未验证 提交 a89afd4c 编写于 作者: T Tao Luo 提交者: GitHub

Merge pull request #13685 from luotao1/naive_cmake

update libpaddle_fluid.a/so
set(pass_file ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h)
file(WRITE ${pass_file} "// Generated by the paddle/fluid/framework/ir/CMakeLists.txt. DO NOT EDIT!\n\n")
file(APPEND ${pass_file} "\#pragma once\n")
file(APPEND ${pass_file} "\#include \"paddle/fluid/framework/ir/pass.h\"\n")
......
......@@ -20,7 +20,8 @@ cc_library(paddle_fluid_origin DEPS ${fluid_modules} paddle_fluid_api)
add_subdirectory(api)
# Create static library
cc_library(paddle_fluid DEPS ${fluid_modules} paddle_fluid_api paddle_inference_api analysis_predictor)
cc_library(paddle_fluid DEPS ${fluid_modules} paddle_fluid_api paddle_inference_api
analysis_predictor zero_copy_tensor)
if(NOT APPLE)
# TODO(liuyiqu: Temporarily disable the link flag because it is not support on Mac.
set(LINK_FLAGS "-Wl,--retain-symbols-file ${CMAKE_CURRENT_SOURCE_DIR}/paddle_fluid.sym")
......@@ -31,6 +32,7 @@ endif()
cc_library(paddle_fluid_shared SHARED
SRCS io.cc ${CMAKE_CURRENT_SOURCE_DIR}/api/api.cc ${CMAKE_CURRENT_SOURCE_DIR}/api/api_impl.cc
${CMAKE_CURRENT_SOURCE_DIR}/api/analysis_predictor.cc
${CMAKE_CURRENT_SOURCE_DIR}/api/details/zero_copy_tensor.cc
DEPS ${fluid_modules} paddle_fluid_api)
set_target_properties(paddle_fluid_shared PROPERTIES OUTPUT_NAME paddle_fluid)
......
......@@ -24,7 +24,6 @@
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/api/paddle_inference_pass.h"
#include "paddle/fluid/inference/api/timer.h"
#include "paddle/fluid/inference/utils/singleton.h"
#include "paddle/fluid/platform/profiler.h"
......
......@@ -23,7 +23,6 @@ limitations under the License. */
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/inference/api/api_impl.h"
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/inference/api/timer.h"
#include "paddle/fluid/platform/profiler.h"
DEFINE_bool(profile, false, "Turn on profiler for fluid");
......
......@@ -16,19 +16,34 @@
#include <glog/logging.h>
#include <sys/time.h>
#include <algorithm>
#include <chrono> // NOLINT
#include <numeric>
#include <sstream>
#include <string>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/api/timer.h"
#include "paddle/fluid/string/printf.h"
#include "paddle_inference_api.h"
namespace paddle {
namespace inference {
// Timer for timer
class Timer {
public:
std::chrono::high_resolution_clock::time_point start;
std::chrono::high_resolution_clock::time_point startu;
void tic() { start = std::chrono::high_resolution_clock::now(); }
double toc() {
startu = std::chrono::high_resolution_clock::now();
std::chrono::duration<double> time_span =
std::chrono::duration_cast<std::chrono::duration<double>>(startu -
start);
double used_time_ms = static_cast<double>(time_span.count()) * 1000.0;
return used_time_ms;
}
};
static void split(const std::string &str, char sep,
std::vector<std::string> *pieces) {
pieces->clear();
......@@ -154,127 +169,5 @@ static void PrintTime(int batch_size, int repeat, int num_threads, int tid,
}
}
template <typename T>
std::string LoDTensorSummary(const framework::LoDTensor &tensor) {
std::stringstream ss;
ss << "\n---- tensor ---" << '\n';
ss << "lod: [";
for (const auto &level : tensor.lod()) {
ss << "[ ";
for (auto i : level) {
ss << i << ", ";
}
ss << "]";
}
ss << "]\n";
ss << "shape: [";
int size = 1;
for (int i = 0; i < tensor.dims().size(); i++) {
int dim = tensor.dims()[i];
ss << dim << ", ";
size *= dim;
}
ss << "]\n";
ss << "data: ";
for (int i = 0; i < std::min(20, size); i++) {
ss << tensor.data<T>()[i] << " ";
}
ss << "\n";
return ss.str();
}
static bool CompareLoD(const framework::LoD &a, const framework::LoD &b) {
if (a.size() != b.size()) {
LOG(ERROR) << string::Sprintf("lod size not match %d != %d", a.size(),
b.size());
return false;
}
for (size_t i = 0; i < a.size(); i++) {
auto &al = a[i];
auto &bl = b[i];
if (al.size() != bl.size()) {
LOG(ERROR) << string::Sprintf("level size %d != %d", al.size(),
bl.size());
return false;
}
}
return true;
}
static bool CompareShape(const std::vector<int64_t> &a,
const std::vector<int64_t> &b) {
if (a.size() != b.size()) {
LOG(ERROR) << string::Sprintf("shape size not match %d != %d", a.size(),
b.size());
return false;
}
for (size_t i = 0; i < a.size(); i++) {
if (a[i] != b[i]) {
LOG(ERROR) << string::Sprintf("shape %d-th element not match %d != %d", i,
a[i], b[i]);
return false;
}
}
return true;
}
static bool CompareTensorData(const framework::LoDTensor &a,
const framework::LoDTensor &b) {
auto a_shape = framework::vectorize(a.dims());
auto b_shape = framework::vectorize(b.dims());
size_t a_size = std::accumulate(a_shape.begin(), a_shape.end(), 1,
[](int a, int b) { return a * b; });
size_t b_size = std::accumulate(b_shape.begin(), b_shape.end(), 1,
[](int a, int b) { return a * b; });
if (a_size != b_size) {
LOG(ERROR) << string::Sprintf("tensor data size not match, %d != %d",
a_size, b_size);
}
for (size_t i = 0; i < a_size; i++) {
if (a.type() == typeid(float)) {
const auto *a_data = a.data<float>();
const auto *b_data = b.data<float>();
if (std::abs(a_data[i] - b_data[i]) > 1e-3) {
LOG(ERROR) << string::Sprintf(
"tensor data %d-th element not match, %f != %f", i, a_data[i],
b_data[i]);
return false;
}
} else if (a.type() == typeid(int64_t)) {
const auto *a_data = a.data<int64_t>();
const auto *b_data = b.data<int64_t>();
if (std::abs(a_data[i] - b_data[i]) > 1e-3) {
LOG(ERROR) << string::Sprintf(
"tensor data %d-th element not match, %f != %f", i, a_data[i],
b_data[i]);
return false;
}
}
}
return true;
}
static bool CompareTensor(const framework::LoDTensor &a,
const framework::LoDTensor &b) {
if (!CompareLoD(a.lod(), b.lod())) {
return false;
}
if (!CompareShape(framework::vectorize(a.dims()),
framework::vectorize(b.dims()))) {
return false;
}
if (!CompareTensorData(a, b)) {
return false;
}
return true;
}
} // namespace inference
} // namespace paddle
......@@ -268,9 +268,8 @@ struct AnalysisConfig : public NativeConfig {
// NOT stable yet.
bool use_feed_fetch_ops{true};
// NOTE this is just for internal development, please not use it. NOT
// stable
// yet.
// NOTE this is just for internal development, please not use it.
// NOT stable yet.
bool _use_mkldnn{false};
};
......
// 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 <chrono> // NOLINT
namespace paddle {
namespace inference {
// Timer for timer
class Timer {
public:
std::chrono::high_resolution_clock::time_point start;
std::chrono::high_resolution_clock::time_point startu;
void tic() { start = std::chrono::high_resolution_clock::now(); }
double toc() {
startu = std::chrono::high_resolution_clock::now();
std::chrono::duration<double> time_span =
std::chrono::duration_cast<std::chrono::duration<double>>(startu -
start);
double used_time_ms = static_cast<double>(time_span.count()) * 1000.0;
return used_time_ms;
}
};
} // namespace inference
} // namespace paddle
......@@ -22,7 +22,6 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/api/timer.h"
#include "utils/logger/logger.h"
DEFINE_string(model, "", "Directory of the inference model.");
......
......@@ -12,7 +12,6 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/inference/api/analysis_predictor.h"
#include "paddle/fluid/inference/tests/api/tester_helper.h"
DEFINE_bool(with_precision_check, true, "turn on test");
......
......@@ -15,6 +15,7 @@
#pragma once
#include <gtest/gtest.h>
#include <algorithm>
#include <string>
#include <thread> // NOLINT
#include <vector>
......@@ -182,5 +183,127 @@ void CompareNativeAndAnalysis(
CompareResult(analysis_outputs, native_outputs);
}
template <typename T>
std::string LoDTensorSummary(const framework::LoDTensor &tensor) {
std::stringstream ss;
ss << "\n---- tensor ---" << '\n';
ss << "lod: [";
for (const auto &level : tensor.lod()) {
ss << "[ ";
for (auto i : level) {
ss << i << ", ";
}
ss << "]";
}
ss << "]\n";
ss << "shape: [";
int size = 1;
for (int i = 0; i < tensor.dims().size(); i++) {
int dim = tensor.dims()[i];
ss << dim << ", ";
size *= dim;
}
ss << "]\n";
ss << "data: ";
for (int i = 0; i < std::min(20, size); i++) {
ss << tensor.data<T>()[i] << " ";
}
ss << "\n";
return ss.str();
}
static bool CompareLoD(const framework::LoD &a, const framework::LoD &b) {
if (a.size() != b.size()) {
LOG(ERROR) << string::Sprintf("lod size not match %d != %d", a.size(),
b.size());
return false;
}
for (size_t i = 0; i < a.size(); i++) {
auto &al = a[i];
auto &bl = b[i];
if (al.size() != bl.size()) {
LOG(ERROR) << string::Sprintf("level size %d != %d", al.size(),
bl.size());
return false;
}
}
return true;
}
static bool CompareShape(const std::vector<int64_t> &a,
const std::vector<int64_t> &b) {
if (a.size() != b.size()) {
LOG(ERROR) << string::Sprintf("shape size not match %d != %d", a.size(),
b.size());
return false;
}
for (size_t i = 0; i < a.size(); i++) {
if (a[i] != b[i]) {
LOG(ERROR) << string::Sprintf("shape %d-th element not match %d != %d", i,
a[i], b[i]);
return false;
}
}
return true;
}
static bool CompareTensorData(const framework::LoDTensor &a,
const framework::LoDTensor &b) {
auto a_shape = framework::vectorize(a.dims());
auto b_shape = framework::vectorize(b.dims());
size_t a_size = std::accumulate(a_shape.begin(), a_shape.end(), 1,
[](int a, int b) { return a * b; });
size_t b_size = std::accumulate(b_shape.begin(), b_shape.end(), 1,
[](int a, int b) { return a * b; });
if (a_size != b_size) {
LOG(ERROR) << string::Sprintf("tensor data size not match, %d != %d",
a_size, b_size);
}
for (size_t i = 0; i < a_size; i++) {
if (a.type() == typeid(float)) {
const auto *a_data = a.data<float>();
const auto *b_data = b.data<float>();
if (std::abs(a_data[i] - b_data[i]) > 1e-3) {
LOG(ERROR) << string::Sprintf(
"tensor data %d-th element not match, %f != %f", i, a_data[i],
b_data[i]);
return false;
}
} else if (a.type() == typeid(int64_t)) {
const auto *a_data = a.data<int64_t>();
const auto *b_data = b.data<int64_t>();
if (std::abs(a_data[i] - b_data[i]) > 1e-3) {
LOG(ERROR) << string::Sprintf(
"tensor data %d-th element not match, %f != %f", i, a_data[i],
b_data[i]);
return false;
}
}
}
return true;
}
static bool CompareTensor(const framework::LoDTensor &a,
const framework::LoDTensor &b) {
if (!CompareLoD(a.lod(), b.lod())) {
return false;
}
if (!CompareShape(framework::vectorize(a.dims()),
framework::vectorize(b.dims()))) {
return false;
}
if (!CompareTensorData(a, b)) {
return false;
}
return true;
}
} // namespace inference
} // namespace paddle
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