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提交 f1426f03 编写于 作者: L liuruilong
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add log print module

上级 0c9279af ffb6447c
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Showing with 19617 addition and 16783 deletion
.clang-format 0 → 100644
浏览文件 @ f1426f03
---
Language: Cpp
BasedOnStyle: LLVM
Standard: Cpp11
IndentWidth: 4
NamespaceIndentation: All
...
.pre-commit-config.yaml
浏览文件 @ f1426f03
......@@ -6,6 +6,7 @@ repos:
files: (src).*\.(md|py|mm|swift|java|c|cc|cxx|cpp|cu|h|hpp|hxx)$
- id: remove-tabs
files: (src).*\.(md|py|mm|swift|java|c|cc|cxx|cpp|cu|h|hpp|hxx)$
- repo: https://github.com/pre-commit/pre-commit-hooks
sha: 5bf6c09bfa1297d3692cadd621ef95f1284e33c0
hooks:
......@@ -18,11 +19,21 @@ repos:
files: (src).*\.(md|py|mm|swift|java|c|cc|cxx|cpp|cu|h|hpp|hxx)$
- id: trailing-whitespace
files: (src).*\.(md|py|mm|swift|java|c|cc|cxx|cpp|cu|h|hpp|hxx)$
- repo: local
hooks:
- id: clang-format-with-version-check
name: clang-format
description: Format files with ClangFormat.
entry: bash .clang_format.hook -i
entry: bash ./tools/pre-commit.hooks/.clang_format.hook -i
language: system
files: (src).*\.(c|cc|cxx|cpp|h|hpp|hxx)$
#- repo: local
# hooks:
# - id: copyright_checker
# name: copyright_checker
# entry: python ./tools/pre-commit.hooks/.copyright.hook
# language: system
# files: (src).*\.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto|py)$
# exclude: (?!.*third_party)^.*$ | (?!.*book)^.*$
CMakeLists.txt
浏览文件 @ f1426f03
......@@ -46,7 +46,8 @@ target_link_libraries(paddle-mobile-static protobuf-lite openblas)
add_dependencies(paddle-mobile openblas_proj)
# gen test
ADD_EXECUTABLE(paddle-mobile-test test/main.cpp test/test_helper.h)
ADD_EXECUTABLE(paddle-mobile-test test/main.cpp test/test_helper.h
test/elementwise_add_op_test.h test/test_include.h)
target_link_libraries(paddle-mobile-test paddle-mobile)
# gen test log
......
README.md
浏览文件 @ f1426f03
# Paddle-Mobile
# Paddle-Mobile
![License MIT](https://img.shields.io/github/license/mashape/apistatus.svg) [![Build Status](https://travis-ci.org/PaddlePaddle/paddle-mobile.svg?branch=develop&longCache=true&style=flat-square)](https://travis-ci.org/PaddlePaddle/paddle-mobile)
This project is used to develop the next version deep learning freamwork for mobile device.
......
src/common/type_define.h
浏览文件 @ f1426f03
......@@ -23,30 +23,31 @@ SOFTWARE.
namespace paddle_mobile {
namespace framework {
template <typename Dtype> class OperatorBase;
class OpDesc;
class BlockDesc;
class InferShapeContext;
}
using VariableNameMap = std::map<std::string, std::vector<std::string>>;
template <typename Dtype>
using OpCreator = std::function<framework::OperatorBase<Dtype> *(
const std::string & /*type*/, const VariableNameMap & /*inputs*/,
const VariableNameMap & /*outputs*/,
const framework::AttributeMap & /*attrs*/)>;
using GradOpMakerFN =
std::function<std::vector<std::unique_ptr<framework::OpDesc>>(
const framework::OpDesc &,
const std::unordered_set<std::string> & /*no_grad_set*/,
std::unordered_map<std::string, std::string> * /*grad_to_var*/,
const std::vector<framework::BlockDesc *> &grad_block)>;
using InferVarTypeFN = std::function<void(const framework::OpDesc & /*op_desc*/,
framework::BlockDesc * /*block*/)>;
using InferShapeFN = std::function<void(framework::InferShapeContext *)>;
namespace framework {
template <typename Dtype> class OperatorBase;
class OpDesc;
class BlockDesc;
class InferShapeContext;
}
using VariableNameMap = std::map<std::string, std::vector<std::string>>;
template <typename Dtype>
using OpCreator = std::function<framework::OperatorBase<Dtype> *(
const std::string & /*type*/, const VariableNameMap & /*inputs*/,
const VariableNameMap & /*outputs*/,
const framework::AttributeMap & /*attrs*/)>;
using GradOpMakerFN =
std::function<std::vector<std::unique_ptr<framework::OpDesc>>(
const framework::OpDesc &,
const std::unordered_set<std::string> & /*no_grad_set*/,
std::unordered_map<std::string, std::string> * /*grad_to_var*/,
const std::vector<framework::BlockDesc *> &grad_block)>;
using InferVarTypeFN =
std::function<void(const framework::OpDesc & /*op_desc*/,
framework::BlockDesc * /*block*/)>;
using InferShapeFN = std::function<void(framework::InferShapeContext *)>;
};
src/common/types.h
浏览文件 @ f1426f03
......@@ -19,45 +19,45 @@ SOFTWARE.
#pragma once;
namespace paddle_mobile {
enum class Precision : int { FP32 = 0 };
enum class Precision : int { FP32 = 0 };
//! device type
enum DeviceTypeEnum { kINVALID = -1, kCPU = 0, kFPGA = 1, kGPU_MALI = 2 };
//! device type
enum DeviceTypeEnum { kINVALID = -1, kCPU = 0, kFPGA = 1, kGPU_MALI = 2 };
template <DeviceTypeEnum T> struct DeviceType {};
template <DeviceTypeEnum T> struct DeviceType {};
typedef DeviceType<kCPU> CPU;
typedef DeviceType<kFPGA> FPGA;
typedef DeviceType<kGPU_MALI> GPU_MALI;
typedef DeviceType<kCPU> CPU;
typedef DeviceType<kFPGA> FPGA;
typedef DeviceType<kGPU_MALI> GPU_MALI;
//! data type
enum DataType {
PM_INVALID = -1,
PM_HALF = 0,
PM_FLOAT = 1,
PM_DOUBLE = 2,
PM_INT8 = 3,
PM_INT16 = 4,
PM_INT32 = 5,
PM_INT64 = 6,
PM_UINT8 = 7,
PM_UINT16 = 8,
PM_UINT32 = 9,
PM_STRING = 10,
PM_BOOL = 11,
PM_SHAPE = 12,
PM_TENSOR = 13
};
//!
enum PMStatus {
PMSuccess = 0xFF, /*!< No errors */
PMNotInitialized = 0x01, /*!< Data not initialized. */
PMInvalidValue = 0x02, /*!< Incorrect variable value. */
PMMemAllocFailed = 0x03, /*!< Memory allocation error. */
PMUnKownError = 0x04, /*!< Unknown error. */
PMOutOfAuthority = 0x05, /*!< Try to modified data not your own*/
PMOutOfMem = 0x06, /*!< OOM error*/
PMUnImplError = 0x07, /*!< Unimplement error. */
PMWrongDevice = 0x08 /*!< un-correct device. */
};
//! data type
enum DataType {
PM_INVALID = -1,
PM_HALF = 0,
PM_FLOAT = 1,
PM_DOUBLE = 2,
PM_INT8 = 3,
PM_INT16 = 4,
PM_INT32 = 5,
PM_INT64 = 6,
PM_UINT8 = 7,
PM_UINT16 = 8,
PM_UINT32 = 9,
PM_STRING = 10,
PM_BOOL = 11,
PM_SHAPE = 12,
PM_TENSOR = 13
};
//!
enum PMStatus {
PMSuccess = 0xFF, /*!< No errors */
PMNotInitialized = 0x01, /*!< Data not initialized. */
PMInvalidValue = 0x02, /*!< Incorrect variable value. */
PMMemAllocFailed = 0x03, /*!< Memory allocation error. */
PMUnKownError = 0x04, /*!< Unknown error. */
PMOutOfAuthority = 0x05, /*!< Try to modified data not your own*/
PMOutOfMem = 0x06, /*!< OOM error*/
PMUnImplError = 0x07, /*!< Unimplement error. */
PMWrongDevice = 0x08 /*!< un-correct device. */
};
}
src/common/variant.h
浏览文件 @ f1426f03
......@@ -21,79 +21,79 @@ SOFTWARE.
#pragma once
namespace paddle_mobile {
template <int ID, typename Type> struct IDToType { typedef Type type_t; };
template <int ID, typename Type> struct IDToType { typedef Type type_t; };
template <typename F, typename... Ts> struct VariantHelper {
static const size_t size = sizeof(F) > VariantHelper<Ts...>::size
? sizeof(F)
: VariantHelper<Ts...>::size;
template <typename F, typename... Ts> struct VariantHelper {
static const size_t size = sizeof(F) > VariantHelper<Ts...>::size
? sizeof(F)
: VariantHelper<Ts...>::size;
inline static void Destroy(size_t id, void *data) {
if (id == typeid(F).hash_code()) {
reinterpret_cast<F *>(data)->~F();
} else {
VariantHelper<Ts...>::Destroy(id, data);
}
}
};
inline static void Destroy(size_t id, void *data) {
if (id == typeid(F).hash_code()) {
reinterpret_cast<F *>(data)->~F();
} else {
VariantHelper<Ts...>::Destroy(id, data);
}
}
};
template <typename F> struct VariantHelper<F> {
static const size_t size = sizeof(F);
inline static void Destroy(size_t id, void *data) {
if (id == typeid(F).hash_code()) {
// reinterpret_cast<F*>(data)->~F();
} else {
// std::cout << "未匹配到 " << std::endl;
}
}
};
template <typename F> struct VariantHelper<F> {
static const size_t size = sizeof(F);
inline static void Destroy(size_t id, void *data) {
if (id == typeid(F).hash_code()) {
// reinterpret_cast<F*>(data)->~F();
} else {
// std::cout << "未匹配到 " << std::endl;
}
}
};
template <size_t size> class RawData {
public:
char data[size];
RawData() {}
RawData(const RawData &raw_data) { strcpy(data, raw_data.data); }
// void operator=(const RawData &raw_data){
// strcpy(data, raw_data.data);
// }
};
template <size_t size> class RawData {
public:
char data[size];
RawData() {}
RawData(const RawData &raw_data) { strcpy(data, raw_data.data); }
// void operator=(const RawData &raw_data){
// strcpy(data, raw_data.data);
// }
};
template <typename... Ts> struct Variant {
Variant(const Variant &variant) {
// std::cout << " 赋值构造函数 " << std::endl;
type_id = variant.type_id;
data = variant.data;
}
template <typename... Ts> struct Variant {
Variant(const Variant &variant) {
// std::cout << " 赋值构造函数 " << std::endl;
type_id = variant.type_id;
data = variant.data;
}
Variant() : type_id(invalid_type()) {}
~Variant() {
// helper::Destroy(type_id, &data);
}
Variant() : type_id(invalid_type()) {}
~Variant() {
// helper::Destroy(type_id, &data);
}
template <typename T, typename... Args> void Set(Args &&... args) {
helper::Destroy(type_id, &data);
new (&data) T(std::forward<Args>(args)...);
type_id = typeid(T).hash_code();
}
template <typename T, typename... Args> void Set(Args &&... args) {
helper::Destroy(type_id, &data);
new (&data) T(std::forward<Args>(args)...);
type_id = typeid(T).hash_code();
}
template <typename T> T &Get() const {
if (type_id == typeid(T).hash_code()) {
return *const_cast<T *>(reinterpret_cast<const T *>(&data));
} else {
// std::cout << " bad cast in variant " << std::endl;
throw std::bad_cast();
}
}
template <typename T> T &Get() const {
if (type_id == typeid(T).hash_code()) {
return *const_cast<T *>(reinterpret_cast<const T *>(&data));
} else {
// std::cout << " bad cast in variant " << std::endl;
throw std::bad_cast();
}
}
size_t TypeId() const { return type_id; }
size_t TypeId() const { return type_id; }
private:
static inline size_t invalid_type() { return typeid(void).hash_code(); }
typedef VariantHelper<Ts...> helper;
size_t type_id;
RawData<helper::size> data;
};
private:
static inline size_t invalid_type() { return typeid(void).hash_code(); }
typedef VariantHelper<Ts...> helper;
size_t type_id;
RawData<helper::size> data;
};
template <typename T> struct Vistor { typedef T type_t; };
template <typename T> struct Vistor { typedef T type_t; };
} // namespace paddle_mobile
src/framework/attribute.cpp
浏览文件 @ f1426f03
......@@ -19,5 +19,5 @@ SOFTWARE.
#include "attribute.h"
namespace paddle_mobile {
namespace framework {}
namespace framework {}
} // namespace paddle_mobile
src/framework/attribute.h
浏览文件 @ f1426f03
......@@ -22,107 +22,110 @@ SOFTWARE.
#include "framework.pb.h"
namespace paddle_mobile {
namespace framework {
namespace framework {
class BlockDesc;
class BlockDesc;
class Attribute {
public:
static Attribute GetAttrValue(const proto::OpDesc::Attr &attr_desc) {
// std::cout << "begin get attr value" << std::endl;
Attribute attr;
switch (attr_desc.type()) {
case proto::AttrType::BOOLEAN: {
attr.Set<bool>(attr_desc.b());
break;
}
case proto::AttrType::INT: {
attr.Set<int>(attr_desc.i());
break;
}
case proto::AttrType::FLOAT: {
attr.Set<float>(attr_desc.f());
break;
}
case proto::AttrType::STRING: {
attr.Set<std::string>(attr_desc.s());
break;
}
case proto::AttrType::BOOLEANS: {
std::vector<bool> val(attr_desc.bools_size());
for (int i = 0; i < attr_desc.bools_size(); ++i) {
val[i] = attr_desc.bools(i);
}
attr.Set<std::vector<bool>>(val);
break;
}
case proto::AttrType::INTS: {
std::vector<int> val(attr_desc.ints_size());
for (int i = 0; i < attr_desc.ints_size(); ++i) {
val[i] = attr_desc.ints(i);
}
attr.Set<std::vector<int>>(val);
break;
}
case proto::AttrType::FLOATS: {
std::vector<float> val(attr_desc.floats_size());
for (int i = 0; i < attr_desc.floats_size(); ++i) {
val[i] = attr_desc.floats(i);
}
attr.Set<std::vector<float>>(val);
break;
}
case proto::AttrType::STRINGS: {
std::vector<std::string> val(attr_desc.strings_size());
for (int i = 0; i < attr_desc.strings_size(); ++i) {
val[i] = attr_desc.strings(i);
}
attr.Set<std::vector<std::string>>(val);
break;
}
case proto::AttrType::LONG: {
attr.Set<int64_t>(attr_desc.l());
break;
}
default:
// std::cout << " not support " << std::endl;
break;
}
// std::cout << "end get attr value" << std::endl;
return attr;
}
class Attribute {
public:
static Attribute
GetAttrValue(const proto::OpDesc::Attr &attr_desc) {
// std::cout << "begin get attr value" << std::endl;
Attribute attr;
switch (attr_desc.type()) {
case proto::AttrType::BOOLEAN: {
attr.Set<bool>(attr_desc.b());
break;
}
case proto::AttrType::INT: {
attr.Set<int>(attr_desc.i());
break;
}
case proto::AttrType::FLOAT: {
attr.Set<float>(attr_desc.f());
break;
}
case proto::AttrType::STRING: {
attr.Set<std::string>(attr_desc.s());
break;
}
case proto::AttrType::BOOLEANS: {
std::vector<bool> val(attr_desc.bools_size());
for (int i = 0; i < attr_desc.bools_size(); ++i) {
val[i] = attr_desc.bools(i);
}
attr.Set<std::vector<bool>>(val);
break;
}
case proto::AttrType::INTS: {
std::vector<int> val(attr_desc.ints_size());
for (int i = 0; i < attr_desc.ints_size(); ++i) {
val[i] = attr_desc.ints(i);
}
attr.Set<std::vector<int>>(val);
break;
}
case proto::AttrType::FLOATS: {
std::vector<float> val(attr_desc.floats_size());
for (int i = 0; i < attr_desc.floats_size(); ++i) {
val[i] = attr_desc.floats(i);
}
attr.Set<std::vector<float>>(val);
break;
}
case proto::AttrType::STRINGS: {
std::vector<std::string> val(attr_desc.strings_size());
for (int i = 0; i < attr_desc.strings_size(); ++i) {
val[i] = attr_desc.strings(i);
}
attr.Set<std::vector<std::string>>(val);
break;
}
case proto::AttrType::LONG: {
attr.Set<int64_t>(attr_desc.l());
break;
}
default:
// std::cout << " not support " << std::endl;
break;
}
// std::cout << "end get attr value" << std::endl;
return attr;
}
Attribute() {}
template <typename T, typename... Args> Attribute &Set(Args &&... args) {
variant_.Set<T>(args...);
return *this;
}
Attribute() {}
template <typename T, typename... Args>
Attribute &Set(Args &&... args) {
variant_.Set<T>(args...);
return *this;
}
template <typename T> T &Get() const { return variant_.Get<T>(); }
template <typename T> T &Get() const { return variant_.Get<T>(); }
private:
Variant<int, float, std::string, std::vector<int>, std::vector<float>,
std::vector<std::string>, bool, std::vector<bool>, BlockDesc *,
int64_t>
variant_;
};
private:
Variant<int, float, std::string, std::vector<int>,
std::vector<float>, std::vector<std::string>, bool,
std::vector<bool>, BlockDesc *, int64_t>
variant_;
};
using AttributeMap = std::unordered_map<std::string, Attribute>;
using AttributeMap = std::unordered_map<std::string, Attribute>;
class AttrReader {
public:
explicit AttrReader(const AttributeMap &attrs) : attrs_(attrs) {}
class AttrReader {
public:
explicit AttrReader(const AttributeMap &attrs) : attrs_(attrs) {}
template <typename T> inline T Get(const std::string &name) const {
// PADDLE_ENFORCE(attrs_.count(name) != 0, "%s should be in
// AttributeMap",
// name);
return ((Attribute)attrs_.at(name)).Get<T>();
}
template <typename T> inline T Get(const std::string &name) const {
// PADDLE_ENFORCE(attrs_.count(name) != 0, "%s should
// be in
// AttributeMap",
// name);
return ((Attribute)attrs_.at(name)).Get<T>();
}
private:
const AttributeMap &attrs_;
};
private:
const AttributeMap &attrs_;
};
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/block_desc.cpp
浏览文件 @ f1426f03
......@@ -19,32 +19,32 @@ SOFTWARE.
#include "block_desc.h"
namespace paddle_mobile {
namespace framework {
namespace framework {
std::vector<std::shared_ptr<VarDesc>> BlockDesc::Vars() const {
std::vector<std::shared_ptr<VarDesc>> res;
for (const auto &p : vars_) {
res.push_back(p.second);
}
return res;
}
std::vector<std::shared_ptr<VarDesc>> BlockDesc::Vars() const {
std::vector<std::shared_ptr<VarDesc>> res;
for (const auto &p : vars_) {
res.push_back(p.second);
}
return res;
}
std::vector<std::shared_ptr<OpDesc>> BlockDesc::Ops() const {
std::vector<std::shared_ptr<OpDesc>> res;
for (const auto &op : ops_) {
res.push_back(op);
}
return res;
}
std::vector<std::shared_ptr<OpDesc>> BlockDesc::Ops() const {
std::vector<std::shared_ptr<OpDesc>> res;
for (const auto &op : ops_) {
res.push_back(op);
}
return res;
}
BlockDesc::BlockDesc(const proto::BlockDesc &desc) : desc_(desc) {
for (const proto::VarDesc &var_desc : desc_.vars()) {
vars_[var_desc.name()].reset(new VarDesc(var_desc));
}
for (const proto::OpDesc &op_desc : desc_.ops()) {
ops_.emplace_back(new framework::OpDesc(op_desc));
}
}
BlockDesc::BlockDesc(const proto::BlockDesc &desc) : desc_(desc) {
for (const proto::VarDesc &var_desc : desc_.vars()) {
vars_[var_desc.name()].reset(new VarDesc(var_desc));
}
for (const proto::OpDesc &op_desc : desc_.ops()) {
ops_.emplace_back(new framework::OpDesc(op_desc));
}
}
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/block_desc.h
浏览文件 @ f1426f03
......@@ -24,46 +24,50 @@ SOFTWARE.
#include "var_desc.h"
namespace paddle_mobile {
namespace framework {
namespace framework {
class BlockDesc : PaddleMobileObject {
public:
BlockDesc(const proto::BlockDesc &desc);
class BlockDesc : PaddleMobileObject {
public:
BlockDesc(const proto::BlockDesc &desc);
const int &ID() const { return desc_.idx(); }
const int &ID() const { return desc_.idx(); }
const int &Parent() const { return desc_.parent_idx(); }
const int &Parent() const { return desc_.parent_idx(); }
bool operator==(const paddle_mobile::framework::BlockDesc &in_block) const {
return this->ID() == in_block.ID() && this->Parent() == in_block.Parent();
}
bool operator==(
const paddle_mobile::framework::BlockDesc &in_block) const {
return this->ID() == in_block.ID() &&
this->Parent() == in_block.Parent();
}
bool operator<(const paddle_mobile::framework::BlockDesc &in_block) const {
return this->ID() < in_block.ID() && this->Parent() < in_block.Parent();
}
bool operator<(
const paddle_mobile::framework::BlockDesc &in_block) const {
return this->ID() < in_block.ID() &&
this->Parent() < in_block.Parent();
}
std::vector<std::shared_ptr<VarDesc>> Vars() const;
std::vector<std::shared_ptr<OpDesc>> Ops() const;
std::vector<std::shared_ptr<VarDesc>> Vars() const;
std::vector<std::shared_ptr<OpDesc>> Ops() const;
private:
proto::BlockDesc desc_;
std::vector<std::shared_ptr<OpDesc>> ops_;
std::unordered_map<std::string, std::shared_ptr<VarDesc>> vars_;
};
private:
proto::BlockDesc desc_;
std::vector<std::shared_ptr<OpDesc>> ops_;
std::unordered_map<std::string, std::shared_ptr<VarDesc>> vars_;
};
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
namespace std {
template <> struct hash<paddle_mobile::framework::BlockDesc> {
typedef paddle_mobile::framework::BlockDesc argument_type;
typedef std::size_t result_type;
result_type operator()(argument_type const &s) const noexcept {
result_type const h1(std::hash<int>{}(s.ID()));
result_type const h2(std::hash<int>{}(s.ID()));
return h1 ^ (h2 << 1);
}
};
template <> struct hash<paddle_mobile::framework::BlockDesc> {
typedef paddle_mobile::framework::BlockDesc argument_type;
typedef std::size_t result_type;
result_type operator()(argument_type const &s) const noexcept {
result_type const h1(std::hash<int>{}(s.ID()));
result_type const h2(std::hash<int>{}(s.ID()));
return h1 ^ (h2 << 1);
}
};
} // namespace std
src/framework/data_layout.h
浏览文件 @ f1426f03
......@@ -19,49 +19,50 @@ limitations under the License. */
#include <string>
namespace paddle_mobile {
namespace framework {
namespace framework {
enum class DataLayout {
kNHWC = 0,
kNCHW = 1,
kAnyLayout = 2,
};
enum class DataLayout {
kNHWC = 0,
kNCHW = 1,
kAnyLayout = 2,
};
inline DataLayout StringToDataLayout(const std::string &str) {
std::string s(str);
for (size_t i = 0; i < s.size(); ++i) {
s[i] = toupper(s[i]);
}
inline DataLayout StringToDataLayout(const std::string &str) {
std::string s(str);
for (size_t i = 0; i < s.size(); ++i) {
s[i] = toupper(s[i]);
}
if (s == "NHWC") {
return DataLayout::kNHWC;
} else if (s == "NCHW") {
return DataLayout::kNCHW;
} else if (s == "ANYLAYOUT") {
return DataLayout::kAnyLayout;
} else {
// std::cout << "Unknown storage order string: %s", s;
}
}
if (s == "NHWC") {
return DataLayout::kNHWC;
} else if (s == "NCHW") {
return DataLayout::kNCHW;
} else if (s == "ANYLAYOUT") {
return DataLayout::kAnyLayout;
} else {
// std::cout << "Unknown storage order string: %s", s;
}
}
inline std::string DataLayoutToString(const DataLayout &data_layout) {
switch (data_layout) {
case DataLayout::kNHWC:
return "NHWC";
case DataLayout::kNCHW:
return "NCHW";
case DataLayout::kAnyLayout:
return "ANY_LAYOUT";
default:
break;
// std::cout << "unknown DataLayou %d", data_layout;
}
}
inline std::string DataLayoutToString(const DataLayout &data_layout) {
switch (data_layout) {
case DataLayout::kNHWC:
return "NHWC";
case DataLayout::kNCHW:
return "NCHW";
case DataLayout::kAnyLayout:
return "ANY_LAYOUT";
default:
break;
// std::cout << "unknown DataLayou %d", data_layout;
}
}
inline std::ostream &operator<<(std::ostream &out, const DataLayout &l) {
out << DataLayoutToString(l);
return out;
}
inline std::ostream &operator<<(std::ostream &out,
const DataLayout &l) {
out << DataLayoutToString(l);
return out;
}
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/data_transform.cpp
浏览文件 @ f1426f03
......@@ -21,67 +21,72 @@ SOFTWARE.
#include "data_transform.h"
namespace paddle_mobile {
namespace framework {
namespace framework {
static void PassTensorData(Tensor *from, Tensor *to) {
to->ShareDataWith(*from);
*from = Tensor();
}
static void PassTensorData(Tensor *from, Tensor *to) {
to->ShareDataWith(*from);
*from = Tensor();
}
void DataTransform(const OpKernelType &expected_kernel_type,
const OpKernelType &kernel_type_for_var,
const Tensor &input_tensor, Tensor *output_tensor) {
bool transformed = false;
Tensor in;
in.ShareDataWith(input_tensor);
Tensor out;
void DataTransform(const OpKernelType &expected_kernel_type,
const OpKernelType &kernel_type_for_var,
const Tensor &input_tensor, Tensor *output_tensor) {
bool transformed = false;
Tensor in;
in.ShareDataWith(input_tensor);
Tensor out;
// // do layout transform
// if (NeedTransformLayout(expected_kernel_type.data_layout_,
// kernel_type_for_var.data_layout_)) {
// TransDataLayout(kernel_type_for_var, expected_kernel_type, in, &out);
// transformed = true;
// PassTensorData(&out, &in);
// }
//
// // do data type transform
// if (expected_kernel_type.data_type_ != kernel_type_for_var.data_type_) {
// TransDataType(kernel_type_for_var, expected_kernel_type, in, &out);
// transformed = true;
// PassTensorData(&out, &in);
// }
//
// // do device transform
// if (!platform::is_same_place(kernel_type_for_var.place_,
// expected_kernel_type.place_)) {
// TransDataDevice(in, expected_kernel_type.place_, &out);
// transformed = true;
// PassTensorData(&out, &in);
// }
//
// PADDLE_ENFORCE(transformed, "No transform is applied, please check!");
// get output data
output_tensor->ShareDataWith(in);
}
// // do layout transform
// if (NeedTransformLayout(expected_kernel_type.data_layout_,
// kernel_type_for_var.data_layout_)) {
// TransDataLayout(kernel_type_for_var, expected_kernel_type, in,
// &out);
// transformed = true;
// PassTensorData(&out, &in);
// }
//
// // do data type transform
// if (expected_kernel_type.data_type_ !=
// kernel_type_for_var.data_type_) {
// TransDataType(kernel_type_for_var, expected_kernel_type, in,
// &out);
// transformed = true;
// PassTensorData(&out, &in);
// }
//
// // do device transform
// if (!platform::is_same_place(kernel_type_for_var.place_,
// expected_kernel_type.place_)) {
// TransDataDevice(in, expected_kernel_type.place_, &out);
// transformed = true;
// PassTensorData(&out, &in);
// }
//
// PADDLE_ENFORCE(transformed, "No transform is applied, please
// check!");
// get output data
output_tensor->ShareDataWith(in);
}
void CopyVariableWithTensor(const Variable &in_var, const Tensor &tensor,
Variable &out_var) {
// if (in_var.IsType<LoDTensor>()) {
// auto& in_lod_tensor = in_var.Get<LoDTensor>();
// auto* tran_lod_tensor = out_var.GetMutable<LoDTensor>();
// tran_lod_tensor->set_lod(in_lod_tensor.lod());
// tran_lod_tensor->set_layout(in_lod_tensor.layout());
// tran_lod_tensor->ShareDataWith(tensor);
// } else if (in_var.IsType<SelectedRows>()) {
// auto& in_selected_rows = in_var.Get<SelectedRows>();
// auto* trans_selected_rows = out_var.GetMutable<SelectedRows>();
// trans_selected_rows->set_height(in_selected_rows.height());
// trans_selected_rows->set_rows(in_selected_rows.rows());
// trans_selected_rows->mutable_value()->ShareDataWith(tensor);
// } else {
// PADDLE_THROW("unknown var type");
// }
}
void CopyVariableWithTensor(const Variable &in_var,
const Tensor &tensor, Variable &out_var) {
// if (in_var.IsType<LoDTensor>()) {
// auto& in_lod_tensor = in_var.Get<LoDTensor>();
// auto* tran_lod_tensor = out_var.GetMutable<LoDTensor>();
// tran_lod_tensor->set_lod(in_lod_tensor.lod());
// tran_lod_tensor->set_layout(in_lod_tensor.layout());
// tran_lod_tensor->ShareDataWith(tensor);
// } else if (in_var.IsType<SelectedRows>()) {
// auto& in_selected_rows = in_var.Get<SelectedRows>();
// auto* trans_selected_rows =
// out_var.GetMutable<SelectedRows>();
// trans_selected_rows->set_height(in_selected_rows.height());
// trans_selected_rows->set_rows(in_selected_rows.rows());
// trans_selected_rows->mutable_value()->ShareDataWith(tensor);
// } else {
// PADDLE_THROW("unknown var type");
// }
}
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/data_transform.h
浏览文件 @ f1426f03
......@@ -28,14 +28,14 @@ SOFTWARE.
#include "variable.h"
namespace paddle_mobile {
namespace framework {
namespace framework {
void DataTransform(const OpKernelType &expected_kernel_type,
const OpKernelType &kernel_type_for_var,
const Tensor &input_tensor, Tensor *out);
void DataTransform(const OpKernelType &expected_kernel_type,
const OpKernelType &kernel_type_for_var,
const Tensor &input_tensor, Tensor *out);
void CopyVariableWithTensor(const Variable &in_var, const Tensor &tensor,
Variable &out_var);
void CopyVariableWithTensor(const Variable &in_var,
const Tensor &tensor, Variable &out_var);
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/data_type.h
浏览文件 @ f1426f03
......@@ -21,23 +21,23 @@ SOFTWARE.
#include "framework.pb.h"
namespace paddle_mobile {
namespace framework {
namespace framework {
// inline proto::VarType::Type ToDataType(std::type_index type) {
// using namespace paddle_mobile::framework::proto;
// if (typeid(float).hash_code() == type.hash_code()) {
// return proto::VarType::FP32;
// } else if (typeid(double).hash_code() == type.hash_code()) {
// return proto::VarType::FP64;
// } else if (typeid(int).hash_code() == type.hash_code()) {
// return proto::VarType::INT32;
// } else if (typeid(int64_t).hash_code() == type.hash_code()) {
// return proto::VarType::INT64;
// } else if (typeid(bool).hash_code() == type.hash_code()) {
// return proto::VarType::BOOL;
// } else {
//// PADDLE_THROW("Not supported");
// }
// }
}
// inline proto::VarType::Type ToDataType(std::type_index type) {
// using namespace paddle_mobile::framework::proto;
// if (typeid(float).hash_code() == type.hash_code()) {
// return proto::VarType::FP32;
// } else if (typeid(double).hash_code() == type.hash_code()) {
// return proto::VarType::FP64;
// } else if (typeid(int).hash_code() == type.hash_code()) {
// return proto::VarType::INT32;
// } else if (typeid(int64_t).hash_code() == type.hash_code()) {
// return proto::VarType::INT64;
// } else if (typeid(bool).hash_code() == type.hash_code()) {
// return proto::VarType::BOOL;
// } else {
//// PADDLE_THROW("Not supported");
// }
// }
}
} // namespace paddle_mobile
src/framework/ddim.cc
浏览文件 @ f1426f03
......@@ -15,311 +15,320 @@ limitations under the License. */
#include "ddim.h"
namespace paddle_mobile {
namespace framework {
/// @cond HIDDEN
template <int i> Dim<i> make_dim(const int64_t *d) {
return Dim<i>(*d, make_dim<i - 1>(d + 1));
}
template <> Dim<0> make_dim<0>(const int64_t *d) { return Dim<0>(*d); }
void make_ddim(DDim &ddim, const int64_t *dims, int n) {
switch (n) {
case 0:
ddim = make_dim<0>(dims);
break;
case 1:
ddim = make_dim<1>(dims);
break;
case 2:
ddim = make_dim<2>(dims);
break;
case 3:
ddim = make_dim<3>(dims);
break;
case 4:
ddim = make_dim<4>(dims);
break;
case 5:
ddim = make_dim<5>(dims);
break;
case 6:
ddim = make_dim<6>(dims);
break;
case 7:
ddim = make_dim<7>(dims);
break;
case 8:
ddim = make_dim<8>(dims);
break;
case 9:
ddim = make_dim<9>(dims);
break;
default:
// std::cout << "Dynamic dimensions must have between [1, 9]
// dimensions.";
break;
}
}
/// @endcond
DDim make_ddim(std::initializer_list<int64_t> dims) {
DDim result(make_dim(0));
make_ddim(result, dims.begin(), dims.size());
return result;
}
DDim make_ddim(const std::vector<int64_t> &dims) {
DDim result(make_dim(0));
make_ddim(result, &dims[0], dims.size());
return result;
}
DDim make_ddim(const std::vector<int> &dims) {
std::vector<int64_t> res(dims.size());
std::transform(dims.begin(), dims.end(), res.begin(),
[](int d) { return static_cast<int64_t>(d); });
return make_ddim(res);
}
/// @cond HIDDEN
// XXX For some reason, putting this in an anonymous namespace causes errors
struct DynamicMutableIndexer : Vistor<int64_t &> {
public:
explicit DynamicMutableIndexer(int idx) : idx_(idx) {}
template <int D> int64_t &operator()(Dim<D> &dim) const { return dim[idx_]; }
private:
int idx_;
};
struct DynamicConstIndexer : public Vistor<int64_t> {
public:
explicit DynamicConstIndexer(int idx) : idx_(idx) {}
template <int D> int64_t operator()(const Dim<D> &dim) const {
return dim[idx_];
}
private:
int idx_;
};
/// @endcond
int64_t &DDim::operator[](int idx) {
return DDim::ApplyVistor(DynamicMutableIndexer(idx), *this);
}
int64_t DDim::operator[](int idx) const {
return DDim::ApplyVistor(DynamicConstIndexer(idx), *this);
}
int DDim::size() const { return arity(*this); }
bool DDim::operator==(DDim d) const {
// if (var.which() != d.getVar().which()) {
// return false;
// } else {
std::vector<int64_t> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
for (unsigned int i = 0; i < v1.size(); i++) {
if (v1[i] != v2[i]) {
return false;
}
}
return true;
// }
}
bool DDim::operator!=(DDim d) const { return !(*this == d); }
DDim DDim::operator+(DDim d) const {
std::vector<int64_t> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
std::vector<int64_t> v3;
assert(v1.size() == v2.size());
for (unsigned int i = 0; i < v1.size(); i++) {
v3.push_back(v1[i] + v2[i]);
}
return make_ddim(v3);
}
DDim DDim::operator*(DDim d) const {
std::vector<int64_t> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
std::vector<int64_t> v3;
assert(v1.size() == v2.size());
for (unsigned int i = 0; i < v1.size(); i++) {
v3.push_back(v1[i] * v2[i]);
}
return make_ddim(v3);
}
int64_t get(const DDim &ddim, int idx) { return ddim[idx]; }
void set(DDim &ddim, int idx, int value) { ddim[idx] = value; }
/// @cond HIDDEN
struct VectorizeVisitor : Vistor<void> {
std::vector<int64_t> &vector;
explicit VectorizeVisitor(std::vector<int64_t> &v) : vector(v) {}
template <typename T> void operator()(const T &t) {
vector.push_back(t.head);
this->operator()(t.tail);
}
void operator()(const Dim<0> &t) {}
};
/// @endcond
std::vector<int64_t> vectorize(const DDim &ddim) {
std::vector<int64_t> result;
VectorizeVisitor visitor(result);
DDim::ApplyVistor(visitor, ddim);
return result;
}
// NOTE: framework::vectorize converts to type int64_t
// which does not fit cudnn inputs.
std::vector<int> vectorize2int(const DDim &ddim) {
std::vector<int64_t> temp = vectorize(ddim);
std::vector<int> result(temp.begin(), temp.end());
return result;
}
struct ProductVisitor : Vistor<int64_t> {
template <int D> int64_t operator()(const Dim<D> &dim) {
return product(dim);
}
};
int64_t product(const DDim &ddim) {
ProductVisitor visitor;
return DDim::ApplyVistor(visitor, ddim);
}
struct SliceVectorizeVisitor : Vistor<void> {
std::vector<int64_t> &vector;
int begin;
int end;
SliceVectorizeVisitor(std::vector<int64_t> &v, int b, int e)
: vector(v), begin(b), end(e) {
// PADDLE_ENFORCE(begin < end,
// "Begin index must be less than end index in ddim
// slice.");
// PADDLE_ENFORCE(begin >= 0,
// "Begin index can't be less than zero in ddim slice.");
}
template <int S> void operator()(const Dim<S> &dim) {
if (begin == 0) {
vector.push_back(dim.head);
} else {
--begin;
}
--end;
if (end > 0) {
this->operator()(dim.tail);
}
}
void operator()(const Dim<0> &dim) {
// PADDLE_ENFORCE(end == 0, "End index in ddim slice is out of bound.");
}
};
DDim slice_ddim(const DDim &ddim, int begin, int end) {
std::vector<int64_t> vec;
vec.reserve(end - begin);
SliceVectorizeVisitor visitor(vec, begin, end);
// boost::apply_visitor(visitor, dim);
DDim::ApplyVistor(visitor, ddim);
// visitor(ddim.var.Get<Dim<4>>());
return make_ddim(vec);
}
/// \cond HIDDEN
struct ArityVisitor : Vistor<int> {
template <int D> int operator()(Dim<D>) const { return D; }
};
/// \endcond
int arity(const DDim &d) {
ArityVisitor arityVisitor = ArityVisitor();
return DDim::ApplyVistor(arityVisitor, d);
// return arityVisitor(d.var.Get<Dim<4>>());
// return boost::apply_visitor(ArityVisitor(), d); }
}
/// \cond HIDDEN
/// \endcond
struct OSVistor : Vistor<std::ostream &> {
OSVistor(std::ostream &os) : os_(os) {}
template <int D> std::ostream &operator()(Dim<D> dim) const {
return os_ << dim;
}
private:
std::ostream &os_;
};
std::ostream &operator<<(std::ostream &os, const DDim &ddim) {
auto vistor = OSVistor(os);
DDim::ApplyVistor(vistor, ddim);
return os;
}
DDim::DDim(std::initializer_list<int64_t> init_list) {
*this = make_ddim(init_list);
}
DDim flatten_to_2d(const DDim &src, int num_col_dims) {
int rank = src.size();
return make_ddim({product(slice_ddim(src, 0, num_col_dims)),
product(slice_ddim(src, num_col_dims, rank))});
}
DDim flatten_to_1d(const DDim &src) { return make_ddim({product(src)}); }
DDim stride(const DDim &ddim) {
std::vector<int64_t> strides(ddim.size());
strides[ddim.size() - 1] = 1;
for (int i = ddim.size() - 2; i >= 0; --i) {
strides[i] = strides[i + 1] * ddim[i + 1];
}
return framework::make_ddim(strides);
}
DDim stride_numel(const framework::DDim &ddim) {
std::vector<int64_t> strides(ddim.size());
strides[ddim.size() - 1] = ddim[ddim.size() - 1];
for (int i = ddim.size() - 2; i >= 0; --i) {
strides[i] = strides[i + 1] * ddim[i];
}
return framework::make_ddim(strides);
}
} // namespace framework
namespace framework {
/// @cond HIDDEN
template <int i> Dim<i> make_dim(const int64_t *d) {
return Dim<i>(*d, make_dim<i - 1>(d + 1));
}
template <> Dim<0> make_dim<0>(const int64_t *d) { return Dim<0>(*d); }
void make_ddim(DDim &ddim, const int64_t *dims, int n) {
switch (n) {
case 0:
ddim = make_dim<0>(dims);
break;
case 1:
ddim = make_dim<1>(dims);
break;
case 2:
ddim = make_dim<2>(dims);
break;
case 3:
ddim = make_dim<3>(dims);
break;
case 4:
ddim = make_dim<4>(dims);
break;
case 5:
ddim = make_dim<5>(dims);
break;
case 6:
ddim = make_dim<6>(dims);
break;
case 7:
ddim = make_dim<7>(dims);
break;
case 8:
ddim = make_dim<8>(dims);
break;
case 9:
ddim = make_dim<9>(dims);
break;
default:
// std::cout << "Dynamic dimensions must have between [1,
// 9]
// dimensions.";
break;
}
}
/// @endcond
DDim make_ddim(std::initializer_list<int64_t> dims) {
DDim result(make_dim(0));
make_ddim(result, dims.begin(), dims.size());
return result;
}
DDim make_ddim(const std::vector<int64_t> &dims) {
DDim result(make_dim(0));
make_ddim(result, &dims[0], dims.size());
return result;
}
DDim make_ddim(const std::vector<int> &dims) {
std::vector<int64_t> res(dims.size());
std::transform(dims.begin(), dims.end(), res.begin(),
[](int d) { return static_cast<int64_t>(d); });
return make_ddim(res);
}
/// @cond HIDDEN
// XXX For some reason, putting this in an anonymous namespace causes
// errors
struct DynamicMutableIndexer : Vistor<int64_t &> {
public:
explicit DynamicMutableIndexer(int idx) : idx_(idx) {}
template <int D> int64_t &operator()(Dim<D> &dim) const {
return dim[idx_];
}
private:
int idx_;
};
struct DynamicConstIndexer : public Vistor<int64_t> {
public:
explicit DynamicConstIndexer(int idx) : idx_(idx) {}
template <int D> int64_t operator()(const Dim<D> &dim) const {
return dim[idx_];
}
private:
int idx_;
};
/// @endcond
int64_t &DDim::operator[](int idx) {
return DDim::ApplyVistor(DynamicMutableIndexer(idx), *this);
}
int64_t DDim::operator[](int idx) const {
return DDim::ApplyVistor(DynamicConstIndexer(idx), *this);
}
int DDim::size() const { return arity(*this); }
bool DDim::operator==(DDim d) const {
// if (var.which() != d.getVar().which()) {
// return false;
// } else {
std::vector<int64_t> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
for (unsigned int i = 0; i < v1.size(); i++) {
if (v1[i] != v2[i]) {
return false;
}
}
return true;
// }
}
bool DDim::operator!=(DDim d) const { return !(*this == d); }
DDim DDim::operator+(DDim d) const {
std::vector<int64_t> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
std::vector<int64_t> v3;
assert(v1.size() == v2.size());
for (unsigned int i = 0; i < v1.size(); i++) {
v3.push_back(v1[i] + v2[i]);
}
return make_ddim(v3);
}
DDim DDim::operator*(DDim d) const {
std::vector<int64_t> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
std::vector<int64_t> v3;
assert(v1.size() == v2.size());
for (unsigned int i = 0; i < v1.size(); i++) {
v3.push_back(v1[i] * v2[i]);
}
return make_ddim(v3);
}
int64_t get(const DDim &ddim, int idx) { return ddim[idx]; }
void set(DDim &ddim, int idx, int value) { ddim[idx] = value; }
/// @cond HIDDEN
struct VectorizeVisitor : Vistor<void> {
std::vector<int64_t> &vector;
explicit VectorizeVisitor(std::vector<int64_t> &v) : vector(v) {}
template <typename T> void operator()(const T &t) {
vector.push_back(t.head);
this->operator()(t.tail);
}
void operator()(const Dim<0> &t) {}
};
/// @endcond
std::vector<int64_t> vectorize(const DDim &ddim) {
std::vector<int64_t> result;
VectorizeVisitor visitor(result);
DDim::ApplyVistor(visitor, ddim);
return result;
}
// NOTE: framework::vectorize converts to type int64_t
// which does not fit cudnn inputs.
std::vector<int> vectorize2int(const DDim &ddim) {
std::vector<int64_t> temp = vectorize(ddim);
std::vector<int> result(temp.begin(), temp.end());
return result;
}
struct ProductVisitor : Vistor<int64_t> {
template <int D> int64_t operator()(const Dim<D> &dim) {
return product(dim);
}
};
int64_t product(const DDim &ddim) {
ProductVisitor visitor;
return DDim::ApplyVistor(visitor, ddim);
}
struct SliceVectorizeVisitor : Vistor<void> {
std::vector<int64_t> &vector;
int begin;
int end;
SliceVectorizeVisitor(std::vector<int64_t> &v, int b, int e)
: vector(v), begin(b), end(e) {
// PADDLE_ENFORCE(begin < end,
// "Begin index must be less than end index in
// ddim
// slice.");
// PADDLE_ENFORCE(begin >= 0,
// "Begin index can't be less than zero in
// ddim slice.");
}
template <int S> void operator()(const Dim<S> &dim) {
if (begin == 0) {
vector.push_back(dim.head);
} else {
--begin;
}
--end;
if (end > 0) {
this->operator()(dim.tail);
}
}
void operator()(const Dim<0> &dim) {
// PADDLE_ENFORCE(end == 0, "End index in ddim slice is out
// of bound.");
}
};
DDim slice_ddim(const DDim &ddim, int begin, int end) {
std::vector<int64_t> vec;
vec.reserve(end - begin);
SliceVectorizeVisitor visitor(vec, begin, end);
// boost::apply_visitor(visitor, dim);
DDim::ApplyVistor(visitor, ddim);
// visitor(ddim.var.Get<Dim<4>>());
return make_ddim(vec);
}
/// \cond HIDDEN
struct ArityVisitor : Vistor<int> {
template <int D> int operator()(Dim<D>) const { return D; }
};
/// \endcond
int arity(const DDim &d) {
ArityVisitor arityVisitor = ArityVisitor();
return DDim::ApplyVistor(arityVisitor, d);
// return arityVisitor(d.var.Get<Dim<4>>());
// return boost::apply_visitor(ArityVisitor(), d); }
}
/// \cond HIDDEN
/// \endcond
struct OSVistor : Vistor<std::ostream &> {
OSVistor(std::ostream &os) : os_(os) {}
template <int D> std::ostream &operator()(Dim<D> dim) const {
return os_ << dim;
}
private:
std::ostream &os_;
};
std::ostream &operator<<(std::ostream &os, const DDim &ddim) {
auto vistor = OSVistor(os);
DDim::ApplyVistor(vistor, ddim);
return os;
}
DDim::DDim(std::initializer_list<int64_t> init_list) {
*this = make_ddim(init_list);
}
DDim flatten_to_2d(const DDim &src, int num_col_dims) {
int rank = src.size();
return make_ddim({product(slice_ddim(src, 0, num_col_dims)),
product(slice_ddim(src, num_col_dims, rank))});
}
DDim flatten_to_1d(const DDim &src) {
return make_ddim({product(src)});
}
DDim stride(const DDim &ddim) {
std::vector<int64_t> strides(ddim.size());
strides[ddim.size() - 1] = 1;
for (int i = ddim.size() - 2; i >= 0; --i) {
strides[i] = strides[i + 1] * ddim[i + 1];
}
return framework::make_ddim(strides);
}
DDim stride_numel(const framework::DDim &ddim) {
std::vector<int64_t> strides(ddim.size());
strides[ddim.size() - 1] = ddim[ddim.size() - 1];
for (int i = ddim.size() - 2; i >= 0; --i) {
strides[i] = strides[i + 1] * ddim[i];
}
return framework::make_ddim(strides);
}
} // namespace framework
} // namespace paddle_mobile
src/framework/ddim.h
浏览文件 @ f1426f03
......@@ -22,140 +22,145 @@ limitations under the License. */
#include <vector>
namespace paddle_mobile {
namespace framework {
/**
* \brief A dynamically sized dimension.
*
* The number of dimensions must be between [1, 9].
*/
struct DDim {
typedef Variant<Dim<0>, Dim<1>, Dim<2>, Dim<3>, Dim<4>, Dim<5>, Dim<6>,
Dim<7>, Dim<8>, Dim<9>>
DDimVar;
DDimVar var;
template <typename Vistor>
static typename Vistor::type_t ApplyVistor(Vistor vistor, const DDim &d) {
if (d.var.TypeId() == typeid(Dim<0>).hash_code()) {
return vistor(d.var.Get<Dim<0>>());
} else if (d.var.TypeId() == typeid(Dim<1>).hash_code()) {
return vistor(d.var.Get<Dim<1>>());
} else if (d.var.TypeId() == typeid(Dim<2>).hash_code()) {
return vistor(d.var.Get<Dim<2>>());
} else if (d.var.TypeId() == typeid(Dim<3>).hash_code()) {
return vistor(d.var.Get<Dim<3>>());
} else if (d.var.TypeId() == typeid(Dim<4>).hash_code()) {
return vistor(d.var.Get<Dim<4>>());
} else if (d.var.TypeId() == typeid(Dim<5>).hash_code()) {
return vistor(d.var.Get<Dim<5>>());
} else if (d.var.TypeId() == typeid(Dim<6>).hash_code()) {
return vistor(d.var.Get<Dim<6>>());
} else if (d.var.TypeId() == typeid(Dim<7>).hash_code()) {
return vistor(d.var.Get<Dim<7>>());
} else if (d.var.TypeId() == typeid(Dim<8>).hash_code()) {
return vistor(d.var.Get<Dim<8>>());
} else if (d.var.TypeId() == typeid(Dim<9>).hash_code()) {
return vistor(d.var.Get<Dim<9>>());
} else {
printf(" dim not support \n");
throw std::bad_exception();
// return typename Vistor::type_t();
}
}
DDim() { var.Set<Dim<1>>(Dim<1>()); }
template <int D> explicit DDim(const Dim<D> &in) { var.Set<Dim<D>>(in); }
/*implicit*/ DDim(std::initializer_list<int64_t> init_list);
template <int D> DDim &operator=(const Dim<D> &in) {
var.Set<Dim<D>>(in);
return *this;
}
int64_t &operator[](int idx);
int64_t operator[](int idx) const;
// template <typename Visitor>
// typename Visitor::result_type apply_visitor(Visitor& visitor) {
// return var.apply_visitor(visitor);
// }
//
// template <typename Visitor>
// typename Visitor::result_type apply_visitor(Visitor& visitor) const {
// return var.apply_visitor(visitor);
// }
DDimVar getVar() { return var; }
bool operator==(DDim d) const;
bool operator!=(DDim d) const;
DDim operator+(DDim d) const;
DDim operator*(DDim d) const;
int size() const;
};
/**
* \brief Make a DDim from std::vector<int64_t>
*
* \param dims An vector of ints. Must be sized between [1, 9]
*/
DDim make_ddim(const std::vector<int64_t> &dims);
DDim make_ddim(const std::vector<int> &dims);
/**
* \brief Make a DDim from an initializer list
*
* \param dims An initializer list of ints. Must be sized between [1, 9]
*
*/
DDim make_ddim(std::initializer_list<int64_t> dims);
int64_t get(const DDim &dim, int idx);
void set(DDim &dim, int idx, int val);
std::vector<int64_t> vectorize(const DDim &ddim);
std::vector<int> vectorize2int(const DDim &ddim);
int64_t product(const DDim &ddim);
/**
* \brief Slice a ddim
*
* Slice dim with [begin, end).
* e.g. DDim d = make_ddim({1,2,3,4,5});
* slice_ddim(d, 1, 3); ====> {2,3}
*/
DDim slice_ddim(const DDim &dim, int begin, int end);
/**
* \brief What is the length of this dimension?
*
* \param Dynamic dimension to inspect
*/
namespace framework {
/**
* \brief A dynamically sized dimension.
*
* The number of dimensions must be between [1, 9].
*/
struct DDim {
typedef Variant<Dim<0>, Dim<1>, Dim<2>, Dim<3>, Dim<4>, Dim<5>,
Dim<6>, Dim<7>, Dim<8>, Dim<9>>
DDimVar;
DDimVar var;
template <typename Vistor>
static typename Vistor::type_t ApplyVistor(Vistor vistor,
const DDim &d) {
if (d.var.TypeId() == typeid(Dim<0>).hash_code()) {
return vistor(d.var.Get<Dim<0>>());
} else if (d.var.TypeId() == typeid(Dim<1>).hash_code()) {
return vistor(d.var.Get<Dim<1>>());
} else if (d.var.TypeId() == typeid(Dim<2>).hash_code()) {
return vistor(d.var.Get<Dim<2>>());
} else if (d.var.TypeId() == typeid(Dim<3>).hash_code()) {
return vistor(d.var.Get<Dim<3>>());
} else if (d.var.TypeId() == typeid(Dim<4>).hash_code()) {
return vistor(d.var.Get<Dim<4>>());
} else if (d.var.TypeId() == typeid(Dim<5>).hash_code()) {
return vistor(d.var.Get<Dim<5>>());
} else if (d.var.TypeId() == typeid(Dim<6>).hash_code()) {
return vistor(d.var.Get<Dim<6>>());
} else if (d.var.TypeId() == typeid(Dim<7>).hash_code()) {
return vistor(d.var.Get<Dim<7>>());
} else if (d.var.TypeId() == typeid(Dim<8>).hash_code()) {
return vistor(d.var.Get<Dim<8>>());
} else if (d.var.TypeId() == typeid(Dim<9>).hash_code()) {
return vistor(d.var.Get<Dim<9>>());
} else {
printf(" dim not support \n");
throw std::bad_exception();
// return typename Vistor::type_t();
}
}
DDim() { var.Set<Dim<1>>(Dim<1>()); }
template <int D> explicit DDim(const Dim<D> &in) {
var.Set<Dim<D>>(in);
}
/*implicit*/ DDim(std::initializer_list<int64_t> init_list);
template <int D> DDim &operator=(const Dim<D> &in) {
var.Set<Dim<D>>(in);
return *this;
}
int64_t &operator[](int idx);
int64_t operator[](int idx) const;
// template <typename Visitor>
// typename Visitor::result_type apply_visitor(Visitor& visitor) {
// return var.apply_visitor(visitor);
// }
//
// template <typename Visitor>
// typename Visitor::result_type apply_visitor(Visitor& visitor)
// const {
// return var.apply_visitor(visitor);
// }
DDimVar getVar() { return var; }
bool operator==(DDim d) const;
bool operator!=(DDim d) const;
DDim operator+(DDim d) const;
DDim operator*(DDim d) const;
int size() const;
};
/**
* \brief Make a DDim from std::vector<int64_t>
*
* \param dims An vector of ints. Must be sized between [1, 9]
*/
DDim make_ddim(const std::vector<int64_t> &dims);
DDim make_ddim(const std::vector<int> &dims);
/**
* \brief Make a DDim from an initializer list
*
* \param dims An initializer list of ints. Must be sized between [1, 9]
*
*/
DDim make_ddim(std::initializer_list<int64_t> dims);
int64_t get(const DDim &dim, int idx);
void set(DDim &dim, int idx, int val);
std::vector<int64_t> vectorize(const DDim &ddim);
std::vector<int> vectorize2int(const DDim &ddim);
int64_t product(const DDim &ddim);
/**
* \brief Slice a ddim
*
* Slice dim with [begin, end).
* e.g. DDim d = make_ddim({1,2,3,4,5});
* slice_ddim(d, 1, 3); ====> {2,3}
*/
DDim slice_ddim(const DDim &dim, int begin, int end);
/**
* \brief What is the length of this dimension?
*
* \param Dynamic dimension to inspect
*/
int arity(const DDim &ddim);
int arity(const DDim &ddim);
std::ostream &operator<<(std::ostream &, const DDim &);
std::ostream &operator<<(std::ostream &, const DDim &);
// Reshape a tensor to a matrix. The matrix's first dimension(column length)
// will be the product of tensor's first `num_col_dims` dimensions.
DDim flatten_to_2d(const DDim &src, int num_col_dims);
// Reshape a tensor to a matrix. The matrix's first dimension(column
// length)
// will be the product of tensor's first `num_col_dims` dimensions.
DDim flatten_to_2d(const DDim &src, int num_col_dims);
DDim flatten_to_1d(const DDim &src);
DDim flatten_to_1d(const DDim &src);
DDim stride(const DDim &ddim);
DDim stride(const DDim &ddim);
DDim stride_numel(const DDim &ddim);
} // namespace framework
DDim stride_numel(const DDim &ddim);
} // namespace framework
} // namespace paddle_mobile
src/framework/dim.h
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此差异已折叠。
src/framework/executor.cpp
浏览文件 @ f1426f03
......@@ -23,72 +23,75 @@ SOFTWARE.
#include "variable.h"
namespace paddle_mobile {
namespace framework {
namespace framework {
template <typename Dtype>
Executor<Dtype>::Executor(const Program<Dtype> p) : program_(p) {
if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram;
} else {
to_predict_program_ = program_.originProgram;
}
template <typename Dtype>
Executor<Dtype>::Executor(const Program<Dtype> p) : program_(p) {
if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram;
} else {
to_predict_program_ = program_.originProgram;
}
const std::vector<std::shared_ptr<BlockDesc>> blocks =
to_predict_program_->Blocks();
for (int i = 0; i < blocks.size(); ++i) {
std::shared_ptr<BlockDesc> block_desc = blocks[i];
std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j];
if (op->Type() == "conv2d" && op->Input("Input")[0] == "pixel") {
Attribute strides_attr = op->GetAttrMap().at("strides");
std::vector<int> stride = strides_attr.Get<std::vector<int>>();
for (int k = 0; k < stride.size(); ++k) {
const std::vector<std::shared_ptr<BlockDesc>> blocks =
to_predict_program_->Blocks();
for (int i = 0; i < blocks.size(); ++i) {
std::shared_ptr<BlockDesc> block_desc = blocks[i];
std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j];
if (op->Type() == "conv2d" &&
op->Input("Input")[0] == "pixel") {
Attribute strides_attr = op->GetAttrMap().at("strides");
std::vector<int> stride =
strides_attr.Get<std::vector<int>>();
for (int k = 0; k < stride.size(); ++k) {
}
std::shared_ptr<operators::ConvOp<Dtype, float>> conv =
std::make_shared<operators::ConvOp<Dtype, float>>(
op->Type(), op->GetInputs(), op->GetOutputs(),
op->GetAttrMap(), program_.scope);
ops_of_block_[*block_desc.get()].push_back(conv);
}
}
}
}
std::shared_ptr<operators::ConvOp<Dtype, float>> conv =
std::make_shared<operators::ConvOp<Dtype, float>>(
op->Type(), op->GetInputs(), op->GetOutputs(), op->GetAttrMap(),
program_.scope);
ops_of_block_[*block_desc.get()].push_back(conv);
}
}
}
}
template <typename Dtype>
std::shared_ptr<Tensor> Executor<Dtype>::predict(Tensor &t) {
// feed
auto scope = program_.scope;
Variable *g_feed_value = scope->Var("pixel");
auto tensor = g_feed_value->GetMutable<Tensor>();
tensor->ShareDataWith(t);
template <typename Dtype>
std::shared_ptr<Tensor> Executor<Dtype>::predict(Tensor &t) {
// feed
auto scope = program_.scope;
Variable *g_feed_value = scope->Var("pixel");
auto tensor = g_feed_value->GetMutable<Tensor>();
tensor->ShareDataWith(t);
Variable *con_output = scope->Var("conv2d_0.tmp_0");
Tensor *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({1, 16, 32, 32});
// std::cout << typeid(output_tensor).name() << std::endl;
// std::cout << "output_tensor dims: " << output_tensor->dims() <<
// std::endl;
Variable *con_output = scope->Var("conv2d_0.tmp_0");
Tensor *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({1, 16, 32, 32});
// std::cout << typeid(output_tensor).name() << std::endl;
// std::cout << "output_tensor dims: " << output_tensor->dims() << std::endl;
std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
predict(t, 0);
return out_tensor;
}
predict(t, 0);
return out_tensor;
}
template <typename Dtype>
void Executor<Dtype>::predict(const Tensor &t, int block_id) {
std::shared_ptr<BlockDesc> to_predict_block =
to_predict_program_->Block(block_id);
for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size(); ++j) {
auto op = ops_of_block_[*to_predict_block.get()][j];
// std::cout << "开始run" << std::endl;
op->Run();
}
}
template <typename Dtype>
void Executor<Dtype>::predict(const Tensor &t, int block_id) {
std::shared_ptr<BlockDesc> to_predict_block =
to_predict_program_->Block(block_id);
for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size();
++j) {
auto op = ops_of_block_[*to_predict_block.get()][j];
// std::cout << "开始run" << std::endl;
op->Run();
}
}
template class Executor<CPU>;
template class Executor<CPU>;
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/executor.h
浏览文件 @ f1426f03
......@@ -32,22 +32,22 @@ SOFTWARE.
#include "variable.h"
namespace paddle_mobile {
namespace framework {
namespace framework {
template <typename Dtype> class Executor {
public:
Executor(const Program<Dtype> p);
std::shared_ptr<Tensor> predict(Tensor &t);
template <typename Dtype> class Executor {
public:
Executor(const Program<Dtype> p);
std::shared_ptr<Tensor> predict(Tensor &t);
private:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
void predict(const Tensor &t, int block_id);
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
};
private:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
void predict(const Tensor &t, int block_id);
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
};
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/framework.pb.cpp
浏览文件 @ f1426f03
此差异已折叠。
src/framework/framework.pb.h
浏览文件 @ f1426f03
此差异已折叠。
src/framework/lod_tensor.cc
浏览文件 @ f1426f03
此差异已折叠。
src/framework/lod_tensor.h
浏览文件 @ f1426f03
......@@ -23,178 +23,190 @@ limitations under the License. */
namespace paddle_mobile {
namespace framework {
/*
* LoD is short for Level of Details.
*
* - in a level, each element indicates relative offset of the lower level
* - the first element should be 0 and that indicates that this sequence start
* from 0
* - each sequence's begin and end(no-inclusive) is level[id, id+1]
*
* For example:
* 3-level LoD stores
*
* 0 2 3
* 0 2 4 7
* 0 2 5 7 10 12 15 20
*/
using LoD = std::vector<std::vector<size_t>>;
std::ostream &operator<<(std::ostream &os, const LoD &lod);
std::ostream &operator<<(std::ostream &os, const LoDTensor &t);
std::string LoDToString(const LoD &lod);
LoD SliceInLevel(const LoD &in, size_t level, size_t elem_begin,
size_t elem_end);
/*
* Transform an LoD from relative offsets to absolute offsets.
*/
LoD ToAbsOffset(const LoD &in);
bool operator==(const LoD &a, const LoD &b);
/*
* Check whether this lod's format is valid.
*
* ATTENTION:
* - Empty lod is treated as valid.
*
* It will check two things:
*
* 1. all the offsets in a level should be ascending(no same items allows).
* 2. there should be more than 2 offsets existing in each level.
* 3. the higher level's last offset should equals the lower level's size-1.
* 4. the first offset(the begin offset) of each level should be 0.
* 5. the lowest level's last offset should equals `tensor_height` if
* tensor_height>0.
*/
bool CheckLoD(const LoD &in, int tensor_height = -1);
/*
* Check whether this absolute lod's format is valid.
*
* ATTENTION:
* - Empty lod is treated as valid.
*
* It will check two things:
* 1. all the offsets in a level should be ascending(no same items allows)
* 2. there should be more than 2 offsets existing in each level.
* 3. the first offset of each level should be 0, and the last should be the
* same(the height of underlying tensor) or `tensor_height` if
* tensor_height>0.
*/
bool CheckAbsLoD(const LoD &in, int tensor_height = -1);
/*
* LoDTensor (Level of details Tensor)
* see https://en.wikipedia.org/wiki/Level_of_details for reference.
*/
class LoDTensor : public Tensor {
public:
LoDTensor() : Tensor() {}
explicit LoDTensor(const LoD &lod) : lod_(lod) {}
void set_lod(const LoD &lod) { lod_ = lod; }
const LoD &lod() const { return lod_; }
LoD *mutable_lod() { return &lod_; }
/*
* Get the start offset and end offset of an element from LoD.
*/
std::pair<size_t, size_t> lod_element(size_t level, size_t elem) const {
// PADDLE_ENFORCE_LT(level, NumLevels());
// PADDLE_ENFORCE_LT(elem, NumElements(level));
return std::make_pair((lod_)[level][elem], (lod_)[level][elem + 1]);
}
/*
* Number of LoDTensor's levels, each level has units of data, for example,
* in the sentence's view, article, paragraph, sentence are 3 levels.
*/
size_t NumLevels() const { return lod_.size(); }
/*
* Number of elements in a level.
*/
size_t NumElements(size_t level = 0) const {
// PADDLE_ENFORCE_LT(level, NumLevels());
// the last offset is the end of last element
return (lod_)[level].size() - 1;
}
private:
LoD lod_;
};
/*
* Expand the `source` to fit the LoD of `lod`. For example, a `source`
* LoDTensor is
* - LoD: [0, 2]
* - tensor: [a0, a1]
* a `lod` is
* - LoD: [0 3 5]
* returns a new LoDTensor
* - [a0 a0 a0 a1 a1]
*/
template <typename T>
LoDTensor LodExpand(const LoDTensor &source, const LoD &lod, size_t level) {
LoD abs_lod = ToAbsOffset(lod);
const auto &lod_level = lod[level];
size_t num_instances = source.dims()[0];
// new tensor
LoDTensor tensor;
tensor.set_lod(lod);
auto dims = source.dims();
dims[0] = lod_level.back();
tensor.Resize(dims);
tensor.mutable_data<T>();
// PADDLE_ENFORCE_EQ(num_instances, lod_level.size() - 1);
for (size_t ins = 0; ins < num_instances; ins++) {
for (size_t elem = lod_level[ins]; elem < lod_level[ins + 1]; elem++) {
auto slice = tensor.Slice(elem, elem + 1);
TensorCopy(source.Slice(ins, ins + 1), &slice);
}
}
return tensor;
}
// Get the absolute offset of a lod[start_level][start_idx:end_idx] and
// relative length of details for every levels(i.e., [start_level: ]).
//
// For example,
// lod = [[0, 3, 4, 8], [0, 9, 10, 11, 13, 17, 19, 22, 24]]
// start_level = 0
// start_idx = 1
// end_idx = 3
//
// Returns:
// LoD = [[1, 4], [2, 4, 2, 3, 2]]
// pair<size_t, size_t> = {11, 24}
std::pair<LoD, std::pair<size_t, size_t>>
GetSubLoDAndAbsoluteOffset(const LoD &lod, size_t start_idx, size_t end_idx,
size_t start_level);
void AppendLoD(LoD *lod, const LoD &lod_length);
/*
* Serialize/Desiralize LoDTensor to std::ostream
* You can pass ofstream or ostringstream to serilize to file
* or to a in memory string. GPU tensor will be copied to CPU.
*/
void SerializeToStream(std::ostream &os, const LoDTensor &tensor);
void DeserializeFromStream(std::istream &is, LoDTensor *tensor);
} // namespace framework
namespace framework {
/*
* LoD is short for Level of Details.
*
* - in a level, each element indicates relative offset of the lower
* level
* - the first element should be 0 and that indicates that this sequence
* start
* from 0
* - each sequence's begin and end(no-inclusive) is level[id, id+1]
*
* For example:
* 3-level LoD stores
*
* 0 2 3
* 0 2 4 7
* 0 2 5 7 10 12 15 20
*/
using LoD = std::vector<std::vector<size_t>>;
std::ostream &operator<<(std::ostream &os, const LoD &lod);
std::ostream &operator<<(std::ostream &os, const LoDTensor &t);
std::string LoDToString(const LoD &lod);
LoD SliceInLevel(const LoD &in, size_t level, size_t elem_begin,
size_t elem_end);
/*
* Transform an LoD from relative offsets to absolute offsets.
*/
LoD ToAbsOffset(const LoD &in);
bool operator==(const LoD &a, const LoD &b);
/*
* Check whether this lod's format is valid.
*
* ATTENTION:
* - Empty lod is treated as valid.
*
* It will check two things:
*
* 1. all the offsets in a level should be ascending(no same items
* allows).
* 2. there should be more than 2 offsets existing in each level.
* 3. the higher level's last offset should equals the lower level's
* size-1.
* 4. the first offset(the begin offset) of each level should be 0.
* 5. the lowest level's last offset should equals `tensor_height` if
* tensor_height>0.
*/
bool CheckLoD(const LoD &in, int tensor_height = -1);
/*
* Check whether this absolute lod's format is valid.
*
* ATTENTION:
* - Empty lod is treated as valid.
*
* It will check two things:
* 1. all the offsets in a level should be ascending(no same items
* allows)
* 2. there should be more than 2 offsets existing in each level.
* 3. the first offset of each level should be 0, and the last should
* be the
* same(the height of underlying tensor) or `tensor_height` if
* tensor_height>0.
*/
bool CheckAbsLoD(const LoD &in, int tensor_height = -1);
/*
* LoDTensor (Level of details Tensor)
* see https://en.wikipedia.org/wiki/Level_of_details for reference.
*/
class LoDTensor : public Tensor {
public:
LoDTensor() : Tensor() {}
explicit LoDTensor(const LoD &lod) : lod_(lod) {}
void set_lod(const LoD &lod) { lod_ = lod; }
const LoD &lod() const { return lod_; }
LoD *mutable_lod() { return &lod_; }
/*
* Get the start offset and end offset of an element from LoD.
*/
std::pair<size_t, size_t> lod_element(size_t level,
size_t elem) const {
// PADDLE_ENFORCE_LT(level, NumLevels());
// PADDLE_ENFORCE_LT(elem, NumElements(level));
return std::make_pair((lod_)[level][elem],
(lod_)[level][elem + 1]);
}
/*
* Number of LoDTensor's levels, each level has units of data, for
* example,
* in the sentence's view, article, paragraph, sentence are 3
* levels.
*/
size_t NumLevels() const { return lod_.size(); }
/*
* Number of elements in a level.
*/
size_t NumElements(size_t level = 0) const {
// PADDLE_ENFORCE_LT(level, NumLevels());
// the last offset is the end of last element
return (lod_)[level].size() - 1;
}
private:
LoD lod_;
};
/*
* Expand the `source` to fit the LoD of `lod`. For example, a `source`
* LoDTensor is
* - LoD: [0, 2]
* - tensor: [a0, a1]
* a `lod` is
* - LoD: [0 3 5]
* returns a new LoDTensor
* - [a0 a0 a0 a1 a1]
*/
template <typename T>
LoDTensor LodExpand(const LoDTensor &source, const LoD &lod,
size_t level) {
LoD abs_lod = ToAbsOffset(lod);
const auto &lod_level = lod[level];
size_t num_instances = source.dims()[0];
// new tensor
LoDTensor tensor;
tensor.set_lod(lod);
auto dims = source.dims();
dims[0] = lod_level.back();
tensor.Resize(dims);
tensor.mutable_data<T>();
// PADDLE_ENFORCE_EQ(num_instances, lod_level.size() - 1);
for (size_t ins = 0; ins < num_instances; ins++) {
for (size_t elem = lod_level[ins]; elem < lod_level[ins + 1];
elem++) {
auto slice = tensor.Slice(elem, elem + 1);
TensorCopy(source.Slice(ins, ins + 1), &slice);
}
}
return tensor;
}
// Get the absolute offset of a lod[start_level][start_idx:end_idx] and
// relative length of details for every levels(i.e., [start_level: ]).
//
// For example,
// lod = [[0, 3, 4, 8], [0, 9, 10, 11, 13, 17, 19, 22, 24]]
// start_level = 0
// start_idx = 1
// end_idx = 3
//
// Returns:
// LoD = [[1, 4], [2, 4, 2, 3, 2]]
// pair<size_t, size_t> = {11, 24}
std::pair<LoD, std::pair<size_t, size_t>>
GetSubLoDAndAbsoluteOffset(const LoD &lod, size_t start_idx,
size_t end_idx, size_t start_level);
void AppendLoD(LoD *lod, const LoD &lod_length);
/*
* Serialize/Desiralize LoDTensor to std::ostream
* You can pass ofstream or ostringstream to serilize to file
* or to a in memory string. GPU tensor will be copied to CPU.
*/
void SerializeToStream(std::ostream &os, const LoDTensor &tensor);
void DeserializeFromStream(std::istream &is, LoDTensor *tensor);
} // namespace framework
} // namespace paddle_mobile
src/framework/op_desc.cpp
浏览文件 @ f1426f03
......@@ -5,55 +5,58 @@
#include "op_desc.h"
namespace paddle_mobile {
namespace framework {
OpDesc::OpDesc(const proto::OpDesc &desc) : desc_(desc) {
for (int i = 0; i < desc_.inputs_size(); ++i) {
const proto::OpDesc::Var &var = desc_.inputs(i);
std::vector<std::string> &args = inputs_[var.parameter()];
int arg_size = var.arguments_size();
for (int j = 0; j < arg_size; ++j) {
args.push_back(var.arguments(j));
}
}
for (int i = 0; i < desc_.outputs_size(); ++i) {
const proto::OpDesc::Var &var = desc_.outputs(i);
std::vector<std::string> &args = outputs_[var.parameter()];
int arg_size = var.arguments_size();
for (int j = 0; j < arg_size; ++j) {
args.push_back(var.arguments(j));
}
}
for (const proto::OpDesc::Attr &attr : desc_.attrs()) {
std::string attr_name = attr.name();
if (attr.type() != proto::AttrType::BLOCK) {
attrs_[attr_name] = Attribute::GetAttrValue(attr);
// if (attr.type() == proto::AttrType::INT){
// std::cout << " attrName " << attr_name << " " <<
// attrs_[attr_name].Get<int>() << std::endl;
// }
}
}
}
const std::vector<std::string> &OpDesc::Input(const std::string &name) const {
return inputs_.find(name)->second;
}
const std::vector<std::string> &OpDesc::Output(const std::string &name) const {
return outputs_.find(name)->second;
}
Attribute OpDesc::GetAttr(const std::string &name) const {
auto it = attrs_.find(name);
return it->second;
}
const std::unordered_map<std::string, Attribute> &OpDesc::GetAttrMap() const {
return attrs_;
}
} // namespace framework
namespace framework {
OpDesc::OpDesc(const proto::OpDesc &desc) : desc_(desc) {
for (int i = 0; i < desc_.inputs_size(); ++i) {
const proto::OpDesc::Var &var = desc_.inputs(i);
std::vector<std::string> &args = inputs_[var.parameter()];
int arg_size = var.arguments_size();
for (int j = 0; j < arg_size; ++j) {
args.push_back(var.arguments(j));
}
}
for (int i = 0; i < desc_.outputs_size(); ++i) {
const proto::OpDesc::Var &var = desc_.outputs(i);
std::vector<std::string> &args = outputs_[var.parameter()];
int arg_size = var.arguments_size();
for (int j = 0; j < arg_size; ++j) {
args.push_back(var.arguments(j));
}
}
for (const proto::OpDesc::Attr &attr : desc_.attrs()) {
std::string attr_name = attr.name();
if (attr.type() != proto::AttrType::BLOCK) {
attrs_[attr_name] = Attribute::GetAttrValue(attr);
// if (attr.type() == proto::AttrType::INT){
// std::cout << " attrName " << attr_name << " " <<
// attrs_[attr_name].Get<int>() << std::endl;
// }
}
}
}
const std::vector<std::string> &
OpDesc::Input(const std::string &name) const {
return inputs_.find(name)->second;
}
const std::vector<std::string> &
OpDesc::Output(const std::string &name) const {
return outputs_.find(name)->second;
}
Attribute OpDesc::GetAttr(const std::string &name) const {
auto it = attrs_.find(name);
return it->second;
}
const std::unordered_map<std::string, Attribute> &
OpDesc::GetAttrMap() const {
return attrs_;
}
} // namespace framework
} // namespace paddle_mobile
src/framework/op_desc.h
浏览文件 @ f1426f03
......@@ -23,29 +23,31 @@ SOFTWARE.
#include "paddle_mobile_object.h"
namespace paddle_mobile {
namespace framework {
namespace framework {
class OpDesc : PaddleMobileObject {
public:
OpDesc(const proto::OpDesc &desc);
const std::vector<std::string> &Input(const std::string &name) const;
const std::vector<std::string> &Output(const std::string &name) const;
Attribute GetAttr(const std::string &name) const;
class OpDesc : PaddleMobileObject {
public:
OpDesc(const proto::OpDesc &desc);
const std::vector<std::string> &
Input(const std::string &name) const;
const std::vector<std::string> &
Output(const std::string &name) const;
Attribute GetAttr(const std::string &name) const;
const VariableNameMap &GetInputs() { return inputs_; }
const VariableNameMap &GetInputs() { return inputs_; }
const VariableNameMap &GetOutputs() { return outputs_; }
const VariableNameMap &GetOutputs() { return outputs_; }
const AttributeMap &GetAttrMap() const;
const AttributeMap &GetAttrMap() const;
const std::string &Type() { return desc_.type(); };
const std::string &Type() { return desc_.type(); };
private:
proto::OpDesc desc_;
VariableNameMap inputs_;
VariableNameMap outputs_;
AttributeMap attrs_;
};
private:
proto::OpDesc desc_;
VariableNameMap inputs_;
VariableNameMap outputs_;
AttributeMap attrs_;
};
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/op_info.h
浏览文件 @ f1426f03
......@@ -22,70 +22,74 @@ SOFTWARE.
#include "framework.pb.h"
namespace paddle_mobile {
namespace framework {
template <typename Dtype> struct OpInfo {
OpCreator<Dtype> creator_;
const OpCreator<Dtype> &Creator() const {
// PADDLE_ENFORCE_NOT_NULL(creator_,
// "Operator Creator has not been registered");
return creator_;
}
};
template <typename Dtype> class OpInfoMap;
template <typename Dtype> static OpInfoMap<Dtype> *g_op_info_map = nullptr;
template <typename Dtype> class OpInfoMap {
public:
static OpInfoMap &Instance() {
if (g_op_info_map<Dtype> == nullptr) {
g_op_info_map<Dtype> = new OpInfoMap();
}
return *g_op_info_map<Dtype>;
};
bool Has(const std::string &op_type) const {
return map_.find(op_type) != map_.end();
}
void Insert(const std::string &type, const OpInfo<Dtype> &info) {
// PADDLE_ENFORCE(!Has(type), "Operator %s has been registered", type);
map_.insert({type, info});
}
const OpInfo<Dtype> &Get(const std::string &type) const {
auto op_info_ptr = GetNullable(type);
// PADDLE_ENFORCE_NOT_NULL(op_info_ptr, "Operator %s has not been
// registered",
// type);
return *op_info_ptr;
}
const OpInfo<Dtype> *GetNullable(const std::string &type) const {
auto it = map_.find(type);
if (it == map_.end()) {
return nullptr;
} else {
return &it->second;
}
}
const std::unordered_map<std::string, OpInfo<Dtype>> &map() const {
return map_;
}
std::unordered_map<std::string, OpInfo<Dtype>> *mutable_map() {
return &map_;
}
private:
OpInfoMap() = default;
std::unordered_map<std::string, OpInfo<Dtype>> map_;
// DISABLE_COPY_AND_ASSIGN(OpInfoMap);
};
} // namespace framework
namespace framework {
template <typename Dtype> struct OpInfo {
OpCreator<Dtype> creator_;
const OpCreator<Dtype> &Creator() const {
// PADDLE_ENFORCE_NOT_NULL(creator_,
// "Operator Creator has not been
// registered");
return creator_;
}
};
template <typename Dtype> class OpInfoMap;
template <typename Dtype>
static OpInfoMap<Dtype> *g_op_info_map = nullptr;
template <typename Dtype> class OpInfoMap {
public:
static OpInfoMap &Instance() {
if (g_op_info_map<Dtype> == nullptr) {
g_op_info_map<Dtype> = new OpInfoMap();
}
return *g_op_info_map<Dtype>;
};
bool Has(const std::string &op_type) const {
return map_.find(op_type) != map_.end();
}
void Insert(const std::string &type, const OpInfo<Dtype> &info) {
// PADDLE_ENFORCE(!Has(type), "Operator %s has been
// registered", type);
map_.insert({type, info});
}
const OpInfo<Dtype> &Get(const std::string &type) const {
auto op_info_ptr = GetNullable(type);
// PADDLE_ENFORCE_NOT_NULL(op_info_ptr, "Operator %s has not
// been
// registered",
// type);
return *op_info_ptr;
}
const OpInfo<Dtype> *GetNullable(const std::string &type) const {
auto it = map_.find(type);
if (it == map_.end()) {
return nullptr;
} else {
return &it->second;
}
}
const std::unordered_map<std::string, OpInfo<Dtype>> &map() const {
return map_;
}
std::unordered_map<std::string, OpInfo<Dtype>> *mutable_map() {
return &map_;
}
private:
OpInfoMap() = default;
std::unordered_map<std::string, OpInfo<Dtype>> map_;
// DISABLE_COPY_AND_ASSIGN(OpInfoMap);
};
} // namespace framework
} // namespace paddle_mobile
src/framework/op_kernel_type.h
浏览文件 @ f1426f03
......@@ -22,43 +22,51 @@ SOFTWARE.
#include "framework.pb.h"
namespace paddle_mobile {
namespace framework {
struct OpKernelType {
struct Hash {
size_t operator()(const OpKernelType &key) const {
int data_type = static_cast<int>(key.data_type_) << LEFT_SHIFT;
int data_layout = static_cast<int>(key.data_layout_) << (LEFT_SHIFT * 2);
namespace framework {
struct OpKernelType {
struct Hash {
size_t operator()(const OpKernelType &key) const {
int data_type = static_cast<int>(key.data_type_)
<< LEFT_SHIFT;
int data_layout = static_cast<int>(key.data_layout_)
<< (LEFT_SHIFT * 2);
std::hash<int> hasher;
return hasher(data_type + data_layout);
}
};
std::hash<int> hasher;
return hasher(data_type + data_layout);
}
};
// place, data_type, library_type kinds less than 2^8
constexpr static int LEFT_SHIFT = 8;
// place, data_type, library_type kinds less than 2^8
constexpr static int LEFT_SHIFT = 8;
proto::VarType::Type data_type_;
DataLayout data_layout_;
proto::VarType::Type data_type_;
DataLayout data_layout_;
OpKernelType(proto::VarType::Type data_type,
DataLayout data_layout = DataLayout::kAnyLayout)
: data_type_(data_type), data_layout_(data_layout) {}
OpKernelType(proto::VarType::Type data_type,
DataLayout data_layout = DataLayout::kAnyLayout)
: data_type_(data_type), data_layout_(data_layout) {}
bool operator==(const OpKernelType &o) const {
return data_type_ == o.data_type_ && data_layout_ == o.data_layout_;
}
bool operator==(const OpKernelType &o) const {
return data_type_ == o.data_type_ &&
data_layout_ == o.data_layout_;
}
bool operator!=(const OpKernelType &o) const { return !(*this == o); }
};
bool operator!=(const OpKernelType &o) const {
return !(*this == o);
}
};
inline bool NeedTransformLayout(const DataLayout &l, const DataLayout &r) {
return l != DataLayout::kAnyLayout && r != DataLayout::kAnyLayout && l != r;
}
inline bool NeedTransformLayout(const DataLayout &l,
const DataLayout &r) {
return l != DataLayout::kAnyLayout && r != DataLayout::kAnyLayout &&
l != r;
}
inline bool TransFromNeeded(const OpKernelType &l, const OpKernelType &r) {
return (l.data_type_ != r.data_type_) ||
NeedTransformLayout(l.data_layout_, r.data_layout_);
}
inline bool TransFromNeeded(const OpKernelType &l,
const OpKernelType &r) {
return (l.data_type_ != r.data_type_) ||
NeedTransformLayout(l.data_layout_, r.data_layout_);
}
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/op_proto_maker.h
浏览文件 @ f1426f03
......@@ -19,8 +19,8 @@ SOFTWARE.
#pragma once
namespace paddle_mobile {
namespace framework {
// this class not only make proto but also init attribute checkers.
class OpProtoAndCheckerMaker {};
} // namespace framework
namespace framework {
// this class not only make proto but also init attribute checkers.
class OpProtoAndCheckerMaker {};
} // namespace framework
} // namespace paddle_mobile
src/framework/operator.cpp
浏览文件 @ f1426f03
......@@ -20,26 +20,23 @@ SOFTWARE.
#include "op_info.h"
namespace paddle_mobile {
namespace framework {
namespace framework {
template <typename Dtype>
OperatorBase<Dtype>::OperatorBase(const std::string &type,
const VariableNameMap &inputs,
const VariableNameMap &outputs,
const AttributeMap &attrs,
std::shared_ptr<Scope> scope)
: type_(type), inputs_(inputs), outputs_(outputs), attrs_(attrs),
scope_(scope) {
CheckAllInputOutputSet();
}
template <typename Dtype>
OperatorBase<Dtype>::OperatorBase(const std::string &type,
const VariableNameMap &inputs,
const VariableNameMap &outputs,
const AttributeMap &attrs,
std::shared_ptr<Scope> scope)
: type_(type), inputs_(inputs), outputs_(outputs), attrs_(attrs),
scope_(scope) {
CheckAllInputOutputSet();
}
template <typename Dtype>
void OperatorBase<Dtype>::CheckAllInputOutputSet() const {}
template <typename Dtype> void OperatorBase<Dtype>::Run() { RunImpl(); }
template class OperatorBase<CPU>;
template class OperatorWithKernel<CPU>;
template <typename Dtype>
void OperatorBase<Dtype>::CheckAllInputOutputSet() const {}
template class OperatorBase<CPU>;
template class OperatorWithKernel<CPU>;
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/operator.h
浏览文件 @ f1426f03
......@@ -18,8 +18,6 @@ SOFTWARE.
#pragma once
#include <map>
#include "attribute.h"
#include "block_desc.h"
#include "common/type_define.h"
......@@ -31,62 +29,62 @@ SOFTWARE.
#include "scope.h"
#include "tensor.h"
#include "variable.h"
#include <map>
namespace paddle_mobile {
namespace framework {
template <typename Dtype> class OperatorBase : PaddleMobileObject {
public:
OperatorBase(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const AttributeMap &attrs,
std::shared_ptr<Scope> scope);
virtual ~OperatorBase() {}
virtual void Run();
const VariableNameMap &Inputs() const { return inputs_; }
const VariableNameMap &Outputs() const { return outputs_; }
const std::string &Type() const { return type_; }
const AttributeMap &Attrs() const { return attrs_; }
protected:
std::shared_ptr<Scope> scope_;
std::string type_;
VariableNameMap inputs_;
VariableNameMap outputs_;
AttributeMap attrs_;
namespace framework {
private:
void CheckAllInputOutputSet() const;
virtual void RunImpl() const = 0;
};
template <typename Dtype> class OperatorBase : PaddleMobileObject {
public:
OperatorBase(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs,
const AttributeMap &attrs,
std::shared_ptr<Scope> scope);
virtual ~OperatorBase() {}
virtual void Run() const = 0;
template <typename Dtype>
class OperatorWithKernel : public OperatorBase<Dtype> {
public:
OperatorWithKernel(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const AttributeMap &attrs,
std::shared_ptr<Scope> scope)
: OperatorBase<Dtype>(type, inputs, outputs, attrs, scope) {}
virtual void InferShape() const = 0;
const VariableNameMap &Inputs() const { return inputs_; }
const VariableNameMap &Outputs() const { return outputs_; }
const std::string &Type() const { return type_; }
const AttributeMap &Attrs() const { return attrs_; }
void ClearVariables() const {
if (this->scope_) {
this->scope_->EraseVars(this->inputs_.at("Filter"));
this->scope_->EraseVars(this->inputs_.at("Input"));
}
}
void ClearVariables() const {
if (this->scope_) {
this->scope_->EraseVars(this->inputs_.at("Filter"));
this->scope_->EraseVars(this->inputs_.at("Input"));
}
}
protected:
std::shared_ptr<Scope> scope_;
std::string type_;
VariableNameMap inputs_;
VariableNameMap outputs_;
AttributeMap attrs_;
protected:
virtual void RunImpl() const = 0;
private:
void CheckAllInputOutputSet() const;
};
private:
};
template <typename Dtype>
class OperatorWithKernel : public OperatorBase<Dtype> {
public:
OperatorWithKernel(const std::string &type,
const VariableNameMap &inputs,
const VariableNameMap &outputs,
const AttributeMap &attrs,
std::shared_ptr<Scope> scope)
: OperatorBase<Dtype>(type, inputs, outputs, attrs, scope) {}
virtual void InferShape() const = 0;
virtual void Run() const = 0;
};
template <typename Dtype, typename P> class OpKernelBase : PaddleMobileObject {
public:
virtual void Compute(const P &para) const = 0;
template <typename Dtype, typename P>
class OpKernelBase : PaddleMobileObject {
public:
virtual void Compute(const P &para) const = 0;
virtual ~OpKernelBase() = default;
};
virtual ~OpKernelBase() = default;
};
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/paddle_mobile_object.h
浏览文件 @ f1426f03
......@@ -23,14 +23,14 @@ SOFTWARE.
namespace paddle_mobile {
class PaddleMobileObject {
public:
virtual inline const std::string &ToString() {
char address[128] = {0};
sprintf(address, "%p", this);
return std::string(address);
}
class PaddleMobileObject {
public:
virtual inline const std::string &ToString() {
char address[128] = {0};
sprintf(address, "%p", this);
return std::string(address);
}
private:
};
private:
};
} // namespace paddle_mobile
src/framework/program.cpp
浏览文件 @ f1426f03
......@@ -17,5 +17,5 @@ SOFTWARE.
==============================================================================*/
namespace paddle_mobile {
namespace framework {}
namespace framework {}
} // namespace paddle_mobile
src/framework/program.h
浏览文件 @ f1426f03
......@@ -24,17 +24,17 @@ SOFTWARE.
#include "scope.h"
namespace paddle_mobile {
namespace framework {
namespace framework {
template <typename Dtype, Precision P = Precision::FP32>
class Program : PaddleMobileObject {
public:
std::shared_ptr<ProgramDesc> originProgram;
std::shared_ptr<ProgramDesc> optimizeProgram;
std::shared_ptr<Scope> scope;
template <typename Dtype, Precision P = Precision::FP32>
class Program : PaddleMobileObject {
public:
std::shared_ptr<ProgramDesc> originProgram;
std::shared_ptr<ProgramDesc> optimizeProgram;
std::shared_ptr<Scope> scope;
private:
};
private:
};
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/program_desc.cpp
浏览文件 @ f1426f03
......@@ -5,18 +5,18 @@
#include "program_desc.h"
namespace paddle_mobile {
namespace framework {
namespace framework {
ProgramDesc::ProgramDesc(const proto::ProgramDesc &desc) : desc_(desc) {
for (auto &block_desc : *desc_.mutable_blocks()) {
// new framework::BlockDesc(block_desc)
blocks_.emplace_back(std::make_shared<BlockDesc>(block_desc));
}
}
ProgramDesc::ProgramDesc(const proto::ProgramDesc &desc) : desc_(desc) {
for (auto &block_desc : *desc_.mutable_blocks()) {
// new framework::BlockDesc(block_desc)
blocks_.emplace_back(std::make_shared<BlockDesc>(block_desc));
}
}
std::shared_ptr<BlockDesc> ProgramDesc::Block(size_t idx) {
return blocks_[idx];
}
std::shared_ptr<BlockDesc> ProgramDesc::Block(size_t idx) {
return blocks_[idx];
}
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/program_desc.h
浏览文件 @ f1426f03
......@@ -25,18 +25,20 @@ SOFTWARE.
#include "paddle_mobile_object.h"
namespace paddle_mobile {
namespace framework {
namespace framework {
class ProgramDesc : PaddleMobileObject {
public:
ProgramDesc(const proto::ProgramDesc &desc);
std::shared_ptr<BlockDesc> Block(size_t idx);
const std::vector<std::shared_ptr<BlockDesc>> &Blocks() { return blocks_; };
class ProgramDesc : PaddleMobileObject {
public:
ProgramDesc(const proto::ProgramDesc &desc);
std::shared_ptr<BlockDesc> Block(size_t idx);
const std::vector<std::shared_ptr<BlockDesc>> &Blocks() {
return blocks_;
};
private:
std::vector<std::shared_ptr<BlockDesc>> blocks_;
proto::ProgramDesc desc_;
};
private:
std::vector<std::shared_ptr<BlockDesc>> blocks_;
proto::ProgramDesc desc_;
};
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/scope.cc
浏览文件 @ f1426f03
......@@ -4,113 +4,116 @@
#include <vector>
namespace paddle_mobile {
namespace framework {
namespace framework {
Scope &Scope::NewScope() const {
std::unique_lock<std::mutex> lock(mutex_);
kids_.push_back(new Scope(this));
return *kids_.back();
}
Scope &Scope::NewScope() const {
std::unique_lock<std::mutex> lock(mutex_);
kids_.push_back(new Scope(this));
return *kids_.back();
}
Variable *Scope::Var(const std::string &name) {
auto *pvar = FindVarLocally(name);
if (pvar != nullptr) {
return pvar;
};
pvar = new Variable;
vars_[name] = pvar;
pvar->name_ = &(vars_.find(name)->first);
return pvar;
}
Variable *Scope::Var(const std::string &name) {
auto *pvar = FindVarLocally(name);
if (pvar != nullptr) {
return pvar;
};
pvar = new Variable;
vars_[name] = pvar;
pvar->name_ = &(vars_.find(name)->first);
return pvar;
}
// Variable* Scope::Var(std::string* name) {
// auto var_name = string::Sprintf("%p.%d", this, vars_.size());
// if (name != nullptr) {
// *name = var_name;
// }
// return Var(var_name);
// }
// Variable* Scope::Var(std::string* name) {
// auto var_name = string::Sprintf("%p.%d", this,
// vars_.size());
// if (name != nullptr) {
// *name = var_name;
// }
// return Var(var_name);
// }
Variable *Scope::FindVar(const std::string &name) const {
auto *pvar = FindVarLocally(name);
if (pvar != nullptr) {
return pvar;
}
return (parent_ == nullptr) ? nullptr : parent_->FindVar(name);
}
Variable *Scope::FindVar(const std::string &name) const {
auto *pvar = FindVarLocally(name);
if (pvar != nullptr) {
return pvar;
}
return (parent_ == nullptr) ? nullptr : parent_->FindVar(name);
}
const Scope *Scope::FindScope(const Variable *var) const {
for (auto &name_var : vars_) {
if (name_var.second == var) {
return this;
}
}
return (parent_ == nullptr) ? nullptr : parent_->FindScope(var);
}
const Scope *Scope::FindScope(const Variable *var) const {
for (auto &name_var : vars_) {
if (name_var.second == var) {
return this;
}
}
return (parent_ == nullptr) ? nullptr : parent_->FindScope(var);
}
void Scope::DropKids() {
for (Scope *s : kids_) {
delete s;
}
kids_.clear();
}
void Scope::DropKids() {
for (Scope *s : kids_) {
delete s;
}
kids_.clear();
}
std::vector<std::string> Scope::LocalVarNames() const {
std::vector<std::string> known_vars;
known_vars.reserve(vars_.size());
for (auto &name_var : vars_) {
known_vars.emplace_back(name_var.first);
}
return known_vars;
}
std::vector<std::string> Scope::LocalVarNames() const {
std::vector<std::string> known_vars;
known_vars.reserve(vars_.size());
for (auto &name_var : vars_) {
known_vars.emplace_back(name_var.first);
}
return known_vars;
}
void Scope::DeleteScope(Scope *scope) const {
std::unique_lock<std::mutex> lock(mutex_);
auto it = std::find(kids_.begin(), kids_.end(), scope);
kids_.erase(it);
delete scope;
// deferent
}
void Scope::DeleteScope(Scope *scope) const {
std::unique_lock<std::mutex> lock(mutex_);
auto it = std::find(kids_.begin(), kids_.end(), scope);
kids_.erase(it);
delete scope;
// deferent
}
void Scope::EraseVars(const std::vector<std::string> &var_names) {
std::set<std::string> var_set(var_names.begin(), var_names.end());
for (auto it = vars_.begin(); it != vars_.end();) {
if (var_set.find(it->first) != var_set.end()) {
delete it->second;
it = vars_.erase(it);
} else {
++it;
}
}
}
void Scope::EraseVars(const std::vector<std::string> &var_names) {
std::set<std::string> var_set(var_names.begin(), var_names.end());
for (auto it = vars_.begin(); it != vars_.end();) {
if (var_set.find(it->first) != var_set.end()) {
delete it->second;
it = vars_.erase(it);
} else {
++it;
}
}
}
void Scope::Rename(const std::string &origin_name,
const std::string &new_name) const {
auto origin_it = vars_.find(origin_name);
if (origin_it == vars_.end()) {
return;
}
auto new_it = vars_.find(new_name);
if (new_it != vars_.end()) {
return;
}
vars_[new_name] = origin_it->second;
vars_.erase(origin_it);
}
//
// std::string Scope::Rename(const std::string& origin_name) const {
// auto var_name = string::Sprintf("%p.%d", this, vars_.size());
// Rename(origin_name, var_name);
// return var_name;
// }
void Scope::Rename(const std::string &origin_name,
const std::string &new_name) const {
auto origin_it = vars_.find(origin_name);
if (origin_it == vars_.end()) {
return;
}
auto new_it = vars_.find(new_name);
if (new_it != vars_.end()) {
return;
}
vars_[new_name] = origin_it->second;
vars_.erase(origin_it);
}
//
// std::string Scope::Rename(const std::string& origin_name)
// const {
// auto var_name = string::Sprintf("%p.%d", this,
// vars_.size());
// Rename(origin_name, var_name);
// return var_name;
// }
Variable *Scope::FindVarLocally(const std::string &name) const {
auto it = vars_.find(name);
if (it != vars_.end()) {
return it->second;
}
return nullptr;
}
Variable *Scope::FindVarLocally(const std::string &name) const {
auto it = vars_.find(name);
if (it != vars_.end()) {
return it->second;
}
return nullptr;
}
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/scope.h
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#include <unordered_map> //std::unordered_map
namespace paddle_mobile {
namespace framework {
class Scope {
public:
Scope() {}
~Scope() {}
namespace framework {
class Scope {
public:
Scope() {}
~Scope() {}
Scope &NewScope() const;
Scope &NewScope() const;
/// Create a variable with given name if it doesn't exist.
Variable *Var(const std::string &name);
/// Create a variable with given name if it doesn't exist.
Variable *Var(const std::string &name);
/// Create a variable with a scope-unique name.
Variable *Var(std::string *name = nullptr);
/// Create a variable with a scope-unique name.
Variable *Var(std::string *name = nullptr);
void EraseVars(const std::vector<std::string> &var_names);
void EraseVars(const std::vector<std::string> &var_names);
/// Find a variable in the scope or any of its ancestors. Returns
/// nullptr if cannot find.
Variable *FindVar(const std::string &name) const;
/// Find a variable in the scope or any of its ancestors. Returns
/// nullptr if cannot find.
Variable *FindVar(const std::string &name) const;
const Scope *parent() const { return parent_; }
const Scope *parent() const { return parent_; }
/// Find the scope or an ancestor scope that contains the given variable.
const Scope *FindScope(const Variable *var) const;
/// Find the scope or an ancestor scope that contains the given
/// variable.
const Scope *FindScope(const Variable *var) const;
void DeleteScope(Scope *scope) const;
void DeleteScope(Scope *scope) const;
/// Drop all kids scopes belonged to this scope.
void DropKids();
/// Drop all kids scopes belonged to this scope.
void DropKids();
// enumerate all the variables current contains.
std::vector<std::string> LocalVarNames() const;
// enumerate all the variables current contains.
std::vector<std::string> LocalVarNames() const;
// Rename variable to a new name
void Rename(const std::string &origin_name,
const std::string &new_name) const;
// Rename variable to a new name
void Rename(const std::string &origin_name,
const std::string &new_name) const;
// Rename variable to a new name and return the new name
std::string Rename(const std::string &origin_name) const;
// Rename variable to a new name and return the new name
std::string Rename(const std::string &origin_name) const;
Variable *FindVarLocally(const std::string &name) const;
Variable *FindVarLocally(const std::string &name) const;
private:
// Call Scope::NewScope for a sub-scope.
explicit Scope(Scope const *parent) : parent_(parent) {}
private:
// Call Scope::NewScope for a sub-scope.
explicit Scope(Scope const *parent) : parent_(parent) {}
mutable std::unordered_map<std::string, Variable *> vars_;
mutable std::list<Scope *> kids_;
Scope const *parent_{nullptr};
mutable std::unordered_map<std::string, Variable *> vars_;
mutable std::list<Scope *> kids_;
Scope const *parent_{nullptr};
mutable std::mutex mutex_;
};
} // namespace framework
mutable std::mutex mutex_;
};
} // namespace framework
} // namespace paddle_mobile
src/framework/selected_rows.h
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#include "tensor.h"
namespace paddle_mobile {
namespace framework {
namespace framework {
class SelectedRows {
public:
SelectedRows(const std::vector<int64_t> &rows, const int64_t &height)
: rows_(rows), height_(height) {
value_.reset(new Tensor());
}
class SelectedRows {
public:
SelectedRows(const std::vector<int64_t> &rows,
const int64_t &height)
: rows_(rows), height_(height) {
value_.reset(new Tensor());
}
SelectedRows() {
height_ = 0;
value_.reset(new Tensor());
}
SelectedRows() {
height_ = 0;
value_.reset(new Tensor());
}
const Tensor &value() const { return *value_; }
const Tensor &value() const { return *value_; }
Tensor *mutable_value() { return value_.get(); }
Tensor *mutable_value() { return value_.get(); }
int64_t height() const { return height_; }
int64_t height() const { return height_; }
void set_height(int64_t height) { height_ = height; }
void set_height(int64_t height) { height_ = height; }
const std::vector<int64_t> &rows() const { return rows_; }
const std::vector<int64_t> &rows() const { return rows_; }
std::vector<int64_t> *mutable_rows() { return &rows_; }
std::vector<int64_t> *mutable_rows() { return &rows_; }
void set_rows(const std::vector<int64_t> &rows) { rows_ = rows; }
void set_rows(const std::vector<int64_t> &rows) { rows_ = rows; }
/**
* get the index of id in rows
*/
int64_t index(int64_t id) const {
auto it = std::find(rows_.begin(), rows_.end(), id);
// PADDLE_ENFORCE(it != rows_.end(), "id should be in rows");
return static_cast<int64_t>(std::distance(rows_.begin(), it));
}
/**
* get the index of id in rows
*/
int64_t index(int64_t id) const {
auto it = std::find(rows_.begin(), rows_.end(), id);
// PADDLE_ENFORCE(it != rows_.end(), "id should be in rows");
return static_cast<int64_t>(std::distance(rows_.begin(), it));
}
DDim GetCompleteDims() const {
std::vector<int64_t> dims = vectorize(value_->dims());
dims[0] = height_;
return make_ddim(dims);
}
DDim GetCompleteDims() const {
std::vector<int64_t> dims = vectorize(value_->dims());
dims[0] = height_;
return make_ddim(dims);
}
private:
// Notice: rows can be duplicate. We can have {0, 4, 7, 0, 5, 7, 9} here.
// SelectedRows are simply concated when adding together. Until a
// SelectedRows add a Tensor, will the duplicate rows be handled.
std::vector<int64_t> rows_;
std::unique_ptr<Tensor> value_{nullptr};
int64_t height_;
};
private:
// Notice: rows can be duplicate. We can have {0, 4, 7, 0, 5, 7, 9}
// here.
// SelectedRows are simply concated when adding together. Until a
// SelectedRows add a Tensor, will the duplicate rows be handled.
std::vector<int64_t> rows_;
std::unique_ptr<Tensor> value_{nullptr};
int64_t height_;
};
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/tensor.h
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src/framework/tensor_util.cc
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src/framework/tensor_util.h
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#include <vector>
namespace paddle_mobile {
namespace framework {
namespace framework {
void TensorCopy(const Tensor &src, Tensor *dst);
void TensorCopySync(const Tensor &src, Tensor *dst);
void TensorCopy(const Tensor &src, Tensor *dst);
void TensorCopySync(const Tensor &src, Tensor *dst);
template <typename T>
void TensorFromVector(const std::vector<T> &src, Tensor *dst);
template <typename T>
void TensorFromVector(const std::vector<T> &src, Tensor *dst);
template <typename T>
void TesnorToVector(const Tensor &src, std::vector<T> *dst);
template <typename T>
void TesnorToVector(const Tensor &src, std::vector<T> *dst);
bool TensorContainsNAN(const framework::Tensor &tensor);
bool TensorContainsInf(const framework::Tensor &tensor);
bool TensorContainsNAN(const framework::Tensor &tensor);
bool TensorContainsInf(const framework::Tensor &tensor);
void TensorToStream(std::ostream &os, const Tensor &tensor);
void TensorFromStream(std::istream &is, Tensor *tensor);
void TensorToStream(std::ostream &os, const Tensor &tensor);
void TensorFromStream(std::istream &is, Tensor *tensor);
//
// The implementation of template functions.
//
//
// The implementation of template functions.
//
template <typename T>
void TensorFromVector(const std::vector<T> &src, Tensor *dst) {
auto src_ptr = static_cast<const void *>(src.data());
dst->Resize({static_cast<int64_t>(src.size())});
auto dst_ptr = static_cast<void *>(dst->mutable_data<T>());
auto size = src.size() * sizeof(T);
template <typename T>
void TensorFromVector(const std::vector<T> &src, Tensor *dst) {
auto src_ptr = static_cast<const void *>(src.data());
dst->Resize({static_cast<int64_t>(src.size())});
auto dst_ptr = static_cast<void *>(dst->mutable_data<T>());
auto size = src.size() * sizeof(T);
memory::Copy(dst_ptr, src_ptr, size);
}
memory::Copy(dst_ptr, src_ptr, size);
}
template <typename T>
void TensorToVector(const Tensor &src, std::vector<T> *dst) {
auto src_ptr = static_cast<const void *>(src.data<T>());
auto size = src.numel() * sizeof(T);
template <typename T>
void TensorToVector(const Tensor &src, std::vector<T> *dst) {
auto src_ptr = static_cast<const void *>(src.data<T>());
auto size = src.numel() * sizeof(T);
dst->resize(src.numel());
auto dst_ptr = static_cast<void *>(dst->data());
dst->resize(src.numel());
auto dst_ptr = static_cast<void *>(dst->data());
memory::Copy(dst_ptr, src_ptr, size);
}
memory::Copy(dst_ptr, src_ptr, size);
}
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/var_desc.cpp
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namespace paddle_mobile {
namespace framework {
namespace framework {
VarDesc::VarDesc(const proto::VarDesc &desc) : desc_(desc) {}
VarDesc::VarDesc(const proto::VarDesc &desc) : desc_(desc) {}
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/var_desc.h
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src/framework/var_type.h
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#include "variable.h"
namespace paddle_mobile {
namespace framework {
inline proto::VarType::Type ToVarType(std::type_index type) {
if (type.hash_code() == typeid(LoDTensor).hash_code()) {
return proto::VarType_Type_LOD_TENSOR;
} else if (type.hash_code() == typeid(SelectedRows).hash_code()) {
return proto::VarType_Type_SELECTED_ROWS;
} else {
// PADDLE_THROW("ToVarType:Unsupported type %s", type.name());
}
}
namespace framework {
inline proto::VarType::Type ToVarType(std::type_index type) {
if (type.hash_code() == typeid(LoDTensor).hash_code()) {
return proto::VarType_Type_LOD_TENSOR;
} else if (type.hash_code() == typeid(SelectedRows).hash_code()) {
return proto::VarType_Type_SELECTED_ROWS;
} else {
// PADDLE_THROW("ToVarType:Unsupported type %s",
// type.name());
}
}
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/variable.h
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src/io.cpp
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src/io.h
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namespace paddle_mobile {
template <typename Dtype, Precision P = Precision::FP32>
class Loader : PaddleMobileObject {
public:
const framework::Program<Dtype, P> Load(const std::string &dirname);
template <typename Dtype, Precision P = Precision::FP32>
class Loader : PaddleMobileObject {
public:
const framework::Program<Dtype, P> Load(const std::string &dirname);
private:
void LoadVar(framework::LoDTensor *tensor, const std::string &file_path);
};
private:
void LoadVar(framework::LoDTensor *tensor,
const std::string &file_path);
};
} // namespace paddle_mobile
src/memory/t_malloc.cc
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#include <cstring>
namespace paddle_mobile {
namespace memory {
const int MALLOC_ALIGN = 16;
namespace memory {
const int MALLOC_ALIGN = 16;
void Copy(void *dst, const void *src, size_t num) {
std::memcpy(dst, src, num);
};
void Copy(void *dst, const void *src, size_t num) {
std::memcpy(dst, src, num);
};
void *Alloc(size_t size) {
size_t offset = sizeof(void *) + MALLOC_ALIGN - 1;
char *p = static_cast<char *>(malloc(offset + size));
if (!p) {
return nullptr;
}
void *r = reinterpret_cast<void *>(reinterpret_cast<size_t>(p + offset) &
(~(MALLOC_ALIGN - 1)));
static_cast<void **>(r)[-1] = p;
return r;
}
void *Alloc(size_t size) {
size_t offset = sizeof(void *) + MALLOC_ALIGN - 1;
char *p = static_cast<char *>(malloc(offset + size));
if (!p) {
return nullptr;
}
void *r = reinterpret_cast<void *>(
reinterpret_cast<size_t>(p + offset) & (~(MALLOC_ALIGN - 1)));
static_cast<void **>(r)[-1] = p;
return r;
}
void Free(void *ptr) {
if (ptr) {
free(static_cast<void **>(ptr)[-1]);
}
}
void Free(void *ptr) {
if (ptr) {
free(static_cast<void **>(ptr)[-1]);
}
}
} // namespace memory
} // namespace memory
} // namespace paddle_mobile
src/memory/t_malloc.h
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