<float *>(out.data.data());
float *base_data = static_cast<float *>(base_out.data.data());
for (size_t i = 0; i < size; i++) {
EXPECT_NEAR(data[i], base_data[i], 1e-3);
}
}
}
if (use_analysis && activate_ir) {
if (use_analysis_and_activate_ir) {
AnalysisPredictor *analysis_predictor =
dynamic_cast<AnalysisPredictor *>(predictor.get());
auto &fuse_statis = analysis_predictor->analysis_argument()
@ -334,23 +334,16 @@ void TestDituRNNPrediction(const std::string &model_path,
// 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);
LOG(INFO) << "ditu rnn without analysis";
TestDituRNNPrediction(false, 1);
TestDituRNNPrediction(false, 4); // multi-threads
}
// 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);
TestDituRNNPrediction(true, 1);
TestDituRNNPrediction(true, 4); // multi-threads
}
} // namespace analysis

7
paddle/fluid/inference/api/analysis_predictor.cc
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@ -14,6 +14,8 @@
#include "paddle/fluid/inference/api/analysis_predictor.h"
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/framework/scope.h"
@ -30,7 +32,6 @@ bool AnalysisPredictor::Init(
} else {
place_ = paddle::platform::CPUPlace();
}
PADDLE_ENFORCE(!parent_scope);
if (parent_scope) {
scope_ = parent_scope;
sub_scope_ = &(parent_scope->NewScope());
@ -92,8 +93,6 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
Analyzer().Run(&argument_);
CHECK(argument_.transformed_program_desc);
VLOG(5) << "to prepare executor";
// LOG(INFO) << "transformed_parogram_desc " <<
// argument.transformed_program_desc->DebugString();
inference_program_.reset(
new framework::ProgramDesc(*argument_.transformed_program_desc));
PADDLE_ENFORCE(argument_.Has(framework::ir::kParamScopeAttr));
@ -106,7 +105,7 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
template <>
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
NativeConfig, PaddleEngineKind::kAnalysis>(const NativeConfig& config) {
VLOG(3) << "create NativePredictor";
VLOG(3) << "create AnalysisPredictor";
if (config.use_gpu) {
// 1. GPU memeroy
PADDLE_ENFORCE_GT(

41
paddle/fluid/inference/api/helper.h
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@ -14,6 +14,7 @@
#pragma once
#include <glog/logging.h>
#include <sys/time.h>
#include <algorithm>
#include <sstream>
@ -106,5 +107,45 @@ static void TensorAssignData(PaddleTensor *tensor,
}
}
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();
}
void print_time(int batch_size, int repeat, int num_threads, int tid,
double latency) {
LOG(INFO) << "batch_size: " << batch_size << ", repeat: " << repeat
<< ", threads: " << num_threads << ", thread id: " << tid
<< ", latency: " << latency << "ms";
}
} // namespace inference
} // namespace paddle

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