未验证 提交 9ee698e6 编写于 作者: Y Yan Chunwei 提交者: GitHub

enhance/ditu rnn with fc fuse (#12831)

* make fc fuse work with ditu rnn
* add ditu rnn data download to CMAKE
上级 78415f32
......@@ -104,7 +104,7 @@ std::map<ir::Node *, std::unordered_set<ir::Node *>> BuildOperationAdjList(
for (auto &adj_n : var->inputs) {
PADDLE_ENFORCE(adj_n->NodeType() == ir::Node::Type::kOperation);
adj_list[n].insert(adj_n);
VLOG(3) << "adj " << adj_n->Name() << reinterpret_cast<void *>(adj_n)
VLOG(4) << "adj " << adj_n->Name() << reinterpret_cast<void *>(adj_n)
<< " -> " << n->Name() << reinterpret_cast<void *>(n)
<< " via " << var->Name() << reinterpret_cast<void *>(var);
}
......
......@@ -22,7 +22,7 @@ function (inference_analysis_test TARGET)
if(WITH_TESTING)
set(options "")
set(oneValueArgs "")
set(multiValueArgs SRCS)
set(multiValueArgs SRCS EXTRA_DEPS)
cmake_parse_arguments(analysis_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(mem_opt "")
......@@ -31,22 +31,43 @@ function (inference_analysis_test TARGET)
endif()
cc_test(${TARGET}
SRCS "${analysis_test_SRCS}"
DEPS analysis graph fc_fuse_pass graph_viz_pass infer_clean_graph_pass graph_pattern_detecter pass
DEPS analysis graph fc_fuse_pass graph_viz_pass infer_clean_graph_pass graph_pattern_detecter pass ${analysis_test_EXTRA_DEPS}
ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model ${mem_opt})
set_tests_properties(${TARGET} PROPERTIES DEPENDS test_word2vec)
endif(WITH_TESTING)
endfunction(inference_analysis_test)
cc_test(test_analyzer SRCS analyzer_tester.cc DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis
set(DITU_RNN_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/ditu_rnn_fluid%2Fmodel.tar.gz")
set(DITU_RNN_DATA_URL "http://paddle-inference-dist.bj.bcebos.com/ditu_rnn_fluid%2Fdata.txt.tar.gz")
set(DITU_INSTALL_DIR "${THIRD_PARTY_PATH}/install/ditu_rnn" CACHE PATH "Ditu RNN model and data root." FORCE)
set(DITU_RNN_MODEL ${DITU_INSTALL_DIR}/model)
set(DITU_RNN_DATA ${DITU_INSTALL_DIR}/data.txt)
function (inference_download_and_uncompress target url gz_filename)
message(STATUS "Download inference test stuff ${gz_filename} from ${url}")
execute_process(COMMAND bash -c "mkdir -p ${DITU_INSTALL_DIR}")
execute_process(COMMAND bash -c "cd ${DITU_INSTALL_DIR} && wget -q ${url}")
execute_process(COMMAND bash -c "cd ${DITU_INSTALL_DIR} && tar xzf ${gz_filename}")
message(STATUS "finish downloading ${gz_filename}")
endfunction(inference_download_and_uncompress)
if (NOT EXISTS ${DITU_INSTALL_DIR})
inference_download_and_uncompress(ditu_rnn_model ${DITU_RNN_MODEL_URL} "ditu_rnn_fluid%2Fmodel.tar.gz")
inference_download_and_uncompress(ditu_rnn_data ${DITU_RNN_DATA_URL} "ditu_rnn_fluid%2Fdata.txt.tar.gz")
endif()
inference_analysis_test(test_analyzer SRCS analyzer_tester.cc
EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis
# ir
fc_fuse_pass
graph_viz_pass
infer_clean_graph_pass
graph_pattern_detecter
infer_clean_graph_pass
pass
ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model)
#set_tests_properties(test_analyzer PROPERTIES DEPENDS test_word2vec)
#inference_api_test(test_analyzer SRC analyzer_tester.cc ARGS test_word2vec)
ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model
--infer_ditu_rnn_model=${DITU_INSTALL_DIR}/model
--infer_ditu_rnn_data=${DITU_INSTALL_DIR}/data.txt)
inference_analysis_test(test_data_flow_graph SRCS data_flow_graph_tester.cc)
inference_analysis_test(test_data_flow_graph_to_fluid_pass SRCS data_flow_graph_to_fluid_pass_tester.cc)
......
......@@ -23,8 +23,6 @@
#include "paddle/fluid/inference/analysis/tensorrt_subgraph_node_mark_pass.h"
#include "paddle/fluid/inference/analysis/tensorrt_subgraph_pass.h"
namespace paddle {
DEFINE_bool(IA_enable_tensorrt_subgraph_engine, false,
"Enable subgraph to TensorRT engine for acceleration");
......@@ -35,6 +33,7 @@ DEFINE_string(IA_graphviz_log_root, "./",
DEFINE_string(IA_output_storage_path, "", "optimized model output path");
namespace paddle {
namespace inference {
namespace analysis {
......
......@@ -39,8 +39,6 @@ limitations under the License. */
#include "paddle/fluid/inference/analysis/pass.h"
#include "paddle/fluid/inference/analysis/pass_manager.h"
namespace paddle {
// TODO(Superjomn) add a definition flag like PADDLE_WITH_TENSORRT and hide this
// flag if not available.
DECLARE_bool(IA_enable_tensorrt_subgraph_engine);
......@@ -48,6 +46,7 @@ DECLARE_string(IA_graphviz_log_root);
DECLARE_string(IA_output_storage_path);
DECLARE_bool(IA_enable_ir);
namespace paddle {
namespace inference {
namespace analysis {
......
......@@ -13,11 +13,17 @@
// 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");
namespace paddle {
namespace inference {
namespace analysis {
......@@ -38,7 +44,7 @@ TEST(Analyzer, analysis_with_tensorrt) {
analyser.Run(&argument);
}
void TestWord2vecPrediction(const std::string& model_path) {
void TestWord2vecPrediction(const std::string &model_path) {
NativeConfig config;
config.model_dir = model_path;
config.use_gpu = false;
......@@ -69,12 +75,245 @@ void TestWord2vecPrediction(const std::string& model_path) {
// 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],
<< 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;
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) {
// DataRecord data(FLAGS_datapath, 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();
// clang-format off
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});
week_tensor.shape.assign({(int) one_batch.rnn_week_datas.size(), (int) one_batch.rnn_week_datas.front().size()});
week_tensor.lod.assign({one_batch.lod3});
minute_tensor.shape.assign({(int) one_batch.rnn_minute_datas.size(),
(int) one_batch.rnn_minute_datas.front().size()});
minute_tensor.lod.assign({one_batch.lod3});
// 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);
// clang-format on
// 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: ";
// clang-format off
int dim = std::accumulate(tensor.shape.begin(),
tensor.shape.end(),
1,
[](int a, int b) { return a * b; }); // clang-format on
for (size_t 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) << "time/batch: " << timer.toc() / num_times;
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 (int 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;
......@@ -94,6 +333,27 @@ TEST(Analyzer, SupportIRPass) {
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,
10, false, false);
}
// 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,
10, true, false, 1);
}
// 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,
10, true, true, 1);
}
} // namespace analysis
} // namespace inference
} // namespace paddle
......
......@@ -18,7 +18,10 @@ if(APPLE)
endif(APPLE)
set(inference_deps paddle_inference_api paddle_fluid_api)
set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager
graph_viz_pass fc_fuse_pass
infer_clean_graph_pass
)
if(WITH_GPU AND TENSORRT_FOUND)
set(inference_deps ${inference_deps} paddle_inference_tensorrt_subgraph_engine)
......
......@@ -137,9 +137,12 @@ bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
return false;
}
for (size_t i = 0; i < feed_target_names_.size(); ++i) {
VLOG(4) << "setting " << i << "-th target";
if (config_.specify_input_name) {
feed_targets[inputs[i].name] = &feeds[i];
} else {
feed_targets[feed_target_names_[i]] = &feeds[i];
}
}
// get fetch variable
std::map<std::string, framework::LoDTensor *> fetch_targets;
std::vector<framework::LoDTensor> fetchs;
......
// 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.
#pragma once
#include <sys/time.h>
#include <algorithm>
#include <sstream>
#include <string>
#include <vector>
#include "paddle/fluid/inference/api/paddle_inference_api.h"
namespace paddle {
namespace inference {
// Timer for timer
class Timer {
public:
double start;
double startu;
void tic() {
struct timeval tp;
gettimeofday(&tp, NULL);
start = tp.tv_sec;
startu = tp.tv_usec;
}
double toc() {
struct timeval tp;
gettimeofday(&tp, NULL);
double used_time_ms =
(tp.tv_sec - start) * 1000.0 + (tp.tv_usec - startu) / 1000.0;
return used_time_ms;
}
};
void split(const std::string &str, char sep, std::vector<std::string> *pieces) {
pieces->clear();
if (str.empty()) {
return;
}
size_t pos = 0;
size_t next = str.find(sep, pos);
while (next != std::string::npos) {
pieces->push_back(str.substr(pos, next - pos));
pos = next + 1;
next = str.find(sep, pos);
}
if (!str.substr(pos).empty()) {
pieces->push_back(str.substr(pos));
}
}
void split_to_float(const std::string &str, char sep, std::vector<float> *fs) {
std::vector<std::string> pieces;
split(str, sep, &pieces);
std::transform(pieces.begin(), pieces.end(), std::back_inserter(*fs),
[](const std::string &v) { return std::stof(v); });
}
template <typename T>
std::string to_string(const std::vector<T> &vec) {
std::stringstream ss;
for (const auto &c : vec) {
ss << c << " ";
}
return ss.str();
}
template <>
std::string to_string<std::vector<float>>(
const std::vector<std::vector<float>> &vec) {
std::stringstream ss;
for (const auto &piece : vec) {
ss << to_string(piece) << "\n";
}
return ss.str();
}
template <>
std::string to_string<std::vector<std::vector<float>>>(
const std::vector<std::vector<std::vector<float>>> &vec) {
std::stringstream ss;
for (const auto &line : vec) {
for (const auto &rcd : line) {
ss << to_string(rcd) << ";\t";
}
ss << '\n';
}
return ss.str();
}
// clang-format off
void TensorAssignData(PaddleTensor *tensor, const std::vector<std::vector<float>> &data) {
// Assign buffer
int dim = std::accumulate(tensor->shape.begin(), tensor->shape.end(), 1, [](int a, int b) { return a * b; });
tensor->data.Resize(sizeof(float) * dim);
int c = 0;
for (const auto &f : data) {
for (float v : f) { static_cast<float *>(tensor->data.data())[c++] = v; }
}
}
} // namespace inference
} // namespace paddle
......@@ -120,6 +120,8 @@ struct NativeConfig : public PaddlePredictor::Config {
bool use_gpu{false};
int device{0};
float fraction_of_gpu_memory{-1.f}; // Negative to notify initialization.
// Specify the variable's name of each input.
bool specify_input_name{false};
std::string prog_file;
std::string param_file;
......
......@@ -54,9 +54,9 @@ class MulOp : public framework::OperatorWithKernel {
auto x_mat_dims = framework::flatten_to_2d(x_dims, x_num_col_dims);
auto y_mat_dims = framework::flatten_to_2d(y_dims, y_num_col_dims);
PADDLE_ENFORCE_EQ(
x_mat_dims[1], y_mat_dims[0],
"First matrix's width must be equal with second matrix's height.");
PADDLE_ENFORCE_EQ(x_mat_dims[1], y_mat_dims[0],
"First matrix's width must be equal with second matrix's "
"height. %s, %s");
std::vector<int64_t> output_dims;
output_dims.reserve(
static_cast<size_t>(x_num_col_dims + y_dims.size() - y_num_col_dims));
......
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