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提交 47e5978f 编写于 作者: 朔-望's avatar 朔-望
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add clang-format clang-tidy hook

上级 ab5a35c2
隐藏空白更改
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Showing with 17667 addition and 18947 deletion
.pre-commit-config.yaml
浏览文件 @ 47e5978f
......@@ -20,11 +20,20 @@ repos:
- id: trailing-whitespace
files: (src).*\.(md|py|mm|swift|java|c|cc|cxx|cpp|cu|h|hpp|hxx)$
- repo: local
hooks:
- id: clang-format
name: clang-format
description: Format files with ClangFormat.
entry: bash ./tools/pre-commit.hooks/.clang-format.hook -i
language: system
files: \.(c|cc|cxx|cpp|h|hpp|hxx)$
- repo: local
hooks:
- id: clang-tidy
name: clang-tidy
description: Format files with tidy.
description: Check C++ code style using clang-tidy.
entry: bash ./tools/pre-commit.hooks/.clang-tidy.hook -i
language: system
files: (src).*\.(c|cc|cxx|cpp|h|hpp|hxx)$
......
src/common/log.h
浏览文件 @ 47e5978f
......@@ -27,146 +27,145 @@ SOFTWARE.
namespace paddle_mobile {
enum LogLevel {
kNO_LOG,
kLOG_ERROR,
kLOG_WARNING,
kLOG_INFO,
kLOG_DEBUG,
kLOG_DEBUG1,
kLOG_DEBUG2,
kLOG_DEBUG3,
kLOG_DEBUG4
};
// log level
static LogLevel log_level = kLOG_DEBUG4;
static std::vector<std::string> logs{"NO", "ERROR ", "WARNING",
"INFO ", "DEBUG ", "DEBUG1 ",
"DEBUG2 ", "DEBUG3 ", "DEBUG4 "};
struct ToLog;
struct Print;
struct Print {
friend struct ToLog;
template <typename T> Print &operator<<(T const &value) {
buffer_ << value;
return *this;
enum LogLevel {
kNO_LOG,
kLOG_ERROR,
kLOG_WARNING,
kLOG_INFO,
kLOG_DEBUG,
kLOG_DEBUG1,
kLOG_DEBUG2,
kLOG_DEBUG3,
kLOG_DEBUG4
};
// log level
static LogLevel log_level = kLOG_DEBUG4;
static std::vector<std::string> logs{"NO", "ERROR ", "WARNING",
"INFO ", "DEBUG ", "DEBUG1 ",
"DEBUG2 ", "DEBUG3 ", "DEBUG4 "};
struct ToLog;
struct Print;
struct Print {
friend struct ToLog;
template <typename T> Print &operator<<(T const &value) {
buffer_ << value;
return *this;
}
private:
void print(LogLevel level) {
buffer_ << std::endl;
if (level == kLOG_ERROR) {
std::cerr << buffer_.str();
} else {
std::cout << buffer_.str();
}
private:
void print(LogLevel level) {
buffer_ << std::endl;
if (level == kLOG_ERROR) {
std::cerr << buffer_.str();
} else {
std::cout << buffer_.str();
}
}
std::ostringstream buffer_;
};
struct ToLog {
ToLog(LogLevel level = kLOG_DEBUG, const std::string &info = "")
: level_(level) {
unsigned blanks =
(unsigned)(level > kLOG_DEBUG ? (level - kLOG_DEBUG) * 4 : 1);
printer_ << logs[level] << " " << info << ":"
<< std::string(blanks, ' ');
}
template <typename T> ToLog &operator<<(T const &value) {
printer_ << value;
return *this;
}
~ToLog() { printer_.print(level_); }
private:
LogLevel level_;
Print printer_;
};
}
std::ostringstream buffer_;
};
struct ToLog {
ToLog(LogLevel level = kLOG_DEBUG, const std::string &info = "")
: level_(level) {
unsigned blanks =
(unsigned)(level > kLOG_DEBUG ? (level - kLOG_DEBUG) * 4 : 1);
printer_ << logs[level] << " " << info << ":"
<< std::string(blanks, ' ');
}
template <typename T> ToLog &operator<<(T const &value) {
printer_ << value;
return *this;
}
~ToLog() { printer_.print(level_); }
private:
LogLevel level_;
Print printer_;
};
#define LOG(level) \
if (level > paddle_mobile::log_level) { \
} else \
paddle_mobile::ToLog( \
level, \
(std::stringstream() \
<< "[file: " \
<< (strrchr(__FILE__, '/') ? (strrchr(__FILE__, '/') + 1) : __FILE__) \
<< "] [line: " << __LINE__ << "] ") \
.str())
paddle_mobile::ToLog( \
level, (std::stringstream() \
<< "[file: " \
<< (strrchr(__FILE__, '/') ? (strrchr(__FILE__, '/') + 1) \
: __FILE__) \
<< "] [line: " << __LINE__ << "] ") \
.str())
#define DLOG \
if (paddle_mobile::kLOG_DEBUG > paddle_mobile::log_level) { \
} else \
paddle_mobile::ToLog( \
paddle_mobile::kLOG_DEBUG, \
(std::stringstream() \
<< "[file: " \
<< (strrchr(__FILE__, '/') ? (strrchr(__FILE__, '/') + 1) : __FILE__) \
<< "] [line: " << __LINE__ << "] ") \
.str())
}
paddle_mobile::ToLog( \
paddle_mobile::kLOG_DEBUG, \
(std::stringstream() \
<< "[file: " \
<< (strrchr(__FILE__, '/') ? (strrchr(__FILE__, '/') + 1) \
: __FILE__) \
<< "] [line: " << __LINE__ << "] ") \
.str())
} // namespace paddle_mobile
#define LOGF(level, format, ...) \
if (level > paddle_mobile::log_level) { \
} else \
printf(format, ##__VA_ARGS__)
printf(format, ##__VA_ARGS__)
#define DLOGF(format, ...) \
if (paddle_mobile::kLOG_DEBUG > paddle_mobile::log_level) { \
} else \
printf(format, ##__VA_ARGS__)
printf(format, ##__VA_ARGS__)
#else
namespace paddle_mobile {
enum LogLevel {
kNO_LOG,
kLOG_ERROR,
kLOG_WARNING,
kLOG_INFO,
kLOG_DEBUG,
kLOG_DEBUG1,
kLOG_DEBUG2,
kLOG_DEBUG3,
kLOG_DEBUG4
};
struct ToLog;
struct Print {
friend struct ToLog;
template <typename T> Print &operator<<(T const &value) {}
private:
};
struct ToLog {
ToLog(LogLevel level) {}
template <typename T> ToLog &operator<<(T const &value) {
return *this;
}
};
enum LogLevel {
kNO_LOG,
kLOG_ERROR,
kLOG_WARNING,
kLOG_INFO,
kLOG_DEBUG,
kLOG_DEBUG1,
kLOG_DEBUG2,
kLOG_DEBUG3,
kLOG_DEBUG4
};
struct ToLog;
struct Print {
friend struct ToLog;
template <typename T> Print &operator<<(T const &value) {}
private:
};
struct ToLog {
ToLog(LogLevel level) {}
template <typename T> ToLog &operator<<(T const &value) { return *this; }
};
#define LOG(level) \
if (true) { \
} else \
paddle_mobile::ToLog(level)
paddle_mobile::ToLog(level)
#define DLOG \
if (true) { \
} else \
paddle_mobile::ToLog(paddle_mobile::kLOG_DEBUG)
paddle_mobile::ToLog(paddle_mobile::kLOG_DEBUG)
#define LOGF(level, format, ...)
#define DLOGF(format, ...)
}
} // namespace paddle_mobile
#endif
src/common/type_define.h
浏览文件 @ 47e5978f
......@@ -24,30 +24,29 @@ SOFTWARE.
namespace paddle_mobile {
namespace framework {
template<typename Dtype> class OperatorBase;
template <typename Dtype> class OperatorBase;
class OpDesc;
class BlockDesc;
class InferShapeContext;
}
} // namespace framework
using VariableNameMap = std::map<std::string, std::vector<std::string>>;
template<typename Dtype>
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)>;
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 InferVarTypeFN = std::function<void(const framework::OpDesc & /*op_desc*/,
framework::BlockDesc * /*block*/)>;
using InferShapeFN = std::function<void(framework::InferShapeContext *)>;
};
}; // namespace paddle_mobile
src/common/types.h
浏览文件 @ 47e5978f
......@@ -24,7 +24,7 @@ enum class Precision : int { FP32 = 0 };
//! 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;
......@@ -32,32 +32,32 @@ 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
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. */
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. */
};
}
} // namespace paddle_mobile
src/common/variant.h
浏览文件 @ 47e5978f
......@@ -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.h
浏览文件 @ 47e5978f
......@@ -27,104 +27,102 @@ namespace framework {
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;
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;
}
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>;
class AttrReader {
public:
explicit AttrReader(const AttributeMap &attrs) : attrs_(attrs) {}
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
......
src/framework/block_desc.cpp
浏览文件 @ 47e5978f
......@@ -22,28 +22,28 @@ namespace paddle_mobile {
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>> 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>> 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));
}
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
......
src/framework/block_desc.h
浏览文件 @ 47e5978f
......@@ -27,32 +27,29 @@ namespace paddle_mobile {
namespace framework {
class BlockDesc : PaddleMobileObject {
public:
BlockDesc(const proto::BlockDesc &desc);
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
......@@ -60,14 +57,14 @@ private:
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 @ 47e5978f
......@@ -22,42 +22,45 @@ namespace paddle_mobile {
namespace framework {
enum class DataLayout {
kNHWC = 0,
kNCHW = 1,
kAnyLayout = 2,
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]);
}
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;
}
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
......
src/framework/data_transform.cpp
浏览文件 @ 47e5978f
......@@ -24,68 +24,68 @@ namespace paddle_mobile {
namespace framework {
static void PassTensorData(Tensor *from, Tensor *to) {
to->ShareDataWith(*from);
*from = Tensor();
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;
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
......
src/framework/data_transform.h
浏览文件 @ 47e5978f
......@@ -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 @ 47e5978f
......@@ -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 @ 47e5978f
......@@ -15,320 +15,318 @@ 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);
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;
}
}
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;
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;
}
// 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;
--end;
if (end > 0) {
this->operator()(dim.tail);
}
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
}
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 @ 47e5978f
......@@ -22,145 +22,142 @@ 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
浏览文件 @ 47e5978f
......@@ -21,410 +21,392 @@
#include "platform/hostdevice.h"
namespace paddle_mobile {
namespace framework {
namespace framework {
// Statically sized, statically indexed dimension
template <int i> struct Dim {
static constexpr int dimensions = i;
// Statically sized, statically indexed dimension
template <int i> struct Dim {
static constexpr int dimensions = i;
template <typename... Args>
HOSTDEVICE Dim(int64_t _head, Args... _tail)
: head(_head), tail(_tail...) {
static_assert(
sizeof...(_tail) == i - 1,
"Dim initialized with the wrong number of parameters");
}
template <typename... Args>
HOSTDEVICE Dim(int64_t _head, Args... _tail) : head(_head), tail(_tail...) {
static_assert(sizeof...(_tail) == i - 1,
"Dim initialized with the wrong number of parameters");
}
HOSTDEVICE
Dim(int64_t _head, const Dim<i - 1> &_tail)
: head(_head), tail(_tail) {}
HOSTDEVICE
Dim(int64_t _head, const Dim<i - 1> &_tail) : head(_head), tail(_tail) {}
HOSTDEVICE
Dim() : head(0), tail() {}
HOSTDEVICE
Dim() : head(0), tail() {}
/** Construct a Dim from a linear index and size. Uses Fortran
* order
* indexing. */
HOSTDEVICE
Dim(int64_t idx, const Dim<i> &size)
: head(idx % size.head), tail(idx / size.head, size.tail) {}
/** Construct a Dim from a linear index and size. Uses Fortran
* order
* indexing. */
HOSTDEVICE
Dim(int64_t idx, const Dim<i> &size)
: head(idx % size.head), tail(idx / size.head, size.tail) {}
/** Construct a Dim with each dimension set to the given index */
HOSTDEVICE
Dim(int64_t idx) : head(idx), tail(idx) {}
/** Construct a Dim with each dimension set to the given index */
HOSTDEVICE
Dim(int64_t idx) : head(idx), tail(idx) {}
HOSTDEVICE
bool operator==(const Dim<i> &o) const {
return (head == o.head) && (tail == o.tail);
}
HOSTDEVICE
bool operator==(const Dim<i> &o) const {
return (head == o.head) && (tail == o.tail);
}
HOSTDEVICE
bool operator!=(const Dim<i> &o) const { return !(*this == o); }
HOSTDEVICE
bool operator!=(const Dim<i> &o) const { return !(*this == o); }
HOSTDEVICE
int64_t &operator[](int idx);
HOSTDEVICE
int64_t operator[](int idx) const;
HOSTDEVICE
int64_t &operator[](int idx);
HOSTDEVICE
int64_t operator[](int idx) const;
HOST std::string to_string() const;
HOST std::string to_string() const;
int64_t head;
Dim<i - 1> tail;
};
int64_t head;
Dim<i - 1> tail;
};
// Base case specialization
template <> struct Dim<0> {
static constexpr int dimensions = 0;
// Base case specialization
template <> struct Dim<0> {
static constexpr int dimensions = 0;
HOSTDEVICE
Dim(int64_t _head) {}
HOSTDEVICE
Dim(int64_t _head) {}
HOSTDEVICE
Dim() {}
HOSTDEVICE
Dim() {}
HOSTDEVICE
Dim(int idx, const Dim<0> &size) {
HOSTDEVICE
Dim(int idx, const Dim<0> &size) {
#ifndef __CUDA_ARCH__
if (idx > 0) {
throw std::invalid_argument("Index out of range.");
}
if (idx > 0) {
throw std::invalid_argument("Index out of range.");
}
#else
PADDLE_ASSERT(idx == 0);
PADDLE_ASSERT(idx == 0);
#endif
}
HOSTDEVICE
bool operator==(const Dim<0> &o) const { return true; }
HOSTDEVICE
bool operator!=(const Dim<0> &o) const { return false; }
HOSTDEVICE
int64_t &operator[](int idx);
HOSTDEVICE
int64_t operator[](int idx) const;
};
namespace {
// Helper for accessing Dim classes
template <int i> struct DimGetter {
// Return a copy if Dim is const
template <typename D>
HOSTDEVICE static int64_t impl(const D &d) {
return DimGetter<i - 1>::impl(d.tail);
}
// Return a reference if Dim is mutable
template <typename D> HOSTDEVICE static int64_t &impl(D &d) {
return DimGetter<i - 1>::impl(d.tail);
}
};
// Eureka! We found the element!
template <> struct DimGetter<0> {
// Return a copy if Dim is const
template <typename D>
HOSTDEVICE static int64_t impl(const D &d) {
return d.head;
}
// Return a reference if Dim is mutable
template <typename D> HOSTDEVICE static int64_t &impl(D &d) {
return d.head;
}
};
template <int D> HOSTDEVICE int64_t &indexer(Dim<D> &dim, int idx) {
}
HOSTDEVICE
bool operator==(const Dim<0> &o) const { return true; }
HOSTDEVICE
bool operator!=(const Dim<0> &o) const { return false; }
HOSTDEVICE
int64_t &operator[](int idx);
HOSTDEVICE
int64_t operator[](int idx) const;
};
namespace {
// Helper for accessing Dim classes
template <int i> struct DimGetter {
// Return a copy if Dim is const
template <typename D> HOSTDEVICE static int64_t impl(const D &d) {
return DimGetter<i - 1>::impl(d.tail);
}
// Return a reference if Dim is mutable
template <typename D> HOSTDEVICE static int64_t &impl(D &d) {
return DimGetter<i - 1>::impl(d.tail);
}
};
// Eureka! We found the element!
template <> struct DimGetter<0> {
// Return a copy if Dim is const
template <typename D> HOSTDEVICE static int64_t impl(const D &d) {
return d.head;
}
// Return a reference if Dim is mutable
template <typename D> HOSTDEVICE static int64_t &impl(D &d) {
return d.head;
}
};
template <int D> HOSTDEVICE int64_t &indexer(Dim<D> &dim, int idx) {
#ifndef __CUDA_ARCH__
if (idx < 0) {
throw std::invalid_argument(
"Tried to access a negative dimension");
}
if (idx < 0) {
throw std::invalid_argument("Tried to access a negative dimension");
}
#else
PADDLE_ASSERT(idx >= 0);
PADDLE_ASSERT(idx >= 0);
#endif
if (idx == 0) {
return dim.head;
}
return indexer(dim.tail, idx - 1);
}
if (idx == 0) {
return dim.head;
}
return indexer(dim.tail, idx - 1);
}
template <> HOSTDEVICE int64_t &indexer<0>(Dim<0> &dim, int idx) {
template <> HOSTDEVICE int64_t &indexer<0>(Dim<0> &dim, int idx) {
#ifndef __CUDA_ARCH__
throw std::invalid_argument("Invalid index");
throw std::invalid_argument("Invalid index");
#else
PADDLE_ASSERT(false);
PADDLE_ASSERT(false);
#if CUDA_VERSION < 8000
// On CUDA versions previous to 8.0, only __shared__ variables
// could be declared as static in the device code.
int64_t head = 0;
// On CUDA versions previous to 8.0, only __shared__ variables
// could be declared as static in the device code.
int64_t head = 0;
#else
static int64_t head = 0;
static int64_t head = 0;
#endif
return head;
return head;
#endif
}
}
template <int D>
HOSTDEVICE int64_t indexer(const Dim<D> &dim, int idx) {
template <int D> HOSTDEVICE int64_t indexer(const Dim<D> &dim, int idx) {
#ifndef __CUDA_ARCH__
if (idx < 0) {
throw std::invalid_argument(
"Tried to access a negative dimension");
}
if (idx < 0) {
throw std::invalid_argument("Tried to access a negative dimension");
}
#else
PADDLE_ASSERT(idx >= 0);
PADDLE_ASSERT(idx >= 0);
#endif
if (idx == 0) {
return dim.head;
}
return indexer(dim.tail, idx - 1);
}
template <>
HOSTDEVICE int64_t indexer<0>(const Dim<0> &dim, int idx) {
if (idx == 0) {
return dim.head;
}
return indexer(dim.tail, idx - 1);
}
template <> HOSTDEVICE int64_t indexer<0>(const Dim<0> &dim, int idx) {
#ifndef __CUDA_ARCH__
throw std::invalid_argument("Invalid index");
throw std::invalid_argument("Invalid index");
#else
PADDLE_ASSERT(false);
PADDLE_ASSERT(false);
#if CUDA_VERSION < 8000
// On CUDA versions previous to 8.0, only __shared__ variables
// could be declared as static in the device code.
int64_t head = 0;
// On CUDA versions previous to 8.0, only __shared__ variables
// could be declared as static in the device code.
int64_t head = 0;
#else
static int64_t head = 0;
static int64_t head = 0;
#endif
return head;
return head;
#endif
}
} // namespace
// Static access to constant Dim
template <int i, int l> HOSTDEVICE int64_t get(const Dim<l> &d) {
return DimGetter<i>::impl(d);
}
// Static access to mutable Dim
template <int i, int l> HOSTDEVICE int64_t &get(Dim<l> &d) {
return DimGetter<i>::impl(d);
}
// Dynamic access to constant Dim
template <int l> HOSTDEVICE int64_t Dim<l>::operator[](int i) const {
// std::cout << "l: " << l << std::endl;
return indexer(*this, i);
}
// Dynamic access to mutable Dim
template <int l> HOSTDEVICE int64_t &Dim<l>::operator[](int i) {
return indexer(*this, i);
}
// Dynamic access to constant Dim
inline HOSTDEVICE int64_t Dim<0>::operator[](int i) const {
return indexer(*this, i);
}
// Dynamic access to mutable Dim
inline HOSTDEVICE int64_t &Dim<0>::operator[](int i) {
return indexer(*this, i);
}
// Dynamic access to constant Dim
// without std::enable_if will try to instantiate this on get<0>(d)
template <int l>
HOSTDEVICE typename std::enable_if<(l > 0), int64_t>::type
get(const Dim<l> &d, int i) {
return d[i];
}
// Dynamic access to mutable Dim
template <int l>
HOSTDEVICE typename std::enable_if<(l > 0), int64_t &>::type
get(Dim<l> &d, int i) {
return d[i];
}
// Dot product of two dims
template <int i>
HOSTDEVICE int64_t linearize(const Dim<i> &a, const Dim<i> &b) {
return a.head * b.head + linearize(a.tail, b.tail);
}
// Base case dot product of two Dims
// Notice it is inline because it is no longer a template
template <>
HOSTDEVICE inline int64_t linearize(const Dim<0> &a, const Dim<0> &b) {
return 0;
}
// Product of a Dim
template <int i>
HOSTDEVICE int64_t product(const Dim<i> &a, int prod = 1) {
return prod * a.head * product(a.tail);
}
// Base case product of a Dim
// Notice it is inline because it is no longer a template
template <>
HOSTDEVICE inline int64_t product(const Dim<0> &a, int prod) {
return prod;
}
// Is 0 <= idx_i < size_i for all i?
template <int i>
HOSTDEVICE bool contained(const Dim<i> &idx, const Dim<i> &size) {
return ((0 <= idx.head) && (idx.head < size.head) &&
contained(idx.tail, size.tail));
}
// Base case of is 0 <= idx_i < size_i ?
// Notice it is inline because it is no longer a template
template <>
HOSTDEVICE inline bool contained(const Dim<0> &idx,
const Dim<0> &size) {
return true;
}
/**
* \brief Compute exclusive prefix-multiply of a Dim.
*/
template <int i>
HOSTDEVICE Dim<i> ex_prefix_mul(const Dim<i> &src, int mul = 1) {
return Dim<i>(mul, ex_prefix_mul(src.tail, mul * src.head));
}
///\cond HIDDEN
// Base case of ex_prefix_mul
// Notice it is inline because it is no longer a template
template <>
HOSTDEVICE inline Dim<0> ex_prefix_mul(const Dim<0> &src, int mul) {
return Dim<0>();
}
///\endcond
/**
* Add two dimensions together
*/
template <int i>
HOSTDEVICE Dim<i> dim_plus(const Dim<i> &a, const Dim<i> &b) {
return Dim<i>(a.head + b.head, dim_plus(a.tail, b.tail));
}
// Base case
template <>
HOSTDEVICE inline Dim<0> dim_plus(const Dim<0> &a, const Dim<0> &b) {
return Dim<0>();
}
template <int i>
HOSTDEVICE Dim<i> operator+(const Dim<i> &lhs, const Dim<i> &rhs) {
return dim_plus(lhs, rhs);
}
/**
* Multiply two dimensions together
*/
template <int i>
HOSTDEVICE Dim<i> dim_mult(const Dim<i> &a, const Dim<i> &b) {
return Dim<i>(a.head * b.head, dim_mult(a.tail, b.tail));
}
// Base case
template <>
HOSTDEVICE inline Dim<0> dim_mult(const Dim<0> &a, const Dim<0> &b) {
return Dim<0>();
}
template <int i>
HOSTDEVICE Dim<i> operator*(const Dim<i> &lhs, const Dim<i> &rhs) {
return dim_mult(lhs, rhs);
}
/**
* \brief Normalize strides to ensure any dimension with extent 1
* has stride 0.
*
* \param size Dim object containing the size of an array
* \param stride Dim object containing stride of an array
* \return Dim object the same size as \p size with normalized strides
*
*/
template <int i>
HOSTDEVICE Dim<i> normalize_strides(const Dim<i> &size,
const Dim<i> &stride) {
int norm_stride = size.head == 1 ? 0 : stride.head;
return Dim<i>(norm_stride,
normalize_strides(size.tail, stride.tail));
}
///\cond HIDDEN
template <>
HOSTDEVICE inline Dim<0> normalize_strides(const Dim<0> &size,
const Dim<0> &stride) {
return Dim<0>();
}
///\endcond
/**
* Helper function to create a Dim
*
* \param idxes The type of Dim constructed depends on the number of
* params
*
*/
template <typename... Args>
HOSTDEVICE Dim<sizeof...(Args)> make_dim(Args... idxes) {
return Dim<sizeof...(Args)>(idxes...);
}
// Allows us to output a Dim
// XXX For some reason, overloading fails to resolve this correctly
template <int i>
typename std::enable_if<(i > 1), std::ostream &>::type
operator<<(std::ostream &os, const Dim<i> &d) {
os << d.head << ", " << d.tail;
return os;
}
// Base case that allows us to output a Dim
// XXX I wish this could be an overload instead of a template
template <int i>
typename std::enable_if<(i == 1), std::ostream &>::type
operator<<(std::ostream &os, const Dim<i> &d) {
os << d.head;
return os;
}
inline std::ostream &operator<<(std::ostream &os, const Dim<0> &d) {
return os;
}
template <int i> HOST std::string Dim<i>::to_string() const {
std::stringstream stream;
stream << *this;
return stream.str();
}
template <int D>
HOSTDEVICE Dim<D> linear_to_dimension(int linear_index,
Dim<D> extents) {
Dim<D> result;
for (int i = 0; i < D - 1; ++i) {
result[i] = linear_index % extents[i];
linear_index /= extents[i];
}
result[D - 1] = linear_index;
return result;
}
} // namespace framework
}
} // namespace
// Static access to constant Dim
template <int i, int l> HOSTDEVICE int64_t get(const Dim<l> &d) {
return DimGetter<i>::impl(d);
}
// Static access to mutable Dim
template <int i, int l> HOSTDEVICE int64_t &get(Dim<l> &d) {
return DimGetter<i>::impl(d);
}
// Dynamic access to constant Dim
template <int l> HOSTDEVICE int64_t Dim<l>::operator[](int i) const {
// std::cout << "l: " << l << std::endl;
return indexer(*this, i);
}
// Dynamic access to mutable Dim
template <int l> HOSTDEVICE int64_t &Dim<l>::operator[](int i) {
return indexer(*this, i);
}
// Dynamic access to constant Dim
inline HOSTDEVICE int64_t Dim<0>::operator[](int i) const {
return indexer(*this, i);
}
// Dynamic access to mutable Dim
inline HOSTDEVICE int64_t &Dim<0>::operator[](int i) {
return indexer(*this, i);
}
// Dynamic access to constant Dim
// without std::enable_if will try to instantiate this on get<0>(d)
template <int l>
HOSTDEVICE typename std::enable_if<(l > 0), int64_t>::type get(const Dim<l> &d,
int i) {
return d[i];
}
// Dynamic access to mutable Dim
template <int l>
HOSTDEVICE typename std::enable_if<(l > 0), int64_t &>::type get(Dim<l> &d,
int i) {
return d[i];
}
// Dot product of two dims
template <int i>
HOSTDEVICE int64_t linearize(const Dim<i> &a, const Dim<i> &b) {
return a.head * b.head + linearize(a.tail, b.tail);
}
// Base case dot product of two Dims
// Notice it is inline because it is no longer a template
template <>
HOSTDEVICE inline int64_t linearize(const Dim<0> &a, const Dim<0> &b) {
return 0;
}
// Product of a Dim
template <int i> HOSTDEVICE int64_t product(const Dim<i> &a, int prod = 1) {
return prod * a.head * product(a.tail);
}
// Base case product of a Dim
// Notice it is inline because it is no longer a template
template <> HOSTDEVICE inline int64_t product(const Dim<0> &a, int prod) {
return prod;
}
// Is 0 <= idx_i < size_i for all i?
template <int i>
HOSTDEVICE bool contained(const Dim<i> &idx, const Dim<i> &size) {
return ((0 <= idx.head) && (idx.head < size.head) &&
contained(idx.tail, size.tail));
}
// Base case of is 0 <= idx_i < size_i ?
// Notice it is inline because it is no longer a template
template <>
HOSTDEVICE inline bool contained(const Dim<0> &idx, const Dim<0> &size) {
return true;
}
/**
* \brief Compute exclusive prefix-multiply of a Dim.
*/
template <int i>
HOSTDEVICE Dim<i> ex_prefix_mul(const Dim<i> &src, int mul = 1) {
return Dim<i>(mul, ex_prefix_mul(src.tail, mul * src.head));
}
///\cond HIDDEN
// Base case of ex_prefix_mul
// Notice it is inline because it is no longer a template
template <> HOSTDEVICE inline Dim<0> ex_prefix_mul(const Dim<0> &src, int mul) {
return Dim<0>();
}
///\endcond
/**
* Add two dimensions together
*/
template <int i> HOSTDEVICE Dim<i> dim_plus(const Dim<i> &a, const Dim<i> &b) {
return Dim<i>(a.head + b.head, dim_plus(a.tail, b.tail));
}
// Base case
template <>
HOSTDEVICE inline Dim<0> dim_plus(const Dim<0> &a, const Dim<0> &b) {
return Dim<0>();
}
template <int i>
HOSTDEVICE Dim<i> operator+(const Dim<i> &lhs, const Dim<i> &rhs) {
return dim_plus(lhs, rhs);
}
/**
* Multiply two dimensions together
*/
template <int i> HOSTDEVICE Dim<i> dim_mult(const Dim<i> &a, const Dim<i> &b) {
return Dim<i>(a.head * b.head, dim_mult(a.tail, b.tail));
}
// Base case
template <>
HOSTDEVICE inline Dim<0> dim_mult(const Dim<0> &a, const Dim<0> &b) {
return Dim<0>();
}
template <int i>
HOSTDEVICE Dim<i> operator*(const Dim<i> &lhs, const Dim<i> &rhs) {
return dim_mult(lhs, rhs);
}
/**
* \brief Normalize strides to ensure any dimension with extent 1
* has stride 0.
*
* \param size Dim object containing the size of an array
* \param stride Dim object containing stride of an array
* \return Dim object the same size as \p size with normalized strides
*
*/
template <int i>
HOSTDEVICE Dim<i> normalize_strides(const Dim<i> &size, const Dim<i> &stride) {
int norm_stride = size.head == 1 ? 0 : stride.head;
return Dim<i>(norm_stride, normalize_strides(size.tail, stride.tail));
}
///\cond HIDDEN
template <>
HOSTDEVICE inline Dim<0> normalize_strides(const Dim<0> &size,
const Dim<0> &stride) {
return Dim<0>();
}
///\endcond
/**
* Helper function to create a Dim
*
* \param idxes The type of Dim constructed depends on the number of
* params
*
*/
template <typename... Args>
HOSTDEVICE Dim<sizeof...(Args)> make_dim(Args... idxes) {
return Dim<sizeof...(Args)>(idxes...);
}
// Allows us to output a Dim
// XXX For some reason, overloading fails to resolve this correctly
template <int i>
typename std::enable_if<(i > 1), std::ostream &>::type
operator<<(std::ostream &os, const Dim<i> &d) {
os << d.head << ", " << d.tail;
return os;
}
// Base case that allows us to output a Dim
// XXX I wish this could be an overload instead of a template
template <int i>
typename std::enable_if<(i == 1), std::ostream &>::type
operator<<(std::ostream &os, const Dim<i> &d) {
os << d.head;
return os;
}
inline std::ostream &operator<<(std::ostream &os, const Dim<0> &d) {
return os;
}
template <int i> HOST std::string Dim<i>::to_string() const {
std::stringstream stream;
stream << *this;
return stream.str();
}
template <int D>
HOSTDEVICE Dim<D> linear_to_dimension(int linear_index, Dim<D> extents) {
Dim<D> result;
for (int i = 0; i < D - 1; ++i) {
result[i] = linear_index % extents[i];
linear_index /= extents[i];
}
result[D - 1] = linear_index;
return result;
}
} // namespace framework
} // namespace paddle_mobile
src/framework/executor.cpp
浏览文件 @ 47e5978f
......@@ -23,75 +23,72 @@ 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) {
}
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);
}
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);
}
}
}
}
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 @ 47e5978f
......@@ -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 @ 47e5978f
因为 它太大了无法显示 source diff 。你可以改为 查看blob。
src/framework/framework.pb.h
浏览文件 @ 47e5978f
因为 它太大了无法显示 source diff 。你可以改为 查看blob。
src/framework/lod_tensor.cc
浏览文件 @ 47e5978f
......@@ -19,304 +19,295 @@ limitations under the License. */
#include <string.h>
namespace paddle_mobile {
namespace framework {
std::ostream &operator<<(std::ostream &os, const LoD &lod) {
os << "{";
for (auto &v : lod) {
os << "{";
bool is_first = true;
for (auto &i : v) {
if (is_first) {
os << i;
is_first = false;
} else {
os << ", " << i;
}
}
os << "}";
namespace framework {
std::ostream &operator<<(std::ostream &os, const LoD &lod) {
os << "{";
for (auto &v : lod) {
os << "{";
bool is_first = true;
for (auto &i : v) {
if (is_first) {
os << i;
is_first = false;
} else {
os << ", " << i;
}
os << "}";
return os;
}
std::ostream &operator<<(std::ostream &os, const LoDTensor &t) {
// PADDLE_ENFORCE(t.type().hash_code() ==
// typeid(float).hash_code());
// if (!platform::is_cpu_place(t.place())) {
// LoDTensor tt;
// framework::TensorCopy(t, platform::CPUPlace(), &tt);
// platform::DeviceContextPool &pool =
// platform::DeviceContextPool::Instance(); auto &dev_ctx =
// *pool.Get(t.place()); dev_ctx.Wait();
//
// os << tt;
// return os;
// }
os << "dim: " << t.dims() << "\n";
os << "lod: " << t.lod() << "\n";
// only print first ten elements
int64_t size = t.numel() < 10 ? t.numel() : 10;
for (int64_t i = 0; i < size; ++i) {
os << t.data<float>()[i] << " ";
}
return os;
os << "}";
}
os << "}";
return os;
}
std::ostream &operator<<(std::ostream &os, const LoDTensor &t) {
// PADDLE_ENFORCE(t.type().hash_code() ==
// typeid(float).hash_code());
// if (!platform::is_cpu_place(t.place())) {
// LoDTensor tt;
// framework::TensorCopy(t, platform::CPUPlace(), &tt);
// platform::DeviceContextPool &pool =
// platform::DeviceContextPool::Instance(); auto &dev_ctx =
// *pool.Get(t.place()); dev_ctx.Wait();
//
// os << tt;
// return os;
// }
os << "dim: " << t.dims() << "\n";
os << "lod: " << t.lod() << "\n";
// only print first ten elements
int64_t size = t.numel() < 10 ? t.numel() : 10;
for (int64_t i = 0; i < size; ++i) {
os << t.data<float>()[i] << " ";
}
return os;
}
std::string LoDToString(const LoD &lod) {
std::ostringstream stream;
stream << lod;
return stream.str();
}
LoD SliceInLevel(const LoD &in, size_t level, size_t elem_begin,
size_t elem_end) {
// PADDLE_ENFORCE_LT(level, in.size());
// PADDLE_ENFORCE_LT(elem_end, in[level].size());
LoD res;
res.resize(in.size() - level);
// copy the first level
res[0].assign(in[level].begin() + elem_begin,
in[level].begin() + elem_end + 1);
for (size_t lvl = 1; lvl < res.size(); lvl++) {
const auto &in_level = in[level + lvl];
const auto &above_level = res[lvl - 1];
auto &out_level = res[lvl];
out_level.assign(in_level.begin() + above_level.front(),
in_level.begin() + above_level.back() + 1);
}
for (size_t lvl = 0; lvl < res.size(); lvl++) {
// to make the first offset equals 0, all the elements minus the
// first
// element
size_t front = res[lvl].front();
for (auto &ele : res[lvl]) {
ele -= front;
}
std::string LoDToString(const LoD &lod) {
std::ostringstream stream;
stream << lod;
return stream.str();
}
return res;
}
LoD ToAbsOffset(const LoD &in) {
// the lowest level stores relative offsets
if (in.empty() || in.size() == 1)
return in;
LoD result = in;
for (auto level = static_cast<int>(in.size() - 2); level >= 0; level--) {
for (size_t i = 0; i < in[level].size(); ++i) {
size_t index = in[level][i];
result[level][i] = result[level + 1][index];
}
LoD SliceInLevel(const LoD &in, size_t level, size_t elem_begin,
size_t elem_end) {
// PADDLE_ENFORCE_LT(level, in.size());
// PADDLE_ENFORCE_LT(elem_end, in[level].size());
LoD res;
res.resize(in.size() - level);
// copy the first level
res[0].assign(in[level].begin() + elem_begin,
in[level].begin() + elem_end + 1);
for (size_t lvl = 1; lvl < res.size(); lvl++) {
const auto &in_level = in[level + lvl];
const auto &above_level = res[lvl - 1];
auto &out_level = res[lvl];
out_level.assign(in_level.begin() + above_level.front(),
in_level.begin() + above_level.back() + 1);
}
for (size_t lvl = 0; lvl < res.size(); lvl++) {
// to make the first offset equals 0, all the elements minus the
// first
// element
size_t front = res[lvl].front();
for (auto &ele : res[lvl]) {
ele -= front;
}
}
return res;
}
return result;
}
bool operator==(const LoD &a, const LoD &b) {
if (a.size() != b.size()) {
return false;
}
for (size_t i = 0; i < a.size(); i++) {
const auto &a_level = a[i];
const auto &b_level = b[i];
if (a_level.size() != b_level.size()) {
return false;
}
LoD ToAbsOffset(const LoD &in) {
// the lowest level stores relative offsets
if (in.empty() || in.size() == 1)
return in;
LoD result = in;
for (auto level = static_cast<int>(in.size() - 2); level >= 0;
level--) {
for (size_t i = 0; i < in[level].size(); ++i) {
size_t index = in[level][i];
result[level][i] = result[level + 1][index];
}
}
return result;
}
bool operator==(const LoD &a, const LoD &b) {
if (a.size() != b.size()) {
for (size_t j = 0; j < a_level.size(); j++) {
if (a_level[j] != b_level[j]) {
return false;
}
for (size_t i = 0; i < a.size(); i++) {
const auto &a_level = a[i];
const auto &b_level = b[i];
if (a_level.size() != b_level.size()) {
return false;
}
for (size_t j = 0; j < a_level.size(); j++) {
if (a_level[j] != b_level[j]) {
return false;
}
}
}
return true;
}
bool CheckLoD(const LoD &in, int tensor_height) {
if (in.empty())
return true;
for (const auto &level : in) {
// check: there should be more than 2 offsets existing in each
// level.
if (level.size() < 2)
return false;
// check: the first offset(the begin offset) of each level
// should be 0.
if (level.front() != 0)
return false;
// check: all the offsets in a level should be ascending(no same
// items
// allows).
if (!std::is_sorted(level.begin(), level.begin(),
[](size_t a, size_t b) {
if (a < b)
return true;
return false;
})) {
std::cout << "ascending error";
return false;
}
}
// check: the lowest level's last offset should equals
// `tensor_height` if
// tensor_height>0.
if (tensor_height > 0 && (size_t)tensor_height != in.back().back())
return false;
// check: the higher level's last offset should equals the lower
// level's
// size-1.
// NOTE LoD store the levels from top to bottom, so the higher level
// goes
// first.
for (size_t level = 0; level < in.size() - 1; level++) {
if (in[level].back() != in[level + 1].size() - 1)
return false;
}
return true;
}
return true;
}
bool CheckLoD(const LoD &in, int tensor_height) {
if (in.empty())
return true;
for (const auto &level : in) {
// check: there should be more than 2 offsets existing in each
// level.
if (level.size() < 2)
return false;
// check: the first offset(the begin offset) of each level
// should be 0.
if (level.front() != 0)
return false;
// check: all the offsets in a level should be ascending(no same
// items
// allows).
if (!std::is_sorted(level.begin(), level.begin(),
[](size_t a, size_t b) {
if (a < b)
return true;
return false;
})) {
std::cout << "ascending error";
return false;
}
bool CheckAbsLoD(const LoD &in, int tensor_height) {
if (in.empty())
return true;
for (const auto &level : in) {
// check: all the offsets in a level should be ascending(no same
// items
// allows).
if (!std::is_sorted(level.begin(), level.begin(),
[](size_t a, size_t b) {
if (a < b)
return true;
return false;
})) {
return false;
}
// check: there should be more than 2 offsets existing in each
// level.
if (level.size() < 2)
return false;
// check: the first offset of each level should be 0, and the
// last should be
// the same(the height of underlying tensor).
if (level.front() != 0)
return false;
if (tensor_height < 0) {
tensor_height = level.back();
} else if ((size_t)tensor_height != level.back()) {
return false;
}
}
return true;
}
// check: the lowest level's last offset should equals
// `tensor_height` if
// tensor_height>0.
if (tensor_height > 0 && (size_t)tensor_height != in.back().back())
return false;
// check: the higher level's last offset should equals the lower
// level's
// size-1.
// NOTE LoD store the levels from top to bottom, so the higher level
// goes
// first.
for (size_t level = 0; level < in.size() - 1; level++) {
if (in[level].back() != in[level + 1].size() - 1)
return false;
}
return true;
}
bool CheckAbsLoD(const LoD &in, int tensor_height) {
if (in.empty())
return true;
for (const auto &level : in) {
// check: all the offsets in a level should be ascending(no same
// items
// allows).
if (!std::is_sorted(level.begin(), level.begin(),
[](size_t a, size_t b) {
if (a < b)
return true;
return false;
})) {
return false;
}
using LoDAndOffset = std::pair<LoD, std::pair<size_t, size_t>>;
LoDAndOffset GetSubLoDAndAbsoluteOffset(const LoD &lod,
size_t start_idx,
size_t end_idx,
size_t start_level) {
LoD sub_lod;
for (size_t level_idx = start_level; level_idx < lod.size();
++level_idx) {
// PADDLE_ENFORCE_LE(start_idx, end_idx);
// PADDLE_ENFORCE_LT(end_idx, lod[level_idx].size());
std::vector<size_t> level_lens;
for (size_t i = start_idx; i < end_idx; ++i) {
level_lens.push_back(lod[level_idx][i + 1] -
lod[level_idx][i]);
}
sub_lod.emplace_back(level_lens);
start_idx = lod[level_idx][start_idx];
end_idx = lod[level_idx][end_idx];
}
return LoDAndOffset{sub_lod, {start_idx, end_idx}};
// check: there should be more than 2 offsets existing in each
// level.
if (level.size() < 2)
return false;
// check: the first offset of each level should be 0, and the
// last should be
// the same(the height of underlying tensor).
if (level.front() != 0)
return false;
if (tensor_height < 0) {
tensor_height = level.back();
} else if ((size_t)tensor_height != level.back()) {
return false;
}
void AppendLoD(LoD *lod, const LoD &lod_length) {
// PADDLE_ENFORCE(
// lod->empty() || lod->size() == lod_length.size(),
// "The lod_length should has the same size with the appended
// lod.");
if (lod->empty()) {
for (size_t i = 0; i < lod_length.size(); ++i) {
lod->emplace_back(1, 0); // size = 1, value = 0;
}
*lod = LoD(lod_length.size(), std::vector<size_t>({0}));
}
for (size_t i = 0; i < lod->size(); ++i) {
auto &level = (*lod)[i];
for (size_t len : lod_length[i]) {
level.push_back(level.back() + len);
}
}
}
return true;
}
using LoDAndOffset = std::pair<LoD, std::pair<size_t, size_t>>;
LoDAndOffset GetSubLoDAndAbsoluteOffset(const LoD &lod, size_t start_idx,
size_t end_idx, size_t start_level) {
LoD sub_lod;
for (size_t level_idx = start_level; level_idx < lod.size(); ++level_idx) {
// PADDLE_ENFORCE_LE(start_idx, end_idx);
// PADDLE_ENFORCE_LT(end_idx, lod[level_idx].size());
std::vector<size_t> level_lens;
for (size_t i = start_idx; i < end_idx; ++i) {
level_lens.push_back(lod[level_idx][i + 1] - lod[level_idx][i]);
}
void SerializeToStream(std::ostream &os, const LoDTensor &tensor) {
{ // the 1st field, uint32_t version for LoDTensor
constexpr uint32_t version = 0;
os.write(reinterpret_cast<const char *>(&version),
sizeof(version));
}
{
// the 2st field, LoD information
// uint64_t lod_level
// uint64_t lod_level_1 size in byte.
// int* lod_level_1 data
// ...
auto lod = tensor.lod();
uint64_t size = lod.size();
os.write(reinterpret_cast<const char *>(&size), sizeof(size));
for (auto &each : lod) {
size = each.size() *
sizeof(framework::LoD::value_type::value_type);
os.write(reinterpret_cast<const char *>(&size),
sizeof(size));
os.write(reinterpret_cast<const char *>(each.data()),
static_cast<std::streamsize>(size));
}
}
// the 3st field, Tensor
TensorToStream(os, static_cast<Tensor>(tensor));
sub_lod.emplace_back(level_lens);
start_idx = lod[level_idx][start_idx];
end_idx = lod[level_idx][end_idx];
}
return LoDAndOffset{sub_lod, {start_idx, end_idx}};
}
void AppendLoD(LoD *lod, const LoD &lod_length) {
// PADDLE_ENFORCE(
// lod->empty() || lod->size() == lod_length.size(),
// "The lod_length should has the same size with the appended
// lod.");
if (lod->empty()) {
for (size_t i = 0; i < lod_length.size(); ++i) {
lod->emplace_back(1, 0); // size = 1, value = 0;
}
void DeserializeFromStream(std::istream &is, LoDTensor *tensor) {
{
// the 1st field, unit32_t version for LoDTensor
uint32_t version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
// PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is
// supported");
}
{
// the 2st field, LoD information
uint64_t lod_level;
is.read(reinterpret_cast<char *>(&lod_level),
sizeof(lod_level));
auto &lod = *tensor->mutable_lod();
lod.resize(lod_level);
for (uint64_t i = 0; i < lod_level; ++i) {
uint64_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
std::vector<size_t> tmp(size / sizeof(size_t));
is.read(reinterpret_cast<char *>(tmp.data()),
static_cast<std::streamsize>(size));
lod[i] = tmp;
}
}
// the 3st filed, Tensor
TensorFromStream(is, static_cast<Tensor *>(tensor));
*lod = LoD(lod_length.size(), std::vector<size_t>({0}));
}
for (size_t i = 0; i < lod->size(); ++i) {
auto &level = (*lod)[i];
for (size_t len : lod_length[i]) {
level.push_back(level.back() + len);
}
}
}
void SerializeToStream(std::ostream &os, const LoDTensor &tensor) {
{ // the 1st field, uint32_t version for LoDTensor
constexpr uint32_t version = 0;
os.write(reinterpret_cast<const char *>(&version), sizeof(version));
}
{
// the 2st field, LoD information
// uint64_t lod_level
// uint64_t lod_level_1 size in byte.
// int* lod_level_1 data
// ...
auto lod = tensor.lod();
uint64_t size = lod.size();
os.write(reinterpret_cast<const char *>(&size), sizeof(size));
for (auto &each : lod) {
size = each.size() * sizeof(framework::LoD::value_type::value_type);
os.write(reinterpret_cast<const char *>(&size), sizeof(size));
os.write(reinterpret_cast<const char *>(each.data()),
static_cast<std::streamsize>(size));
}
}
// the 3st field, Tensor
TensorToStream(os, static_cast<Tensor>(tensor));
}
void DeserializeFromStream(std::istream &is, LoDTensor *tensor) {
{
// the 1st field, unit32_t version for LoDTensor
uint32_t version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
// PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is
// supported");
}
{
// the 2st field, LoD information
uint64_t lod_level;
is.read(reinterpret_cast<char *>(&lod_level), sizeof(lod_level));
auto &lod = *tensor->mutable_lod();
lod.resize(lod_level);
for (uint64_t i = 0; i < lod_level; ++i) {
uint64_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
std::vector<size_t> tmp(size / sizeof(size_t));
is.read(reinterpret_cast<char *>(tmp.data()),
static_cast<std::streamsize>(size));
lod[i] = tmp;
}
}
// the 3st filed, Tensor
TensorFromStream(is, static_cast<Tensor *>(tensor));
}
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/lod_tensor.h
浏览文件 @ 47e5978f
......@@ -23,190 +23,186 @@ 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;
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);
}
// 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
}
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 @ 47e5978f
......@@ -5,58 +5,55 @@
#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;
// }
}
}
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));
}
const std::vector<std::string> &
OpDesc::Input(const std::string &name) const {
return inputs_.find(name)->second;
}
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));
}
const std::vector<std::string> &
OpDesc::Output(const std::string &name) const {
return outputs_.find(name)->second;
}
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;
// }
}
}
}
Attribute OpDesc::GetAttr(const std::string &name) const {
auto it = attrs_.find(name);
return it->second;
}
const std::vector<std::string> &OpDesc::Input(const std::string &name) const {
return inputs_.find(name)->second;
}
const std::unordered_map<std::string, Attribute> &
OpDesc::GetAttrMap() const {
return attrs_;
}
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
} // namespace paddle_mobile
src/framework/op_desc.h
浏览文件 @ 47e5978f
......@@ -23,31 +23,29 @@ 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 @ 47e5978f
......@@ -22,74 +22,73 @@ 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 @ 47e5978f
......@@ -22,51 +22,44 @@ 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 @ 47e5978f
......@@ -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 @ 47e5978f
......@@ -20,23 +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>
void OperatorBase<Dtype>::CheckAllInputOutputSet() const {}
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 class OperatorBase<CPU>;
template class OperatorWithKernel<CPU>;
template class OperatorBase<CPU>;
template class OperatorWithKernel<CPU>;
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/operator.h
浏览文件 @ 47e5978f
......@@ -33,68 +33,64 @@ SOFTWARE.
#include "variable.h"
namespace paddle_mobile {
namespace framework {
static std::unordered_map<std::string, std::vector<std::string>>
op_input_output_key = {
{"conv2d", {"Input", "Output"}}, {"relu", {"X", "Out"}},
{"softmax", {"X", "Out"}}, {"mul", {"X", "Out"}},
{"elementwise_add", {"X", "Out"}}, {"pool2d", {"X", "Out"}},
{"batch_norm", {"X", "Y"}}, {"lrn", {"X", "Out"}},
{"concat", {"X", "Out"}},
namespace framework {
static std::unordered_map<std::string, std::vector<std::string>>
op_input_output_key = {
{"conv2d", {"Input", "Output"}}, {"relu", {"X", "Out"}},
{"softmax", {"X", "Out"}}, {"mul", {"X", "Out"}},
{"elementwise_add", {"X", "Out"}}, {"pool2d", {"X", "Out"}},
{"batch_norm", {"X", "Y"}}, {"lrn", {"X", "Out"}},
{"concat", {"X", "Out"}},
};
};
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 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;
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"));
}
}
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"));
}
}
protected:
std::shared_ptr<Scope> scope_;
std::string type_;
VariableNameMap inputs_;
VariableNameMap outputs_;
AttributeMap attrs_;
protected:
std::shared_ptr<Scope> scope_;
std::string type_;
VariableNameMap inputs_;
VariableNameMap outputs_;
AttributeMap attrs_;
private:
void CheckAllInputOutputSet() const;
};
private:
void CheckAllInputOutputSet() const;
};
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>
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 @ 47e5978f
......@@ -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 @ 47e5978f
......@@ -17,5 +17,5 @@ SOFTWARE.
==============================================================================*/
namespace paddle_mobile {
namespace framework {}
namespace framework {}
} // namespace paddle_mobile
src/framework/program.h
浏览文件 @ 47e5978f
......@@ -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 @ 47e5978f
......@@ -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 @ 47e5978f
......@@ -25,20 +25,18 @@ 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 @ 47e5978f
......@@ -4,116 +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
浏览文件 @ 47e5978f
......@@ -24,58 +24,58 @@ SOFTWARE.
#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
浏览文件 @ 47e5978f
......@@ -24,59 +24,58 @@ SOFTWARE.
#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
浏览文件 @ 47e5978f
......@@ -25,316 +25,310 @@ limitations under the License. */
#include "memory/t_malloc.h"
namespace paddle_mobile {
namespace framework {
template <typename... T> struct SizeOfTypeFunctor;
template <typename T> struct SizeOfTypeFunctor<T> {
size_t operator()(std::type_index type) const {
if (typeid(T).hash_code() == type.hash_code()) {
return sizeof(T);
} else {
return 0UL;
}
}
};
template <> struct SizeOfTypeFunctor<> {
size_t operator()(std::type_index type) const { return 0UL; }
};
template <typename HEAD, typename... TAIL>
struct SizeOfTypeFunctor<HEAD, TAIL...> {
size_t operator()(std::type_index type) const {
SizeOfTypeFunctor<HEAD> head;
size_t head_size = head(type);
if (head_size != 0) {
return head_size;
}
SizeOfTypeFunctor<TAIL...> tail;
return tail(type);
}
};
static inline size_t SizeOfType(std::type_index type) {
SizeOfTypeFunctor<int, float, double, int16_t, int64_t, bool,
size_t>
functor;
size_t size = functor(type);
// PADDLE_ENFORCE(size != 0UL, "Cannot get size of type %s",
// type.name());
return size;
namespace framework {
template <typename... T> struct SizeOfTypeFunctor;
template <typename T> struct SizeOfTypeFunctor<T> {
size_t operator()(std::type_index type) const {
if (typeid(T).hash_code() == type.hash_code()) {
return sizeof(T);
} else {
return 0UL;
}
class LoDTensor;
class Tensor {
public:
Tensor() : offset_(0) {}
/*! Return a pointer to mutable memory block. */
template <typename T> inline T *data() {
check_memory_size();
// PADDLE_ENFORCE(std::is_same<T, void>::value ||
// holder_->type().hash_code() ==
// typeid(T).hash_code(),
// "Tensor holds the wrong type, it holds %s",
// this->holder_->type().name());
return reinterpret_cast<T *>(
reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
}
/*! Return a pointer to constant memory block. */
template <typename T> inline const T *data() const {
check_memory_size();
// PADDLE_ENFORCE(std::is_same<T, void>::value ||
// holder_->type().hash_code() ==
// typeid(T).hash_code(),
// "Tensor holds the wrong type, it holds %s",
// this->holder_->type().name());
return reinterpret_cast<const T *>(
reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
}
inline bool IsInitialized() const { return holder_ != nullptr; }
/**
* @brief Return a pointer to mutable memory block.
* @note If not exist, then allocation.
*/
template <typename T> inline T *mutable_data() {
static_assert(std::is_pod<T>::value, "T must be POD");
return reinterpret_cast<T *>(mutable_data(typeid(T)));
}
inline void *mutable_data(std::type_index type) {
if (holder_ != nullptr) {
holder_->set_type(type);
}
// PADDLE_ENFORCE_GE(numel(), 0,
// "When calling this method, the Tensor's
// numel must be
// " "equal or larger than zero. " "Please
// check
// Tensor::Resize has been called first.");
int64_t size = numel() * SizeOfType(type);
/* some versions of boost::variant don't have operator!= */
if (holder_ == nullptr || holder_->size() < size + offset_) {
holder_.reset(new PlaceholderImpl(size, type));
offset_ = 0;
}
return reinterpret_cast<void *>(
reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
}
inline void *mutable_data() {
// PADDLE_ENFORCE(this->holder_ != nullptr,
// "Cannot invoke mutable data if current hold
// nothing.");
return mutable_data(holder_->type());
}
/**
* @brief Return a pointer to mutable memory block.
*
* @param[in] dims The dimensions of the memory block.
* @param[in] place The place of the memory block.
*
* @note If not exist, then allocation.
*/
template <typename T> inline T *mutable_data(DDim dims) {
static_assert(std::is_pod<T>::value, "T must be POD");
Resize(dims);
return mutable_data<T>();
}
/*! Return the dimensions of the memory block. */
inline const DDim &dims() const { return dims_; }
/*! Return the numel of the memory block. */
inline int64_t numel() const { return product(dims_); }
/*! Resize the dimensions of the memory block. */
inline Tensor &Resize(const DDim &dims) {
dims_ = dims;
return *this;
}
/*! The internal of two tensors share the same memory block. */
inline Tensor &ShareDataWith(const Tensor &src) {
src.check_memory_size();
*this = src;
return *this;
}
/**
* @brief Return a sub-tensor of the given tensor.
*
* @param[in] begin_idx The index of the start row(inclusive) to
* slice.
* The index number begins from 0.
* @param[in] end_idx The index of the end row(exclusive) to
* slice.
* The index number begins from 0.
*/
inline Tensor Slice(int begin_idx, int end_idx) const {
check_memory_size();
// PADDLE_ENFORCE_GE(begin_idx, 0,
// "The start row index must be greater than
// 0.");
// PADDLE_ENFORCE_LE(end_idx, dims_[0], "The end row index is
// out of
// bound."); PADDLE_ENFORCE_LT(
// begin_idx, end_idx,
// "The start row index must be lesser than the end row
// index.");
if (dims_[0] == 1) {
return *this;
} else {
size_t base = numel() / dims_[0];
Tensor dst;
dst.holder_ = holder_;
dst.set_layout(layout_);
DDim dst_dims = dims_;
dst_dims[0] = end_idx - begin_idx;
dst.Resize(dst_dims);
dst.offset_ =
offset_ + begin_idx * base * SizeOfType(type());
return dst;
}
}
std::type_index type() const {
// PADDLE_ENFORCE_NOT_NULL(
// holder_, "Tensor not initialized yet
// when
// Tensor::type() is called.");
return holder_->type();
}
// memory size returns the holding memory size in byte.
size_t memory_size() const {
return holder_ == nullptr ? 0UL : holder_->size() - offset_;
}
inline void check_memory_size() const {
// PADDLE_ENFORCE_NOT_NULL(
// holder_, "Tensor holds no memory. Call
// Tensor::mutable_data
// first.");
// PADDLE_ENFORCE_LE(
// numel() * SizeOfType(type()), memory_size(),
// "Tensor's dims_ is out of bound. Call
// Tensor::mutable_data "
// "first to re-allocate memory.\n"
// "or maybe the required data-type mismatches the data
// already
// stored.");
}
inline DataLayout layout() const { return layout_; }
inline void set_layout(const DataLayout layout) {
layout_ = layout;
}
private:
/**
* @note Placeholder hides type T, so it doesn't appear as a
* template
* parameter of Variable.
*/
struct Placeholder {
virtual ~Placeholder() = default;
virtual void *ptr() const = 0;
virtual size_t size() const = 0;
virtual std::type_index type() const = 0;
virtual void set_type(std::type_index type) = 0;
};
struct PlaceholderImpl : public Placeholder {
PlaceholderImpl(size_t size, std::type_index type)
: ptr_(static_cast<uint8_t *>(memory::Alloc(size)),
memory::PODDeleter<uint8_t>()),
size_(size), type_(type) {
// PADDLE_ENFORCE_NOT_NULL(ptr_,
// "Insufficient %s
// memory to allocation.",
// (is_cpu_place(place_)
// ?
// "CPU" :
// "GPU"));
}
virtual size_t size() const { return size_; }
virtual void *ptr() const {
return static_cast<void *>(ptr_.get());
}
virtual std::type_index type() const { return type_; }
virtual void set_type(std::type_index type) { type_ = type; }
/*! the pointer of memory block. */
std::unique_ptr<uint8_t, memory::PODDeleter<uint8_t>> ptr_;
/*! the size of memory block. */
size_t size_;
/* the current type of memory */
std::type_index type_;
};
/*! holds the memory block if allocated. */
std::shared_ptr<Placeholder> holder_;
/**
* @brief points to elements dimensions.
*
* @note dims_ do not indicate the memory block size.
*/
DDim dims_;
/**
* @brief the layout of memory block, default is NHWC.
*
* @note the memory allocation order, describe how weight/data is
* stored
* For example, in 4-D Tensor(rank=4), there are three
* commonly
* used layout. They are
* NCHW, NHWC, CHWN.
* N,C,H,W for respectively the batch size, the number of
* feature maps, the height, the width.
*/
DataLayout layout_ = DataLayout::kNHWC;
/**
* @brief A PlaceHolder may be shared by more than one tensor.
*
* @note Some of them may be slices of the others. So the offset_
* is introduced here to indicate the byte offset between
* PlaceHolder::ptr_ and where the tensor data really
* begins.
*/
size_t offset_;
};
inline Tensor ReshapeToMatrix(const Tensor &src, int num_col_dims) {
Tensor res;
res.ShareDataWith(src);
res.Resize(flatten_to_2d(src.dims(), num_col_dims));
return res;
}
};
template <> struct SizeOfTypeFunctor<> {
size_t operator()(std::type_index type) const { return 0UL; }
};
template <typename HEAD, typename... TAIL>
struct SizeOfTypeFunctor<HEAD, TAIL...> {
size_t operator()(std::type_index type) const {
SizeOfTypeFunctor<HEAD> head;
size_t head_size = head(type);
if (head_size != 0) {
return head_size;
}
SizeOfTypeFunctor<TAIL...> tail;
return tail(type);
}
};
static inline size_t SizeOfType(std::type_index type) {
SizeOfTypeFunctor<int, float, double, int16_t, int64_t, bool, size_t>
functor;
size_t size = functor(type);
// PADDLE_ENFORCE(size != 0UL, "Cannot get size of type %s",
// type.name());
return size;
}
class LoDTensor;
class Tensor {
public:
Tensor() : offset_(0) {}
/*! Return a pointer to mutable memory block. */
template <typename T> inline T *data() {
check_memory_size();
// PADDLE_ENFORCE(std::is_same<T, void>::value ||
// holder_->type().hash_code() ==
// typeid(T).hash_code(),
// "Tensor holds the wrong type, it holds %s",
// this->holder_->type().name());
return reinterpret_cast<T *>(
reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
}
/*! Return a pointer to constant memory block. */
template <typename T> inline const T *data() const {
check_memory_size();
// PADDLE_ENFORCE(std::is_same<T, void>::value ||
// holder_->type().hash_code() ==
// typeid(T).hash_code(),
// "Tensor holds the wrong type, it holds %s",
// this->holder_->type().name());
return reinterpret_cast<const T *>(
reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
}
inline bool IsInitialized() const { return holder_ != nullptr; }
/**
* @brief Return a pointer to mutable memory block.
* @note If not exist, then allocation.
*/
template <typename T> inline T *mutable_data() {
static_assert(std::is_pod<T>::value, "T must be POD");
return reinterpret_cast<T *>(mutable_data(typeid(T)));
}
inline void *mutable_data(std::type_index type) {
if (holder_ != nullptr) {
holder_->set_type(type);
}
// PADDLE_ENFORCE_GE(numel(), 0,
// "When calling this method, the Tensor's
// numel must be
// " "equal or larger than zero. " "Please
// check
// Tensor::Resize has been called first.");
int64_t size = numel() * SizeOfType(type);
/* some versions of boost::variant don't have operator!= */
if (holder_ == nullptr || holder_->size() < size + offset_) {
holder_.reset(new PlaceholderImpl(size, type));
offset_ = 0;
}
return reinterpret_cast<void *>(
reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
}
inline void *mutable_data() {
// PADDLE_ENFORCE(this->holder_ != nullptr,
// "Cannot invoke mutable data if current hold
// nothing.");
return mutable_data(holder_->type());
}
/**
* @brief Return a pointer to mutable memory block.
*
* @param[in] dims The dimensions of the memory block.
* @param[in] place The place of the memory block.
*
* @note If not exist, then allocation.
*/
template <typename T> inline T *mutable_data(DDim dims) {
static_assert(std::is_pod<T>::value, "T must be POD");
Resize(dims);
return mutable_data<T>();
}
/*! Return the dimensions of the memory block. */
inline const DDim &dims() const { return dims_; }
/*! Return the numel of the memory block. */
inline int64_t numel() const { return product(dims_); }
/*! Resize the dimensions of the memory block. */
inline Tensor &Resize(const DDim &dims) {
dims_ = dims;
return *this;
}
/*! The internal of two tensors share the same memory block. */
inline Tensor &ShareDataWith(const Tensor &src) {
src.check_memory_size();
*this = src;
return *this;
}
/**
* @brief Return a sub-tensor of the given tensor.
*
* @param[in] begin_idx The index of the start row(inclusive) to
* slice.
* The index number begins from 0.
* @param[in] end_idx The index of the end row(exclusive) to
* slice.
* The index number begins from 0.
*/
inline Tensor Slice(int begin_idx, int end_idx) const {
check_memory_size();
// PADDLE_ENFORCE_GE(begin_idx, 0,
// "The start row index must be greater than
// 0.");
// PADDLE_ENFORCE_LE(end_idx, dims_[0], "The end row index is
// out of
// bound."); PADDLE_ENFORCE_LT(
// begin_idx, end_idx,
// "The start row index must be lesser than the end row
// index.");
if (dims_[0] == 1) {
return *this;
} else {
size_t base = numel() / dims_[0];
Tensor dst;
dst.holder_ = holder_;
dst.set_layout(layout_);
DDim dst_dims = dims_;
dst_dims[0] = end_idx - begin_idx;
dst.Resize(dst_dims);
dst.offset_ = offset_ + begin_idx * base * SizeOfType(type());
return dst;
}
}
std::type_index type() const {
// PADDLE_ENFORCE_NOT_NULL(
// holder_, "Tensor not initialized yet
// when
// Tensor::type() is called.");
return holder_->type();
}
// memory size returns the holding memory size in byte.
size_t memory_size() const {
return holder_ == nullptr ? 0UL : holder_->size() - offset_;
}
inline void check_memory_size() const {
// PADDLE_ENFORCE_NOT_NULL(
// holder_, "Tensor holds no memory. Call
// Tensor::mutable_data
// first.");
// PADDLE_ENFORCE_LE(
// numel() * SizeOfType(type()), memory_size(),
// "Tensor's dims_ is out of bound. Call
// Tensor::mutable_data "
// "first to re-allocate memory.\n"
// "or maybe the required data-type mismatches the data
// already
// stored.");
}
inline DataLayout layout() const { return layout_; }
inline void set_layout(const DataLayout layout) { layout_ = layout; }
private:
/**
* @note Placeholder hides type T, so it doesn't appear as a
* template
* parameter of Variable.
*/
struct Placeholder {
virtual ~Placeholder() = default;
virtual void *ptr() const = 0;
virtual size_t size() const = 0;
virtual std::type_index type() const = 0;
virtual void set_type(std::type_index type) = 0;
};
struct PlaceholderImpl : public Placeholder {
PlaceholderImpl(size_t size, std::type_index type)
: ptr_(static_cast<uint8_t *>(memory::Alloc(size)),
memory::PODDeleter<uint8_t>()),
size_(size), type_(type) {
// PADDLE_ENFORCE_NOT_NULL(ptr_,
// "Insufficient %s
// memory to allocation.",
// (is_cpu_place(place_)
// ?
// "CPU" :
// "GPU"));
}
} // namespace framework
virtual size_t size() const { return size_; }
virtual void *ptr() const { return static_cast<void *>(ptr_.get()); }
virtual std::type_index type() const { return type_; }
virtual void set_type(std::type_index type) { type_ = type; }
/*! the pointer of memory block. */
std::unique_ptr<uint8_t, memory::PODDeleter<uint8_t>> ptr_;
/*! the size of memory block. */
size_t size_;
/* the current type of memory */
std::type_index type_;
};
/*! holds the memory block if allocated. */
std::shared_ptr<Placeholder> holder_;
/**
* @brief points to elements dimensions.
*
* @note dims_ do not indicate the memory block size.
*/
DDim dims_;
/**
* @brief the layout of memory block, default is NHWC.
*
* @note the memory allocation order, describe how weight/data is
* stored
* For example, in 4-D Tensor(rank=4), there are three
* commonly
* used layout. They are
* NCHW, NHWC, CHWN.
* N,C,H,W for respectively the batch size, the number of
* feature maps, the height, the width.
*/
DataLayout layout_ = DataLayout::kNHWC;
/**
* @brief A PlaceHolder may be shared by more than one tensor.
*
* @note Some of them may be slices of the others. So the offset_
* is introduced here to indicate the byte offset between
* PlaceHolder::ptr_ and where the tensor data really
* begins.
*/
size_t offset_;
};
inline Tensor ReshapeToMatrix(const Tensor &src, int num_col_dims) {
Tensor res;
res.ShareDataWith(src);
res.Resize(flatten_to_2d(src.dims(), num_col_dims));
return res;
}
} // namespace framework
} // namespace paddle_mobile
src/framework/tensor_util.cc
浏览文件 @ 47e5978f
......@@ -18,189 +18,187 @@
#include <vector>
namespace paddle_mobile {
namespace framework {
void TensorCopy(const Tensor &src, Tensor *dst) {
// VLOG(3) << "TensorCopy " << src.dims() << " from " <<
// src.place() << " to
// "
// << dst_place;
src.check_memory_size();
dst->Resize(src.dims());
dst->set_layout(src.layout());
auto src_ptr = src.data<void>();
auto dst_ptr = dst->mutable_data(src.type());
auto size = src.numel() * SizeOfType(src.type());
memory::Copy(dst_ptr, src_ptr, size);
}
void TensorCopySync(const Tensor &src, Tensor *dst) {
// VLOG(3) << "TensorCopySync " << src.dims() << " from " <<
// src.place()
// << " to " << dst_place;
src.check_memory_size();
dst->Resize(src.dims());
dst->set_layout(src.layout());
auto src_ptr = src.data<void>();
auto dst_ptr = dst->mutable_data(src.type());
auto size = src.numel() * SizeOfType(src.type());
memory::Copy(dst_ptr, src_ptr, size);
}
template <typename Predicate> struct AnyDTypeVisitor {
Predicate predicate_;
const Tensor &tensor_;
Tensor *out_;
AnyDTypeVisitor(Predicate predicate, const Tensor &tensor,
Tensor *out)
: predicate_(predicate), tensor_(tensor), out_(out) {}
template <typename T> void operator()() const {
// auto t = EigenVector<T>::Flatten(tensor_);
// auto o = EigenScalar<bool>::From(*out_);
// return any of predicate_(t) is true.
// o.device(*ctx_.eigen_device()) = predicate_(t).any();
}
};
template <typename Predicate>
inline void AnyImpl(Predicate predicate, const Tensor &tensor,
framework::Tensor *out) {
VisitDataType(ToDataType(tensor.type()),
AnyDTypeVisitor<Predicate>(predicate, tensor, out));
}
template <typename Predicate> struct AnyVisitor {
const framework::Tensor &tensor_;
Predicate predicate_;
AnyVisitor(const framework::Tensor &tensor, Predicate predicate)
: tensor_(tensor), predicate_(std::move(predicate)) {}
bool operator()(void) const {
framework::Tensor out;
out.Resize({1});
out.mutable_data<bool>();
AnyImpl(predicate_, tensor_, &out);
return this->GetResult(out);
}
bool GetResult(const framework::Tensor &out) const {
return *out.data<bool>();
}
};
template <typename Predicate>
inline bool Any(const framework::Tensor &tensor, Predicate predicate) {
AnyVisitor<Predicate> visitor(tensor, predicate);
// return platform::VisitPlace(visitor);
return visitor();
}
struct ContainsNANPredicate {
template <typename T>
auto operator()(const T &eigen_vec) const
-> decltype(std::declval<T>().isnan()) {
// Cast eigen_vector to vector of bool. true if is inf.
return eigen_vec.isnan();
}
};
bool TensorContainsNAN(const framework::Tensor &tensor) {
ContainsNANPredicate predicate;
return Any(tensor, predicate);
}
struct ContainsInfPredicate {
template <typename T>
auto operator()(const T &eigen_vec) const
-> decltype(std::declval<T>().isinf()) {
// Cast eigen_vector to vector of bool. true if is inf.
return eigen_vec.isinf();
}
};
bool TensorContainsInf(const framework::Tensor &tensor) {
ContainsInfPredicate predicate;
return Any(tensor, predicate);
}
void TensorToStream(std::ostream &os, const Tensor &tensor) {
{ // the 1st field, uint32_t version
constexpr uint32_t version = 0;
os.write(reinterpret_cast<const char *>(&version),
sizeof(version));
}
{ // the 2nd field, tensor description
// int32_t size
// void* protobuf message
proto::VarType::TensorDesc desc;
desc.set_data_type(framework::ToDataType(tensor.type()));
auto dims = framework::vectorize(tensor.dims());
auto *pb_dims = desc.mutable_dims();
pb_dims->Resize(static_cast<int>(dims.size()), 0);
std::copy(dims.begin(), dims.end(), pb_dims->begin());
int32_t size = desc.ByteSize();
os.write(reinterpret_cast<const char *>(&size), sizeof(size));
auto out = desc.SerializeAsString();
os.write(out.data(), size);
}
{ // the 3rd field, tensor data
uint64_t size = tensor.memory_size();
auto *data_ptr = tensor.data<void>();
// PADDLE_ENFORCE(size <
// std::numeric_limits<std::streamsize>::max(),
// "Index overflow when writing tensor");
os.write(static_cast<const char *>(data_ptr),
static_cast<std::streamsize>(size));
}
}
struct DeserializedDataFunctor {
DeserializedDataFunctor(void **buf, Tensor *tensor)
: buf_(buf), tensor_(tensor) {}
template <typename T> void operator()() {
*buf_ = tensor_->mutable_data<T>();
}
void **buf_;
Tensor *tensor_;
};
void TensorFromStream(std::istream &is, framework::Tensor *tensor) {
uint32_t version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
// PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is supported");
proto::VarType::TensorDesc desc;
{ // int32_t size
// proto buffer
int32_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
std::unique_ptr<char[]> buf(new char[size]);
is.read(reinterpret_cast<char *>(buf.get()), size);
// PADDLE_ENFORCE(desc.ParseFromArray(buf.get(), size),
// "Cannot parse tensor desc");
}
{ // read tensor
std::vector<int64_t> dims;
dims.reserve(static_cast<size_t>(desc.dims().size()));
std::copy(desc.dims().begin(), desc.dims().end(),
std::back_inserter(dims));
tensor->Resize(framework::make_ddim(dims));
void *buf;
framework::VisitDataType(desc.data_type(),
DeserializedDataFunctor(&buf, tensor));
is.read(static_cast<char *>(buf), tensor->memory_size());
}
}
} // namespace framework
namespace framework {
void TensorCopy(const Tensor &src, Tensor *dst) {
// VLOG(3) << "TensorCopy " << src.dims() << " from " <<
// src.place() << " to
// "
// << dst_place;
src.check_memory_size();
dst->Resize(src.dims());
dst->set_layout(src.layout());
auto src_ptr = src.data<void>();
auto dst_ptr = dst->mutable_data(src.type());
auto size = src.numel() * SizeOfType(src.type());
memory::Copy(dst_ptr, src_ptr, size);
}
void TensorCopySync(const Tensor &src, Tensor *dst) {
// VLOG(3) << "TensorCopySync " << src.dims() << " from " <<
// src.place()
// << " to " << dst_place;
src.check_memory_size();
dst->Resize(src.dims());
dst->set_layout(src.layout());
auto src_ptr = src.data<void>();
auto dst_ptr = dst->mutable_data(src.type());
auto size = src.numel() * SizeOfType(src.type());
memory::Copy(dst_ptr, src_ptr, size);
}
template <typename Predicate> struct AnyDTypeVisitor {
Predicate predicate_;
const Tensor &tensor_;
Tensor *out_;
AnyDTypeVisitor(Predicate predicate, const Tensor &tensor, Tensor *out)
: predicate_(predicate), tensor_(tensor), out_(out) {}
template <typename T> void operator()() const {
// auto t = EigenVector<T>::Flatten(tensor_);
// auto o = EigenScalar<bool>::From(*out_);
// return any of predicate_(t) is true.
// o.device(*ctx_.eigen_device()) = predicate_(t).any();
}
};
template <typename Predicate>
inline void AnyImpl(Predicate predicate, const Tensor &tensor,
framework::Tensor *out) {
VisitDataType(ToDataType(tensor.type()),
AnyDTypeVisitor<Predicate>(predicate, tensor, out));
}
template <typename Predicate> struct AnyVisitor {
const framework::Tensor &tensor_;
Predicate predicate_;
AnyVisitor(const framework::Tensor &tensor, Predicate predicate)
: tensor_(tensor), predicate_(std::move(predicate)) {}
bool operator()(void) const {
framework::Tensor out;
out.Resize({1});
out.mutable_data<bool>();
AnyImpl(predicate_, tensor_, &out);
return this->GetResult(out);
}
bool GetResult(const framework::Tensor &out) const {
return *out.data<bool>();
}
};
template <typename Predicate>
inline bool Any(const framework::Tensor &tensor, Predicate predicate) {
AnyVisitor<Predicate> visitor(tensor, predicate);
// return platform::VisitPlace(visitor);
return visitor();
}
struct ContainsNANPredicate {
template <typename T>
auto operator()(const T &eigen_vec) const
-> decltype(std::declval<T>().isnan()) {
// Cast eigen_vector to vector of bool. true if is inf.
return eigen_vec.isnan();
}
};
bool TensorContainsNAN(const framework::Tensor &tensor) {
ContainsNANPredicate predicate;
return Any(tensor, predicate);
}
struct ContainsInfPredicate {
template <typename T>
auto operator()(const T &eigen_vec) const
-> decltype(std::declval<T>().isinf()) {
// Cast eigen_vector to vector of bool. true if is inf.
return eigen_vec.isinf();
}
};
bool TensorContainsInf(const framework::Tensor &tensor) {
ContainsInfPredicate predicate;
return Any(tensor, predicate);
}
void TensorToStream(std::ostream &os, const Tensor &tensor) {
{ // the 1st field, uint32_t version
constexpr uint32_t version = 0;
os.write(reinterpret_cast<const char *>(&version), sizeof(version));
}
{ // the 2nd field, tensor description
// int32_t size
// void* protobuf message
proto::VarType::TensorDesc desc;
desc.set_data_type(framework::ToDataType(tensor.type()));
auto dims = framework::vectorize(tensor.dims());
auto *pb_dims = desc.mutable_dims();
pb_dims->Resize(static_cast<int>(dims.size()), 0);
std::copy(dims.begin(), dims.end(), pb_dims->begin());
int32_t size = desc.ByteSize();
os.write(reinterpret_cast<const char *>(&size), sizeof(size));
auto out = desc.SerializeAsString();
os.write(out.data(), size);
}
{ // the 3rd field, tensor data
uint64_t size = tensor.memory_size();
auto *data_ptr = tensor.data<void>();
// PADDLE_ENFORCE(size <
// std::numeric_limits<std::streamsize>::max(),
// "Index overflow when writing tensor");
os.write(static_cast<const char *>(data_ptr),
static_cast<std::streamsize>(size));
}
}
struct DeserializedDataFunctor {
DeserializedDataFunctor(void **buf, Tensor *tensor)
: buf_(buf), tensor_(tensor) {}
template <typename T> void operator()() {
*buf_ = tensor_->mutable_data<T>();
}
void **buf_;
Tensor *tensor_;
};
void TensorFromStream(std::istream &is, framework::Tensor *tensor) {
uint32_t version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
// PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is supported");
proto::VarType::TensorDesc desc;
{ // int32_t size
// proto buffer
int32_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
std::unique_ptr<char[]> buf(new char[size]);
is.read(reinterpret_cast<char *>(buf.get()), size);
// PADDLE_ENFORCE(desc.ParseFromArray(buf.get(), size),
// "Cannot parse tensor desc");
}
{ // read tensor
std::vector<int64_t> dims;
dims.reserve(static_cast<size_t>(desc.dims().size()));
std::copy(desc.dims().begin(), desc.dims().end(),
std::back_inserter(dims));
tensor->Resize(framework::make_ddim(dims));
void *buf;
framework::VisitDataType(desc.data_type(),
DeserializedDataFunctor(&buf, tensor));
is.read(static_cast<char *>(buf), tensor->memory_size());
}
}
} // namespace framework
} // namespace paddle_mobile
src/framework/tensor_util.h
浏览文件 @ 47e5978f
......@@ -20,47 +20,47 @@ limitations under the License. */
#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
浏览文件 @ 47e5978f
......@@ -20,9 +20,9 @@ SOFTWARE.
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
浏览文件 @ 47e5978f
......@@ -22,68 +22,67 @@ SOFTWARE.
#include "paddle_mobile_object.h"
namespace paddle_mobile {
namespace framework {
namespace framework {
class VarDesc {
public:
VarDesc(const proto::VarDesc &desc);
class VarDesc {
public:
VarDesc(const proto::VarDesc &desc);
std::string Name() const { return desc_.name(); }
std::string Name() const { return desc_.name(); }
proto::VarType::Type GetType() const { return desc_.type().type(); }
proto::VarType::Type GetType() const { return desc_.type().type(); }
bool Persistable() const { return desc_.persistable(); }
bool Persistable() const { return desc_.persistable(); }
const proto::VarType::ChannelDesc &channel_desc() const {
switch (desc_.type().type()) {
case proto::VarType::CHANNEL:
return desc_.type().channel();
default:
break;
}
}
const proto::VarType::ChannelDesc &channel_desc() const {
switch (desc_.type().type()) {
case proto::VarType::CHANNEL:
return desc_.type().channel();
default:
break;
}
}
const proto::VarType::TensorDesc &tensor_desc() const {
switch (desc_.type().type()) {
case proto::VarType::SELECTED_ROWS:
return desc_.type().selected_rows();
case proto::VarType::LOD_TENSOR:
return desc_.type().lod_tensor().tensor();
case proto::VarType::LOD_TENSOR_ARRAY:
return desc_.type().tensor_array().tensor();
default:
break;
}
}
const proto::VarType::TensorDesc &tensor_desc() const {
switch (desc_.type().type()) {
case proto::VarType::SELECTED_ROWS:
return desc_.type().selected_rows();
case proto::VarType::LOD_TENSOR:
return desc_.type().lod_tensor().tensor();
case proto::VarType::LOD_TENSOR_ARRAY:
return desc_.type().tensor_array().tensor();
default:
break;
}
}
proto::VarType::Type GetDataType() const {
switch (desc_.type().type()) {
case proto::VarType::CHANNEL:
return channel_desc().data_type();
break;
default:
return tensor_desc().data_type();
}
}
proto::VarType::Type GetDataType() const {
switch (desc_.type().type()) {
case proto::VarType::CHANNEL:
return channel_desc().data_type();
break;
default:
return tensor_desc().data_type();
}
}
template <typename T>
std::vector<T> RepeatedToVector(
const google::protobuf::RepeatedField<T> &repeated_field)
const {
std::vector<T> ret;
ret.reserve(repeated_field.size());
std::copy(repeated_field.begin(), repeated_field.end(),
std::back_inserter(ret));
return ret;
}
template <typename T>
std::vector<T> RepeatedToVector(
const google::protobuf::RepeatedField<T> &repeated_field) const {
std::vector<T> ret;
ret.reserve(repeated_field.size());
std::copy(repeated_field.begin(), repeated_field.end(),
std::back_inserter(ret));
return ret;
}
std::vector<int64_t> GetShape() const {
return this->RepeatedToVector(tensor_desc().dims());
}
std::vector<int64_t> GetShape() const {
return this->RepeatedToVector(tensor_desc().dims());
}
private:
proto::VarDesc desc_;
};
private:
proto::VarDesc desc_;
};
} // namespace framework
} // namespace framework
} // namespace paddle_mobile
src/framework/var_type.h
浏览文件 @ 47e5978f
......@@ -23,17 +23,17 @@ SOFTWARE.
#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
浏览文件 @ 47e5978f
......@@ -26,71 +26,71 @@ SOFTWARE.
#include <typeinfo>
namespace paddle_mobile {
namespace framework {
class Variable : public PaddleMobileObject {
public:
template <typename T> const T *Get() const {
return static_cast<const T *>(holder_->Ptr());
namespace framework {
class Variable : public PaddleMobileObject {
public:
template <typename T> const T *Get() const {
return static_cast<const T *>(holder_->Ptr());
}
bool IsInitialized() const { return holder_ != nullptr; }
const std::string *Name() { return name_; }
template <typename T> T *GetMutable() {
if (!IsType<T>()) {
if (*Name() == "pixel") {
// std::cout << " reset " << *Name() <<
// std::endl;
}
bool IsInitialized() const { return holder_ != nullptr; }
const std::string *Name() { return name_; }
template <typename T> T *GetMutable() {
if (!IsType<T>()) {
if (*Name() == "pixel") {
// std::cout << " reset " << *Name() <<
// std::endl;
}
holder_.reset(new PlaceholderImp<T>(new T()));
}
return static_cast<T *>(holder_->Ptr());
}
template <typename T> bool IsType() const {
if (holder_) {
// printf("not null \n");
printf(" holder type : %s, this type %s \n",
holder_->Type().name(), typeid(T).name());
}
// std::cout << " " << holder_->Type() << " " <<
// typeid(T) <<
// std::endl;
return holder_ != nullptr && holder_->Type() == typeid(T);
}
void Clear() { holder_.reset(); }
std::type_index Type() const { return holder_->Type(); }
void SetName(const std::string *name) { name_ = name; }
private:
struct Placeholder {
Placeholder() = default;
virtual ~Placeholder() = default;
virtual const std::type_info &Type() const = 0;
virtual void *Ptr() const = 0;
};
template <typename T> struct PlaceholderImp : public Placeholder {
explicit PlaceholderImp(T *ptr) : ptr_(ptr), type_(typeid(T)) {}
virtual const std::type_info &Type() const { return type_; }
virtual void *Ptr() const override {
return static_cast<void *>(ptr_.get());
}
std::unique_ptr<T> ptr_;
const std::type_info &type_;
};
std::unique_ptr<Placeholder> holder_;
friend class Scope;
const std::string *name_;
};
} // namespace framework
holder_.reset(new PlaceholderImp<T>(new T()));
}
return static_cast<T *>(holder_->Ptr());
}
template <typename T> bool IsType() const {
if (holder_) {
// printf("not null \n");
printf(" holder type : %s, this type %s \n", holder_->Type().name(),
typeid(T).name());
}
// std::cout << " " << holder_->Type() << " " <<
// typeid(T) <<
// std::endl;
return holder_ != nullptr && holder_->Type() == typeid(T);
}
void Clear() { holder_.reset(); }
std::type_index Type() const { return holder_->Type(); }
void SetName(const std::string *name) { name_ = name; }
private:
struct Placeholder {
Placeholder() = default;
virtual ~Placeholder() = default;
virtual const std::type_info &Type() const = 0;
virtual void *Ptr() const = 0;
};
template <typename T> struct PlaceholderImp : public Placeholder {
explicit PlaceholderImp(T *ptr) : ptr_(ptr), type_(typeid(T)) {}
virtual const std::type_info &Type() const { return type_; }
virtual void *Ptr() const override {
return static_cast<void *>(ptr_.get());
}
std::unique_ptr<T> ptr_;
const std::type_info &type_;
};
std::unique_ptr<Placeholder> holder_;
friend class Scope;
const std::string *name_;
};
} // namespace framework
} // namespace paddle_mobile
src/io.cpp
浏览文件 @ 47e5978f
......@@ -28,398 +28,386 @@ SOFTWARE.
namespace paddle_mobile {
void ReadBinaryFile(const std::string &filename, std::string *contents) {
std::ifstream fin(filename, std::ios::in | std::ios::binary);
fin.seekg(0, std::ios::end);
contents->clear();
contents->resize(fin.tellg());
fin.seekg(0, std::ios::beg);
fin.read(&(contents->at(0)), contents->size());
fin.close();
}
template <typename Dtype, Precision P>
void Loader<Dtype, P>::LoadVar(framework::LoDTensor *tensor,
const std::string &file_path) {
// LOG(kLOG_DEBUG) << " to load " << file_path;
// Log(kLOG_DEBUG) << "123";
std::ifstream is(file_path);
std::streampos pos = is.tellg(); // save current position
is.seekg(0, std::ios::end);
// LOG(kLOG_DEBUG) << " file length = " << is.tellg();
is.seekg(pos); // restore saved position
// 1. version
uint32_t version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
// LOG(kLOG_INFO) << " version: " << version;
// 2 Lod information
uint64_t lod_level;
is.read(reinterpret_cast<char *>(&lod_level), sizeof(lod_level));
// LOG(kLOG_DEBUG) << " load level: " << lod_level;
// LOG(kLOG_DEBUG) << " lod info: ";
auto &lod = *tensor->mutable_lod();
lod.resize(lod_level);
for (uint64_t i = 0; i < lod_level; ++i) {
uint64_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
std::vector<size_t> tmp(size / sizeof(size_t));
is.read(reinterpret_cast<char *>(tmp.data()),
static_cast<std::streamsize>(size));
for (int j = 0; j < tmp.size(); ++j) {
LOG(kLOG_DEBUG1) << " lod - " << tmp[j];
}
lod[i] = tmp;
}
// 3. tensor version
uint32_t tensor_version;
is.read(reinterpret_cast<char *>(&tensor_version),
sizeof(tensor_version));
// std::cout << " tensor_version: " << tensor_version << std::endl;
// 4. tensor desc
int32_t size;
void ReadBinaryFile(const std::string &filename, std::string *contents) {
std::ifstream fin(filename, std::ios::in | std::ios::binary);
fin.seekg(0, std::ios::end);
contents->clear();
contents->resize(fin.tellg());
fin.seekg(0, std::ios::beg);
fin.read(&(contents->at(0)), contents->size());
fin.close();
}
template <typename Dtype, Precision P>
void Loader<Dtype, P>::LoadVar(framework::LoDTensor *tensor,
const std::string &file_path) {
// LOG(kLOG_DEBUG) << " to load " << file_path;
// Log(kLOG_DEBUG) << "123";
std::ifstream is(file_path);
std::streampos pos = is.tellg(); // save current position
is.seekg(0, std::ios::end);
// LOG(kLOG_DEBUG) << " file length = " << is.tellg();
is.seekg(pos); // restore saved position
// 1. version
uint32_t version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
// LOG(kLOG_INFO) << " version: " << version;
// 2 Lod information
uint64_t lod_level;
is.read(reinterpret_cast<char *>(&lod_level), sizeof(lod_level));
// LOG(kLOG_DEBUG) << " load level: " << lod_level;
// LOG(kLOG_DEBUG) << " lod info: ";
auto &lod = *tensor->mutable_lod();
lod.resize(lod_level);
for (uint64_t i = 0; i < lod_level; ++i) {
uint64_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
// std::cout << " tensor desc size: " << size << std::endl;
std::unique_ptr<char[]> buf(new char[size]);
is.read(reinterpret_cast<char *>(buf.get()), size);
framework::proto::VarType::TensorDesc desc;
desc.ParseFromArray(buf.get(), size);
// std::cout << " desc dims size " << desc.dims().size() <<
// std::endl;
int memory_size = 1;
for (int l = 0; l < desc.dims().size(); ++l) {
// std::cout << " dim " << l << " value: " << desc.dims()[l]
// <<
// std::endl;
memory_size *= desc.dims()[l];
std::vector<size_t> tmp(size / sizeof(size_t));
is.read(reinterpret_cast<char *>(tmp.data()),
static_cast<std::streamsize>(size));
for (int j = 0; j < tmp.size(); ++j) {
LOG(kLOG_DEBUG1) << " lod - " << tmp[j];
}
lod[i] = tmp;
}
std::vector<int64_t> dims;
dims.reserve(static_cast<size_t>(desc.dims().size()));
std::copy(desc.dims().begin(), desc.dims().end(),
std::back_inserter(dims));
tensor->Resize(framework::make_ddim(dims));
void *memory;
int type_size = 0;
// std::cout << " desc pre type: ";
switch (desc.data_type()) {
case framework::proto::VarType::FP16:
// std::cout << "FP16" << std::endl;
type_size = 2;
break;
case framework::proto::VarType::FP32:
type_size = 4;
memory = tensor->mutable_data<float>();
// std::cout << "FP32" << std::endl;
break;
case framework::proto::VarType::FP64:
type_size = 8;
// std::cout << "FP64" << std::endl;
break;
case framework::proto::VarType::INT32:
type_size = 4;
// std::cout << "INT32" << std::endl;
break;
case framework::proto::VarType::INT64:
type_size = 8;
// std::cout << "INT64" << std::endl;
break;
case framework::proto::VarType::BOOL:
type_size = 1;
// std::cout << "BOOL" << std::endl;
break;
default:
break;
// std::cout << " not support" << std::endl;
}
// 3. tensor version
uint32_t tensor_version;
is.read(reinterpret_cast<char *>(&tensor_version), sizeof(tensor_version));
// std::cout << " tensor_version: " << tensor_version << std::endl;
// 4. tensor desc
int32_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
// std::cout << " tensor desc size: " << size << std::endl;
std::unique_ptr<char[]> buf(new char[size]);
is.read(reinterpret_cast<char *>(buf.get()), size);
framework::proto::VarType::TensorDesc desc;
desc.ParseFromArray(buf.get(), size);
// std::cout << " desc dims size " << desc.dims().size() <<
// std::endl;
int memory_size = 1;
for (int l = 0; l < desc.dims().size(); ++l) {
// std::cout << " dim " << l << " value: " << desc.dims()[l]
// <<
// std::endl;
memory_size *= desc.dims()[l];
}
// std::cout << " malloc size: " << memory_size * type_size <<
// std::endl;
is.read(static_cast<char *>(memory), memory_size * type_size);
// std::cout << " memory: " << memory << std::endl;
is.close();
};
template <typename Dtype, Precision P>
const framework::Program<Dtype, P>
Loader<Dtype, P>::Load(const std::string &dirname) {
std::string model_filename = dirname + "/__model__";
std::string program_desc_str;
ReadBinaryFile(model_filename, &program_desc_str);
framework::proto::ProgramDesc program_desc_proto;
program_desc_proto.ParseFromString(program_desc_str);
std::shared_ptr<framework::ProgramDesc> originProgramDesc =
std::make_shared<framework::ProgramDesc>(program_desc_proto);
framework::Program<Dtype, P> program;
program.originProgram = originProgramDesc;
std::shared_ptr<framework::Scope> scope =
std::make_shared<framework::Scope>();
program.scope = scope;
auto block = originProgramDesc->Block(0);
for (auto block : originProgramDesc->Blocks()) {
// std::cout << "for block" << std::endl;
for (int i = 0; i < block->Vars().size(); ++i) {
std::shared_ptr<framework::VarDesc> var_desc = block->Vars()[i];
auto var = scope->Var(var_desc->Name());
if (var_desc->GetType() ==
framework::proto::VarType::LOD_TENSOR) {
if (var_desc->Persistable() &&
var_desc->GetType() !=
framework::proto::VarType::FEED_MINIBATCH &&
var_desc->GetType() !=
framework::proto::VarType::FETCH_LIST) {
framework::LoDTensor *tensor =
var->GetMutable<framework::LoDTensor>();
// to load
LoadVar(tensor, dirname + "/" + var_desc->Name());
}
} else {
// std::cout << "非 lod" << std::endl;
std::vector<int64_t> dims;
dims.reserve(static_cast<size_t>(desc.dims().size()));
std::copy(desc.dims().begin(), desc.dims().end(), std::back_inserter(dims));
tensor->Resize(framework::make_ddim(dims));
void *memory;
int type_size = 0;
// std::cout << " desc pre type: ";
switch (desc.data_type()) {
case framework::proto::VarType::FP16:
// std::cout << "FP16" << std::endl;
type_size = 2;
break;
case framework::proto::VarType::FP32:
type_size = 4;
memory = tensor->mutable_data<float>();
// std::cout << "FP32" << std::endl;
break;
case framework::proto::VarType::FP64:
type_size = 8;
// std::cout << "FP64" << std::endl;
break;
case framework::proto::VarType::INT32:
type_size = 4;
// std::cout << "INT32" << std::endl;
break;
case framework::proto::VarType::INT64:
type_size = 8;
// std::cout << "INT64" << std::endl;
break;
case framework::proto::VarType::BOOL:
type_size = 1;
// std::cout << "BOOL" << std::endl;
break;
default:
break;
// std::cout << " not support" << std::endl;
}
// std::cout << " malloc size: " << memory_size * type_size <<
// std::endl;
is.read(static_cast<char *>(memory), memory_size * type_size);
// std::cout << " memory: " << memory << std::endl;
is.close();
};
template <typename Dtype, Precision P>
const framework::Program<Dtype, P>
Loader<Dtype, P>::Load(const std::string &dirname) {
std::string model_filename = dirname + "/__model__";
std::string program_desc_str;
ReadBinaryFile(model_filename, &program_desc_str);
framework::proto::ProgramDesc program_desc_proto;
program_desc_proto.ParseFromString(program_desc_str);
std::shared_ptr<framework::ProgramDesc> originProgramDesc =
std::make_shared<framework::ProgramDesc>(program_desc_proto);
framework::Program<Dtype, P> program;
program.originProgram = originProgramDesc;
std::shared_ptr<framework::Scope> scope =
std::make_shared<framework::Scope>();
program.scope = scope;
auto block = originProgramDesc->Block(0);
for (auto block : originProgramDesc->Blocks()) {
// std::cout << "for block" << std::endl;
for (int i = 0; i < block->Vars().size(); ++i) {
std::shared_ptr<framework::VarDesc> var_desc = block->Vars()[i];
auto var = scope->Var(var_desc->Name());
if (var_desc->GetType() == framework::proto::VarType::LOD_TENSOR) {
if (var_desc->Persistable() &&
var_desc->GetType() !=
framework::proto::VarType::FEED_MINIBATCH &&
var_desc->GetType() !=
framework::proto::VarType::FETCH_LIST) {
framework::LoDTensor *tensor =
var->GetMutable<framework::LoDTensor>();
// to load
LoadVar(tensor, dirname + "/" + var_desc->Name());
}
} else {
// std::cout << "非 lod" << std::endl;
}
}
}
#ifdef PADDLE_MOBILE_DEBUG
for (int i = 0; i < program_desc_proto.blocks().size(); ++i) {
framework::proto::BlockDesc block = program_desc_proto.blocks()[i];
LOG(kLOG_DEBUG) << "block: " << block.idx();
for (int j = 0; j < block.ops().size(); ++j) {
framework::proto::OpDesc op = block.ops()[j];
LOG(kLOG_DEBUG1) << " op: " << op.type();
for (int m = 0; m < op.inputs_size(); ++m) {
const framework::proto::OpDesc::Var &var = op.inputs(m);
LOG(kLOG_DEBUG2) << " input parameter: "
<< var.parameter();
for (int n = 0; n < var.arguments().size(); ++n) {
LOG(kLOG_DEBUG3) << " argument - "
<< var.arguments()[n];
}
for (int i = 0; i < program_desc_proto.blocks().size(); ++i) {
framework::proto::BlockDesc block = program_desc_proto.blocks()[i];
LOG(kLOG_DEBUG) << "block: " << block.idx();
for (int j = 0; j < block.ops().size(); ++j) {
framework::proto::OpDesc op = block.ops()[j];
LOG(kLOG_DEBUG1) << " op: " << op.type();
for (int m = 0; m < op.inputs_size(); ++m) {
const framework::proto::OpDesc::Var &var = op.inputs(m);
LOG(kLOG_DEBUG2) << " input parameter: " << var.parameter();
for (int n = 0; n < var.arguments().size(); ++n) {
LOG(kLOG_DEBUG3) << " argument - " << var.arguments()[n];
}
}
for (int y = 0; y < op.outputs_size(); ++y) {
const framework::proto::OpDesc::Var &var = op.outputs(y);
LOG(kLOG_DEBUG2) << " out parameter: " << var.parameter();
for (int z = 0; z < var.arguments().size(); ++z) {
LOG(kLOG_DEBUG3) << " argument - "
<< var.arguments()[z];
}
for (int y = 0; y < op.outputs_size(); ++y) {
const framework::proto::OpDesc::Var &var = op.outputs(y);
LOG(kLOG_DEBUG2) << " out parameter: " << var.parameter();
for (int z = 0; z < var.arguments().size(); ++z) {
LOG(kLOG_DEBUG3) << " argument - " << var.arguments()[z];
}
}
for (int x = 0; x < op.attrs().size(); ++x) {
const framework::proto::OpDesc_Attr attr = op.attrs()[x];
// std::cout << " attr name: " << attr.name() <<
// std::endl;
// std::cout << " attr type: " << attr.type() <<
// std::endl;
for (int x = 0; x < op.attrs().size(); ++x) {
const framework::proto::OpDesc_Attr attr = op.attrs()[x];
// std::cout << " attr name: " << attr.name() <<
// std::endl;
// std::cout << " attr type: " << attr.type() <<
// std::endl;
switch (attr.type()) {
case framework::proto::AttrType::BOOLEAN:
// std::cout << " boolen: " << attr.b() <<
// std::endl;
break;
case framework::proto::AttrType::INT:
// std::cout << " int: " << attr.i() <<
// std::endl;
break;
case framework::proto::AttrType::FLOAT:
// std::cout << " float: " << attr.f() <<
switch (attr.type()) {
case framework::proto::AttrType::BOOLEAN:
// std::cout << " boolen: " << attr.b() <<
// std::endl;
case framework::proto::AttrType::STRING:
// std::cout << " string: " << attr.s() <<
break;
case framework::proto::AttrType::INT:
// std::cout << " int: " << attr.i() <<
// std::endl;
case framework::proto::AttrType::BOOLEANS:
// std::vector<bool>
// bools(attr.bools_size());
for (int y = 0; y < attr.bools_size(); ++y) {
// std::cout << " bool - " <<
// attr.bools(y) <<
// std::endl;
}
case framework::proto::AttrType::LONG:
// std::cout << " long: " << attr.l() <<
// std::endl;
case framework::proto::AttrType::FLOATS:
for (int y = 0; y < attr.floats_size(); ++y) {
// std::cout << " float - " << y <<
// ": " <<
// attr.floats(y)
// << std::endl;
}
case framework::proto::AttrType::INTS:
for (int y = 0; y < attr.ints_size(); ++y) {
// std::cout << " int - " << y << ":
// " <<
// attr.ints(y)
// << std::endl;
}
case framework::proto::AttrType::STRINGS:
for (int y = 0; y < attr.strings_size(); ++y) {
// std::cout << " string - " << y <<
// ": " <<
// attr.strings(y)
// << std::endl;
}
break;
case framework::proto::AttrType::FLOAT:
// std::cout << " float: " << attr.f() <<
// std::endl;
case framework::proto::AttrType::STRING:
// std::cout << " string: " << attr.s() <<
// std::endl;
case framework::proto::AttrType::BOOLEANS:
// std::vector<bool>
// bools(attr.bools_size());
for (int y = 0; y < attr.bools_size(); ++y) {
// std::cout << " bool - " <<
// attr.bools(y) <<
// std::endl;
}
case framework::proto::AttrType::LONG:
// std::cout << " long: " << attr.l() <<
// std::endl;
case framework::proto::AttrType::FLOATS:
for (int y = 0; y < attr.floats_size(); ++y) {
// std::cout << " float - " << y <<
// ": " <<
// attr.floats(y)
// << std::endl;
}
case framework::proto::AttrType::INTS:
for (int y = 0; y < attr.ints_size(); ++y) {
// std::cout << " int - " << y << ":
// " <<
// attr.ints(y)
// << std::endl;
}
case framework::proto::AttrType::STRINGS:
for (int y = 0; y < attr.strings_size(); ++y) {
// std::cout << " string - " << y <<
// ": " <<
// attr.strings(y)
// << std::endl;
}
}
}
}
for (int k = 0; k < block.vars().size(); ++k) {
framework::proto::VarDesc var = block.vars()[k];
if (var.type().type() ==
framework::proto::VarType::LOD_TENSOR) {
// std::cout << " var name: " << var.name() <<
// std::endl;
const framework::proto::VarType::TensorDesc &tensor_desc =
var.type().lod_tensor().tensor();
// std::cout << " in var tensor desc dims size "
// << tensor_desc.dims().size() <<
// std::endl;
int memory_size = 1;
for (int l = 0; l < tensor_desc.dims().size(); ++l) {
// std::cout << " var tensor desc dim " << l
// << " value: " <<
// tensor_desc.dims()[l] <<
// std::endl;
}
for (int k = 0; k < block.vars().size(); ++k) {
framework::proto::VarDesc var = block.vars()[k];
if (var.type().type() == framework::proto::VarType::LOD_TENSOR) {
// std::cout << " var name: " << var.name() <<
// std::endl;
const framework::proto::VarType::TensorDesc &tensor_desc =
var.type().lod_tensor().tensor();
// std::cout << " in var tensor desc dims size "
// << tensor_desc.dims().size() <<
// std::endl;
int memory_size = 1;
for (int l = 0; l < tensor_desc.dims().size(); ++l) {
// std::cout << " var tensor desc dim " << l
// << " value: " <<
// tensor_desc.dims()[l] <<
// std::endl;
}
}
if (var.persistable() &&
var.type().type() !=
framework::proto::VarType::FEED_MINIBATCH &&
var.type().type() !=
framework::proto::VarType::FETCH_LIST) {
// std::cout << " to load " << var.name() <<
// std::endl;
std::string file_path = dirname + "/" + var.name();
std::ifstream is(file_path);
std::streampos pos =
is.tellg(); // save current position
is.seekg(0, std::ios::end);
// std::cout << " file length = " << is.tellg() <<
// std::endl;
is.seekg(pos); // restore saved position
// 1. version
uint32_t version;
is.read(reinterpret_cast<char *>(&version),
sizeof(version));
// std::cout << " version: " << version <<
// std::endl;
// 2 Lod information
uint64_t lod_level;
is.read(reinterpret_cast<char *>(&lod_level),
sizeof(lod_level));
// std::cout << " load level: " << lod_level <<
// std::endl;
// std::cout << " lod info: " << std::endl;
for (uint64_t i = 0; i < lod_level; ++i) {
uint64_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
std::vector<size_t> tmp(size / sizeof(size_t));
is.read(reinterpret_cast<char *>(tmp.data()),
static_cast<std::streamsize>(size));
for (int j = 0; j < tmp.size(); ++j) {
// std::cout << " lod - " << tmp[j] <<
// std::endl;
}
}
uint32_t tensor_version;
is.read(reinterpret_cast<char *>(&version),
sizeof(version));
// std::cout << " tensor_version: " <<
// tensor_version <<
// std::endl;
int32_t size;
if (var.persistable() &&
var.type().type() !=
framework::proto::VarType::FEED_MINIBATCH &&
var.type().type() != framework::proto::VarType::FETCH_LIST) {
// std::cout << " to load " << var.name() <<
// std::endl;
std::string file_path = dirname + "/" + var.name();
std::ifstream is(file_path);
std::streampos pos = is.tellg(); // save current position
is.seekg(0, std::ios::end);
// std::cout << " file length = " << is.tellg() <<
// std::endl;
is.seekg(pos); // restore saved position
// 1. version
uint32_t version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
// std::cout << " version: " << version <<
// std::endl;
// 2 Lod information
uint64_t lod_level;
is.read(reinterpret_cast<char *>(&lod_level),
sizeof(lod_level));
// std::cout << " load level: " << lod_level <<
// std::endl;
// std::cout << " lod info: " << std::endl;
for (uint64_t i = 0; i < lod_level; ++i) {
uint64_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
// std::cout << " tensor desc size: " << size <<
// std::endl;
std::unique_ptr<char[]> buf(new char[size]);
is.read(reinterpret_cast<char *>(buf.get()), size);
framework::proto::VarType::TensorDesc desc;
desc.ParseFromArray(buf.get(), size);
// std::cout << " desc dims size " <<
// desc.dims().size() <<
// std::endl;
int memory_size = 1;
for (int l = 0; l < desc.dims().size(); ++l) {
// std::cout << " dim " << l << " value: "
// <<
// desc.dims()[l]
// << std::endl;
memory_size *= desc.dims()[l];
}
int type_size = 0;
// std::cout << " desc pre type: ";
switch (desc.data_type()) {
case framework::proto::VarType::FP16:
// std::cout << "FP16" << std::endl;
type_size = 2;
break;
case framework::proto::VarType::FP32:
type_size = 4;
// std::cout << "FP32" << std::endl;
break;
case framework::proto::VarType::FP64:
type_size = 8;
// std::cout << "FP64" << std::endl;
break;
case framework::proto::VarType::INT32:
type_size = 4;
// std::cout << "INT32" << std::endl;
break;
case framework::proto::VarType::INT64:
type_size = 8;
// std::cout << "INT64" << std::endl;
break;
case framework::proto::VarType::BOOL:
type_size = 1;
// std::cout << "BOOL" << std::endl;
break;
default:
break;
// std::cout << " not support" <<
std::vector<size_t> tmp(size / sizeof(size_t));
is.read(reinterpret_cast<char *>(tmp.data()),
static_cast<std::streamsize>(size));
for (int j = 0; j < tmp.size(); ++j) {
// std::cout << " lod - " << tmp[j] <<
// std::endl;
}
}
uint32_t tensor_version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
// std::cout << " tensor_version: " <<
// tensor_version <<
// std::endl;
int32_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
// std::cout << " tensor desc size: " << size <<
// std::endl;
std::unique_ptr<char[]> buf(new char[size]);
is.read(reinterpret_cast<char *>(buf.get()), size);
framework::proto::VarType::TensorDesc desc;
desc.ParseFromArray(buf.get(), size);
// std::cout << " desc dims size " <<
// desc.dims().size() <<
// std::endl;
int memory_size = 1;
for (int l = 0; l < desc.dims().size(); ++l) {
// std::cout << " dim " << l << " value: "
// <<
// desc.dims()[l]
// << std::endl;
memory_size *= desc.dims()[l];
}
// std::cout << " malloc size: " << memory_size *
// type_size
// << std::endl;
void *memory = malloc(memory_size * type_size);
is.read(static_cast<char *>(memory),
memory_size * type_size);
// std::cout << " memory: " << memory <<
// std::endl;
is.close();
} else {
// std::cout << " *not load "
// << " var : " << var.name() << std::endl;
int type_size = 0;
// std::cout << " desc pre type: ";
switch (desc.data_type()) {
case framework::proto::VarType::FP16:
// std::cout << "FP16" << std::endl;
type_size = 2;
break;
case framework::proto::VarType::FP32:
type_size = 4;
// std::cout << "FP32" << std::endl;
break;
case framework::proto::VarType::FP64:
type_size = 8;
// std::cout << "FP64" << std::endl;
break;
case framework::proto::VarType::INT32:
type_size = 4;
// std::cout << "INT32" << std::endl;
break;
case framework::proto::VarType::INT64:
type_size = 8;
// std::cout << "INT64" << std::endl;
break;
case framework::proto::VarType::BOOL:
type_size = 1;
// std::cout << "BOOL" << std::endl;
break;
default:
break;
// std::cout << " not support" <<
// std::endl;
}
// std::cout << " malloc size: " << memory_size *
// type_size
// << std::endl;
void *memory = malloc(memory_size * type_size);
is.read(static_cast<char *>(memory), memory_size * type_size);
// std::cout << " memory: " << memory <<
// std::endl;
is.close();
} else {
// std::cout << " *not load "
// << " var : " << var.name() << std::endl;
}
}
}
#endif
return program;
}
return program;
}
template class Loader<CPU, Precision::FP32>;
template class Loader<CPU, Precision::FP32>;
} // namespace paddle_mobile
src/io.h
浏览文件 @ 47e5978f
......@@ -27,14 +27,13 @@ SOFTWARE.
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
浏览文件 @ 47e5978f
......@@ -22,30 +22,30 @@ SOFTWARE.
#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
浏览文件 @ 47e5978f
......@@ -21,44 +21,44 @@ SOFTWARE.
#include <type_traits>
namespace paddle_mobile {
namespace memory {
namespace memory {
void Copy(void *dst, const void *src, size_t num);
void Copy(void *dst, const void *src, size_t num);
void *Alloc(size_t size);
void *Alloc(size_t size);
void Free(void *ptr);
void Free(void *ptr);
/**
* \brief Free memory block in one place.
*
* \note In some cases, custom deleter is used to
* deallocate the memory automatically for
* std::unique_ptr<T> in tensor.h.
* static_cast
*/
template <typename T> class PODDeleter {
static_assert(std::is_pod<T>::value, "T must be POD");
/**
* \brief Free memory block in one place.
*
* \note In some cases, custom deleter is used to
* deallocate the memory automatically for
* std::unique_ptr<T> in tensor.h.
* static_cast
*/
template <typename T> class PODDeleter {
static_assert(std::is_pod<T>::value, "T must be POD");
public:
explicit PODDeleter(){};
public:
explicit PODDeleter(){};
void operator()(T *ptr) { Free(static_cast<void *>(ptr)); }
};
void operator()(T *ptr) { Free(static_cast<void *>(ptr)); }
};
/**
* \brief Free memory block in one place does not meet POD
*
* \note In some cases, custom deleter is used to
* deallocate the memory automatically for
* std::unique_ptr<T> in tensor.h.
* reinterpret_cast
*/
template <typename T> class PlainDeleter {
public:
explicit PlainDeleter(){};
/**
* \brief Free memory block in one place does not meet POD
*
* \note In some cases, custom deleter is used to
* deallocate the memory automatically for
* std::unique_ptr<T> in tensor.h.
* reinterpret_cast
*/
template <typename T> class PlainDeleter {
public:
explicit PlainDeleter(){};
void operator()(T *ptr) { Free(reinterpret_cast<void *>(ptr)); }
};
} // namespace memory
void operator()(T *ptr) { Free(reinterpret_cast<void *>(ptr)); }
};
} // namespace memory
} // namespace paddle_mobile
src/operators/conv_op.cpp
浏览文件 @ 47e5978f
......@@ -23,50 +23,50 @@ SOFTWARE.
namespace paddle_mobile {
namespace operators {
int ConvOutputSize(int input_size, int filter_size, int dilation,
int padding, int stride) {
const int dkernel = dilation * (filter_size - 1) + 1;
int output_size = (input_size + 2 * padding - dkernel) / stride + 1;
return output_size;
int ConvOutputSize(int input_size, int filter_size, int dilation, int padding,
int stride) {
const int dkernel = dilation * (filter_size - 1) + 1;
int output_size = (input_size + 2 * padding - dkernel) / stride + 1;
return output_size;
}
template<typename Dtype, typename T>
template <typename Dtype, typename T>
void ConvOp<Dtype, T>::InferShape() const {
// std::cout << " begin get dims: " << std::endl;
// std::cout << " begin get dims: " << std::endl;
auto in_dims = param_.Input()->dims();
auto in_dims = param_.Input()->dims();
// std::cout << " end get in dims: " << std::endl;
// std::cout << " end get in dims: " << std::endl;
// std::cout << " in_dims: " << in_dims << std::endl;
// std::cout << " in_dims: " << in_dims << std::endl;
// std::cout << " begin get Filter " << std::endl;
// std::cout << " begin get Filter " << std::endl;
auto filter_dims = param_.Filter()->dims();
auto filter_dims = param_.Filter()->dims();
// std::cout << " end get Filter " << std::endl;
// std::cout << " end get Filter " << std::endl;
// std::cout << " begin get Attrs " << std::endl;
// std::cout << " begin get Attrs " << std::endl;
const std::vector<int> &strides = param_.Strides();
const std::vector<int> &strides = param_.Strides();
// std::cout << " end get Attrs " << strides[0] << std::endl;
// std::cout << " end get Attrs " << strides[0] << std::endl;
std::vector<int> paddings = param_.Paddings();
std::vector<int> paddings = param_.Paddings();
int groups = param_.Groups();
int groups = param_.Groups();
std::vector<int> dilations = param_.Dilations();
std::vector<int> dilations = param_.Dilations();
std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
for (size_t i = 0; i < strides.size(); ++i) {
output_shape.push_back(
ConvOutputSize(in_dims[i + 2], filter_dims[i + 2],
dilations[i], paddings[i], strides[i]));
}
std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
for (size_t i = 0; i < strides.size(); ++i) {
output_shape.push_back(ConvOutputSize(in_dims[i + 2],
filter_dims[i + 2], dilations[i],
paddings[i], strides[i]));
}
framework::DDim ddim = framework::make_ddim(output_shape);
param_.Output()->Resize(ddim);
framework::DDim ddim = framework::make_ddim(output_shape);
param_.Output()->Resize(ddim);
}
template class ConvOp<CPU, float>;
......
src/operators/conv_op.h
浏览文件 @ 47e5978f
......@@ -26,29 +26,28 @@ namespace operators {
using namespace framework;
template<typename DeviceType, typename T>
template <typename DeviceType, typename T>
class ConvOp : public framework::OperatorWithKernel<DeviceType> {
public:
ConvOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType>(
type, inputs, outputs, attrs, scope),
param_(inputs, outputs, attrs, *scope) {}
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override;
void Run() const {
operators::ConvKernel<DeviceType, T, ConvParam> kernel;
kernel.Compute(param_);
this->ClearVariables();
}
private:
ConvParam param_;
public:
ConvOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const framework::AttributeMap &attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType>(type, inputs, outputs,
attrs, scope),
param_(inputs, outputs, attrs, *scope) {}
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override;
void Run() const {
operators::ConvKernel<DeviceType, T, ConvParam> kernel;
kernel.Compute(param_);
this->ClearVariables();
}
private:
ConvParam param_;
};
} // operators
} // paddle_mobile
} // namespace operators
} // namespace paddle_mobile
src/operators/elementwise_add_op.cpp
浏览文件 @ 47e5978f
......@@ -21,11 +21,11 @@ SOFTWARE.
namespace paddle_mobile {
namespace operators {
template<typename Dtype, typename T>
template <typename Dtype, typename T>
void ElementwiseAddOp<Dtype, T>::InferShape() const {
auto x_dim = param_.InputX()->dims();
param_.Out()->Resize(x_dim);
auto x_dim = param_.InputX()->dims();
param_.Out()->Resize(x_dim);
}
template class ElementwiseAddOp<CPU, float>;
}
}
} // namespace operators
} // namespace paddle_mobile
src/operators/elementwise_add_op.h
浏览文件 @ 47e5978f
......@@ -25,31 +25,28 @@ namespace operators {
using namespace framework;
template<typename DeviceType, typename T>
class ElementwiseAddOp
: public framework::OperatorWithKernel<DeviceType> {
public:
ElementwiseAddOp(const std::string &type,
const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType>(
type, inputs, outputs, attrs, scope),
param_(inputs, outputs, attrs, *scope) {}
template <typename DeviceType, typename T>
class ElementwiseAddOp : public framework::OperatorWithKernel<DeviceType> {
public:
ElementwiseAddOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType>(type, inputs, outputs,
attrs, scope),
param_(inputs, outputs, attrs, *scope) {}
void Run() const {
operators::ElementwiseAddKernel<DeviceType, T,
ElementwiseAddParam>
kernel;
kernel.Compute(param_);
}
void Run() const {
operators::ElementwiseAddKernel<DeviceType, T, ElementwiseAddParam>
kernel;
kernel.Compute(param_);
}
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override;
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override;
protected:
ElementwiseAddParam param_;
protected:
ElementwiseAddParam param_;
};
}
}
} // namespace operators
} // namespace paddle_mobile
src/operators/kernel/arm/conv_kernel.cpp
浏览文件 @ 47e5978f
......@@ -22,140 +22,131 @@ namespace paddle_mobile {
namespace operators {
bool IsExpand(const std::vector<int64_t> &filter_dim,
const std::vector<int> &strides,
const std::vector<int> &paddings,
const std::vector<int> &strides, const std::vector<int> &paddings,
const std::vector<int> &dilations) {
bool filter_1 = true, strides_1 = true, padding_0 = true,
dilation_1 = true;
for (size_t j = 0; j < strides.size(); ++j) {
filter_1 =
filter_1 && (static_cast<int>(filter_dim[j + 2]) == 1);
strides_1 = strides_1 && (strides[j] == 1);
padding_0 = padding_0 && (paddings[j] == 0);
dilation_1 = dilation_1 && (dilations[j] == 1);
}
return !(filter_1 && strides_1 && padding_0 && dilation_1);
bool filter_1 = true, strides_1 = true, padding_0 = true, dilation_1 = true;
for (size_t j = 0; j < strides.size(); ++j) {
filter_1 = filter_1 && (static_cast<int>(filter_dim[j + 2]) == 1);
strides_1 = strides_1 && (strides[j] == 1);
padding_0 = padding_0 && (paddings[j] == 0);
dilation_1 = dilation_1 && (dilations[j] == 1);
}
return !(filter_1 && strides_1 && padding_0 && dilation_1);
}
template<>
void ConvKernel<CPU, float, ConvParam>::Compute(
const ConvParam &param) const {
LOG(kLOG_DEBUG) << param;
const Tensor *input = param.Input();
// The filter will be reshaped in the calculations,
// so here use an assignment operation,
// that avoids modifying the variable in the Scope.
Tensor filter = *param.Filter();
Tensor *output = param.Output();
// output->mutable_data<T>(context.GetPlace());
int groups = param.Groups();
std::vector<int> strides = param.Strides();
std::vector<int> paddings = param.Paddings();
std::vector<int> dilations = param.Dilations();
DLOG << " compute end get Attrs " << strides[0];
const int batch_size = static_cast<int>(input->dims()[0]);
// filter_shape_vec: {k_o, k_i, k_h, k_w} or {k_o, k_i, k_d, k_h,
// k_w}
std::vector<int64_t> filter_shape_vec(
framework::vectorize(filter.dims()));
// output_shape_vec: {o_n, o_c, o_h, o_w} or {o_n, o_c, o_d, o_h,
// o_w}
std::vector<int64_t> output_shape_vec(
framework::vectorize(output->dims()));
// use col_shape in the im2col calculation
// col_shape_vec: {i_c/g, k_h, k_w, o_h, o_w} or {i_c/g, k_d, k_h,
// k_w, o_d,
// o_h, o_w}
size_t data_dim = filter_shape_vec.size() - 2;
std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
col_shape_vec[0] = input->dims()[1] / groups;
for (size_t j = 0; j < data_dim; ++j) {
col_shape_vec[j + 1] = filter_shape_vec[j + 2];
col_shape_vec[j + 1 + data_dim] = output_shape_vec[j + 2];
}
framework::DDim col_shape(framework::make_ddim(col_shape_vec));
// use col_matrix_shape in the gemm calculation
// size: (i_c/g * k_h * k_w, o_h * o_w) or (i_c/g * k_d * k_h * k_w,
// o_d *
// o_h * o_w)
framework::DDim col_matrix_shape =
framework::flatten_to_2d(col_shape, data_dim + 1);
bool is_expand =
IsExpand(filter_shape_vec, strides, paddings, dilations);
Tensor col;
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
Tensor col_matrix;
if (is_expand) {
col.mutable_data<float>(col_shape);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
}
framework::DDim input_shape = framework::slice_ddim(
input->dims(), 1, static_cast<int>(input->dims().size()));
framework::DDim filter_matrix_shape = {
filter.dims()[0], filter.numel() / filter.dims()[0]};
filter.Resize(filter_matrix_shape);
framework::DDim output_matrix_shape = {
output->dims()[1],
output->numel() / (output->dims()[0] * output->dims()[1])};
// convolution operator: im2col(or vol2col) + gemm
int in_step = static_cast<int>(input->dims()[1]) / groups;
int out_step = static_cast<int>(output->dims()[1]) / groups;
math::Vol2ColFunctor<CPU, float> vol2col;
math::Im2ColFunctor<math::ColFormat::kCFO, CPU, float> im2col;
// auto& dev_ctx = context.template
// device_context<DeviceContext>();
for (int i = 0; i < batch_size; i++) {
Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
Tensor out_batch =
output->Slice(i, i + 1).Resize(output_matrix_shape);
for (int g = 0; g < groups; g++) {
Tensor in_slice =
in_batch.Slice(g * in_step, (g + 1) * in_step);
if (!is_expand) {
col.ShareDataWith(in_slice);
template <>
void ConvKernel<CPU, float, ConvParam>::Compute(const ConvParam &param) const {
LOG(kLOG_DEBUG) << param;
const Tensor *input = param.Input();
// The filter will be reshaped in the calculations,
// so here use an assignment operation,
// that avoids modifying the variable in the Scope.
Tensor filter = *param.Filter();
Tensor *output = param.Output();
// output->mutable_data<T>(context.GetPlace());
int groups = param.Groups();
std::vector<int> strides = param.Strides();
std::vector<int> paddings = param.Paddings();
std::vector<int> dilations = param.Dilations();
DLOG << " compute end get Attrs " << strides[0];
const int batch_size = static_cast<int>(input->dims()[0]);
// filter_shape_vec: {k_o, k_i, k_h, k_w} or {k_o, k_i, k_d, k_h,
// k_w}
std::vector<int64_t> filter_shape_vec(framework::vectorize(filter.dims()));
// output_shape_vec: {o_n, o_c, o_h, o_w} or {o_n, o_c, o_d, o_h,
// o_w}
std::vector<int64_t> output_shape_vec(framework::vectorize(output->dims()));
// use col_shape in the im2col calculation
// col_shape_vec: {i_c/g, k_h, k_w, o_h, o_w} or {i_c/g, k_d, k_h,
// k_w, o_d,
// o_h, o_w}
size_t data_dim = filter_shape_vec.size() - 2;
std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
col_shape_vec[0] = input->dims()[1] / groups;
for (size_t j = 0; j < data_dim; ++j) {
col_shape_vec[j + 1] = filter_shape_vec[j + 2];
col_shape_vec[j + 1 + data_dim] = output_shape_vec[j + 2];
}
framework::DDim col_shape(framework::make_ddim(col_shape_vec));
// use col_matrix_shape in the gemm calculation
// size: (i_c/g * k_h * k_w, o_h * o_w) or (i_c/g * k_d * k_h * k_w,
// o_d *
// o_h * o_w)
framework::DDim col_matrix_shape =
framework::flatten_to_2d(col_shape, data_dim + 1);
bool is_expand = IsExpand(filter_shape_vec, strides, paddings, dilations);
Tensor col;
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
Tensor col_matrix;
if (is_expand) {
col.mutable_data<float>(col_shape);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
} else if (data_dim == 2U) {
// im2col
im2col(in_slice, dilations, strides,
std::vector<int>{paddings[0], paddings[1],
paddings[0], paddings[1]},
&col);
} else if (data_dim == 3U) {
// vol2col
vol2col(in_slice, dilations, strides, paddings, &col);
}
// gemm
Tensor out_slice =
out_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor filter_slice =
filter.Slice(g * out_step, (g + 1) * out_step);
math::matmul<float>(filter_slice, false, col_matrix, false,
float(1.0), &out_slice, float(0.0));
}
}
framework::DDim input_shape = framework::slice_ddim(
input->dims(), 1, static_cast<int>(input->dims().size()));
framework::DDim filter_matrix_shape = {filter.dims()[0],
filter.numel() / filter.dims()[0]};
filter.Resize(filter_matrix_shape);
framework::DDim output_matrix_shape = {
output->dims()[1],
output->numel() / (output->dims()[0] * output->dims()[1])};
// convolution operator: im2col(or vol2col) + gemm
int in_step = static_cast<int>(input->dims()[1]) / groups;
int out_step = static_cast<int>(output->dims()[1]) / groups;
math::Vol2ColFunctor<CPU, float> vol2col;
math::Im2ColFunctor<math::ColFormat::kCFO, CPU, float> im2col;
// auto& dev_ctx = context.template
// device_context<DeviceContext>();
for (int i = 0; i < batch_size; i++) {
Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape);
for (int g = 0; g < groups; g++) {
Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
if (!is_expand) {
col.ShareDataWith(in_slice);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
} else if (data_dim == 2U) {
// im2col
im2col(in_slice, dilations, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&col);
} else if (data_dim == 3U) {
// vol2col
vol2col(in_slice, dilations, strides, paddings, &col);
}
// gemm
Tensor out_slice =
out_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor filter_slice =
filter.Slice(g * out_step, (g + 1) * out_step);
math::matmul<float>(filter_slice, false, col_matrix, false,
float(1.0), &out_slice, float(0.0));
}
}
}
template class ConvKernel<CPU, float, ConvParam>;
......
src/operators/kernel/arm/elementwise_add_kernel.cpp
浏览文件 @ 47e5978f
......@@ -19,23 +19,23 @@ limitations under the License. */
namespace paddle_mobile {
namespace operators {
template<typename T> struct AddFunctor {
inline T operator()(T a, T b) const { return a + b; }
template <typename T> struct AddFunctor {
inline T operator()(T a, T b) const { return a + b; }
};
template<>
template <>
void ElementwiseAddKernel<CPU, float, ElementwiseAddParam>::Compute(
const ElementwiseAddParam &param) const {
const Tensor *input_x = param.InputX();
const Tensor *input_y = param.InputY();
Tensor *Out = param.Out();
Out->mutable_data<float>();
const int axis = param.Axis();
ElementwiseComputeEx<AddFunctor<float>, float>(
input_x, input_y, axis, AddFunctor<float>(), Out);
const Tensor *input_x = param.InputX();
const Tensor *input_y = param.InputY();
Tensor *Out = param.Out();
Out->mutable_data<float>();
const int axis = param.Axis();
ElementwiseComputeEx<AddFunctor<float>, float>(input_x, input_y, axis,
AddFunctor<float>(), Out);
}
template class ElementwiseAddKernel<CPU, float, ElementwiseAddParam>;
} // namespace operators
} // namespace paddle
} // namespace paddle_mobile
src/operators/kernel/arm/mul_kernel.cpp
浏览文件 @ 47e5978f
......@@ -23,34 +23,32 @@ SOFTWARE.
namespace paddle_mobile {
namespace operators {
template<>
void
MulKernel<CPU, float, MulParam>::Compute(const MulParam &param) const {
const Tensor *input_x = param.InputX();
const Tensor *input_y = param.InputY();
Tensor *out = param.Out();
out->mutable_data<float>();
const Tensor x_matrix =
input_x->dims().size() > 2
? framework::ReshapeToMatrix(*input_x, param.XNumColDims())
: *input_x;
const Tensor y_matrix =
input_y->dims().size() > 2
? framework::ReshapeToMatrix(*input_y, param.YNumColDims())
: *input_y;
auto out_dim = out->dims();
if (out_dim.size() != 2) {
out->Resize({x_matrix.dims()[0], y_matrix.dims()[1]});
}
math::matmul<float>(x_matrix, false, y_matrix, false,
static_cast<float>(1), out,
static_cast<float>(0));
if (out_dim.size() != 2) {
out->Resize(out_dim);
}
template <>
void MulKernel<CPU, float, MulParam>::Compute(const MulParam &param) const {
const Tensor *input_x = param.InputX();
const Tensor *input_y = param.InputY();
Tensor *out = param.Out();
out->mutable_data<float>();
const Tensor x_matrix =
input_x->dims().size() > 2
? framework::ReshapeToMatrix(*input_x, param.XNumColDims())
: *input_x;
const Tensor y_matrix =
input_y->dims().size() > 2
? framework::ReshapeToMatrix(*input_y, param.YNumColDims())
: *input_y;
auto out_dim = out->dims();
if (out_dim.size() != 2) {
out->Resize({x_matrix.dims()[0], y_matrix.dims()[1]});
}
math::matmul<float>(x_matrix, false, y_matrix, false, static_cast<float>(1),
out, static_cast<float>(0));
if (out_dim.size() != 2) {
out->Resize(out_dim);
}
}
template class MulKernel<CPU, float, MulParam>;
} // namespace operators
} // namespace paddle
} // namespace paddle_mobile
src/operators/kernel/conv_kernel.h
浏览文件 @ 47e5978f
......@@ -29,11 +29,10 @@ namespace operators {
using namespace framework;
template<typename DeviceType, typename T, typename P>
class ConvKernel
: public framework::OpKernelBase<DeviceType, ConvParam> {
public:
void Compute(const ConvParam &param) const;
template <typename DeviceType, typename T, typename P>
class ConvKernel : public framework::OpKernelBase<DeviceType, ConvParam> {
public:
void Compute(const ConvParam &param) const;
};
}
}
} // namespace operators
} // namespace paddle_mobile
src/operators/kernel/elementwise_add_kernel.h
浏览文件 @ 47e5978f
......@@ -26,11 +26,11 @@ namespace operators {
using namespace framework;
template<typename DeviceType, typename T, typename P>
template <typename DeviceType, typename T, typename P>
class ElementwiseAddKernel
: public framework::OpKernelBase<DeviceType, ElementwiseAddParam> {
public:
void Compute(const ElementwiseAddParam &param) const;
public:
void Compute(const ElementwiseAddParam &param) const;
};
}
}
} // namespace operators
} // namespace paddle_mobile
src/operators/kernel/fpga/conv_kernel.cpp
浏览文件 @ 47e5978f
......@@ -25,4 +25,4 @@ namespace operators {
//
// template class ConvKernel<FPGA, float>;
}
}
} // namespace paddle_mobile
src/operators/kernel/mul_kernel.h
浏览文件 @ 47e5978f
......@@ -26,10 +26,10 @@ namespace operators {
using namespace framework;
template<typename DeviceType, typename T, typename P>
template <typename DeviceType, typename T, typename P>
class MulKernel : public framework::OpKernelBase<DeviceType, MulParam> {
public:
void Compute(const MulParam &param) const;
public:
void Compute(const MulParam &param) const;
};
}
}
} // namespace operators
} // namespace paddle_mobile
src/operators/math/elementwise_op_function.h
浏览文件 @ 47e5978f
......@@ -34,178 +34,174 @@ namespace operators {
inline void get_mid_dims(const framework::DDim &x_dims,
const framework::DDim &y_dims, const int axis,
int *pre, int *n, int *post) {
*pre = 1;
*n = 1;
*post = 1;
// compute pre
for (int i = 0; i < axis; ++i) {
(*pre) *= x_dims[i];
}
for (int i = 0; i < y_dims.size(); ++i) {
assert(x_dims[i + axis] == y_dims[i]);
/// "Broadcast dimension mismatch.");
(*n) *= y_dims[i];
}
for (int i = axis + y_dims.size(); i < x_dims.size(); ++i) {
(*post) *= x_dims[i];
}
*pre = 1;
*n = 1;
*post = 1;
// compute pre
for (int i = 0; i < axis; ++i) {
(*pre) *= x_dims[i];
}
for (int i = 0; i < y_dims.size(); ++i) {
assert(x_dims[i + axis] == y_dims[i]);
/// "Broadcast dimension mismatch.");
(*n) *= y_dims[i];
}
for (int i = axis + y_dims.size(); i < x_dims.size(); ++i) {
(*post) *= x_dims[i];
}
}
/// remove dims tail 1. (4,20,1,1) -> (4,20)
inline void trim_trailing_singular_dims(framework::DDim *dims) {
// Remove trailing dimensions of size 1 for y
auto actual_dims_size = dims->size();
for (; actual_dims_size != 0; --actual_dims_size) {
if ((*dims)[actual_dims_size - 1] != 1)
break;
}
if (actual_dims_size != dims->size()) {
auto actual_dims = framework::vectorize(*dims);
actual_dims.resize(actual_dims_size);
*dims = framework::make_ddim(actual_dims);
}
// Remove trailing dimensions of size 1 for y
auto actual_dims_size = dims->size();
for (; actual_dims_size != 0; --actual_dims_size) {
if ((*dims)[actual_dims_size - 1] != 1)
break;
}
if (actual_dims_size != dims->size()) {
auto actual_dims = framework::vectorize(*dims);
actual_dims.resize(actual_dims_size);
*dims = framework::make_ddim(actual_dims);
}
}
template<typename T> class RowwiseTransformIterator {
public:
RowwiseTransformIterator(const T *ptr, int n)
: ptr_(ptr), i_(0), n_(n) {}
template <typename T> class RowwiseTransformIterator {
public:
RowwiseTransformIterator(const T *ptr, int n) : ptr_(ptr), i_(0), n_(n) {}
RowwiseTransformIterator<T> &operator++() {
++i_;
if (UNLIKELY(i_ == n_)) {
i_ = 0;
RowwiseTransformIterator<T> &operator++() {
++i_;
if (UNLIKELY(i_ == n_)) {
i_ = 0;
}
return *this;
}
return *this;
}
bool operator==(const RowwiseTransformIterator<T> &rhs) const {
return (ptr_ + i_) == &(*rhs);
}
bool operator==(const RowwiseTransformIterator<T> &rhs) const {
return (ptr_ + i_) == &(*rhs);
}
bool operator!=(const RowwiseTransformIterator<T> &rhs) const {
return (ptr_ + i_) != &(*rhs);
}
bool operator!=(const RowwiseTransformIterator<T> &rhs) const {
return (ptr_ + i_) != &(*rhs);
}
const T &operator*() { return ptr_[i_]; }
const T &operator*() { return ptr_[i_]; }
private:
const T *ptr_;
int i_;
int64_t n_;
private:
const T *ptr_;
int i_;
int64_t n_;
};
/// (4,20,2)+(20,): (20,) just as (20,1), when move 2 strides in last
/// dimension
/// in (4,20,2) is 2 ,
/// (20,1) move 1 stride , to fill(add) 2 element with the same number.
template<typename T> class MidWiseTransformIterator {
public:
MidWiseTransformIterator(const T *ptr, int n, int post)
: ptr_(ptr), i_(0), j_(0), n_(n), post_(post) {}
MidWiseTransformIterator<T> &operator++() {
++j_;
if (UNLIKELY(j_ == post_)) {
++i_;
j_ = 0;
if (UNLIKELY(i_ == n_)) {
i_ = 0;
}
}
return *this;
}
bool operator==(const MidWiseTransformIterator<T> &rhs) const {
return (ptr_ + i_) == &(*rhs);
}
bool operator!=(const MidWiseTransformIterator<T> &rhs) const {
return (ptr_ + i_) != &(*rhs);
}
const T &operator*() { return ptr_[i_]; }
private:
const T *ptr_;
int64_t i_;
int64_t j_;
int64_t n_;
int64_t post_;
template <typename T> class MidWiseTransformIterator {
public:
MidWiseTransformIterator(const T *ptr, int n, int post)
: ptr_(ptr), i_(0), j_(0), n_(n), post_(post) {}
MidWiseTransformIterator<T> &operator++() {
++j_;
if (UNLIKELY(j_ == post_)) {
++i_;
j_ = 0;
if (UNLIKELY(i_ == n_)) {
i_ = 0;
}
}
return *this;
}
bool operator==(const MidWiseTransformIterator<T> &rhs) const {
return (ptr_ + i_) == &(*rhs);
}
bool operator!=(const MidWiseTransformIterator<T> &rhs) const {
return (ptr_ + i_) != &(*rhs);
}
const T &operator*() { return ptr_[i_]; }
private:
const T *ptr_;
int64_t i_;
int64_t j_;
int64_t n_;
int64_t post_;
};
template<typename Functor, typename T, typename OutType = T>
template <typename Functor, typename T, typename OutType = T>
class TransformFunctor {
public:
TransformFunctor(const framework::Tensor *x,
const framework::Tensor *y, framework::Tensor *z,
Functor func)
: x_(x->data<T>()), y_(y->data<T>()),
z_(z->mutable_data<OutType>()), nx_(x->numel()), func_(func) {
}
inline void Run() const {
math::Transform trans;
// 同时执行func(x_, y_)传入z_。
trans(x_, x_ + nx_, y_, z_, func_);
}
inline void RunRowWise(int n, int pre) const {
math::Transform trans;
trans(x_, x_ + nx_, RowwiseTransformIterator<T>(y_, n), z_,
func_);
}
inline void RunMidWise(int n, int pre, int post) const {
math::Transform trans;
trans(x_, x_ + nx_, MidWiseTransformIterator<T>(y_, n, post),
z_, func_);
}
private:
const T *x_;
const T *y_;
OutType *z_;
int64_t nx_;
Functor func_;
public:
TransformFunctor(const framework::Tensor *x, const framework::Tensor *y,
framework::Tensor *z, Functor func)
: x_(x->data<T>()), y_(y->data<T>()), z_(z->mutable_data<OutType>()),
nx_(x->numel()), func_(func) {}
inline void Run() const {
math::Transform trans;
// 同时执行func(x_, y_)传入z_。
trans(x_, x_ + nx_, y_, z_, func_);
}
inline void RunRowWise(int n, int pre) const {
math::Transform trans;
trans(x_, x_ + nx_, RowwiseTransformIterator<T>(y_, n), z_, func_);
}
inline void RunMidWise(int n, int pre, int post) const {
math::Transform trans;
trans(x_, x_ + nx_, MidWiseTransformIterator<T>(y_, n, post), z_,
func_);
}
private:
const T *x_;
const T *y_;
OutType *z_;
int64_t nx_;
Functor func_;
};
template<typename Functor, typename T, typename OutType = T>
template <typename Functor, typename T, typename OutType = T>
void ElementwiseComputeEx(const framework::Tensor *x,
const framework::Tensor *y, int axis,
Functor func, framework::Tensor *z) {
TransformFunctor<Functor, T, OutType> functor(x, y, z, func);
auto x_dims = x->dims();
auto y_dims = y->dims();
// PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
// "Rank of first input must >= rank of second
// input.");
if (x_dims == y_dims) {
functor.Run();
return;
}
/// axis = -1 represent the last dimension.
axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
// PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
// "Axis should be in range [0, x_dims)");
trim_trailing_singular_dims(&y_dims);
axis = (y_dims.size() == 0) ? x_dims.size() : axis;
int pre, n, post;
get_mid_dims(x_dims, y_dims, axis, &pre, &n, &post);
if (post == 1) {
functor.RunRowWise(n, pre);
return;
} else {
functor.RunMidWise(n, pre, post);
return;
}
const framework::Tensor *y, int axis, Functor func,
framework::Tensor *z) {
TransformFunctor<Functor, T, OutType> functor(x, y, z, func);
auto x_dims = x->dims();
auto y_dims = y->dims();
// PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
// "Rank of first input must >= rank of second
// input.");
if (x_dims == y_dims) {
functor.Run();
return;
}
/// axis = -1 represent the last dimension.
axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
// PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
// "Axis should be in range [0, x_dims)");
trim_trailing_singular_dims(&y_dims);
axis = (y_dims.size() == 0) ? x_dims.size() : axis;
int pre, n, post;
get_mid_dims(x_dims, y_dims, axis, &pre, &n, &post);
if (post == 1) {
functor.RunRowWise(n, pre);
return;
} else {
functor.RunMidWise(n, pre, post);
return;
}
}
} // namespace operators
} // namespace paddle
} // namespace paddle_mobile
src/operators/math/im2col.cc
浏览文件 @ 47e5978f
......@@ -25,76 +25,71 @@ namespace math {
* [input_channels, filter_height, filter_width, output_height,
* output_width]
*/
template<class T> class Im2ColFunctor<ColFormat::kCFO, CPU, T> {
public:
void operator()(const framework::Tensor &im,
const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding,
framework::Tensor *col) {
// PADDLE_ENFORCE(im.dims().size() == 3);
// PADDLE_ENFORCE(col->dims().size() == 5);
template <class T> class Im2ColFunctor<ColFormat::kCFO, CPU, T> {
public:
void operator()(const framework::Tensor &im,
const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding, framework::Tensor *col) {
// PADDLE_ENFORCE(im.dims().size() == 3);
// PADDLE_ENFORCE(col->dims().size() == 5);
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int col_height = col->dims()[3];
int col_width = col->dims()[4];
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int col_height = col->dims()[3];
int col_width = col->dims()[4];
// PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2]
// -
// ((dilation[0] * (filter_height - 1)
// + 1))) /
// stride[0] +
// 1,
// col_height,
// "Output_height and
// padding(padding_up, padding_down)
// are " "inconsistent.");
// PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3]
// -
// ((dilation[1] * (filter_width - 1)
// + 1))) /
// stride[1] +
// 1,
// col_width,
// "Output_height and
// padding(padding_up, padding_down)
// are " "inconsistent.");
// PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2]
// -
// ((dilation[0] * (filter_height - 1)
// + 1))) /
// stride[0] +
// 1,
// col_height,
// "Output_height and
// padding(padding_up, padding_down)
// are " "inconsistent.");
// PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3]
// -
// ((dilation[1] * (filter_width - 1)
// + 1))) /
// stride[1] +
// 1,
// col_width,
// "Output_height and
// padding(padding_up, padding_down)
// are " "inconsistent.");
int channels_col =
im_channels * filter_height * filter_width;
int channels_col = im_channels * filter_height * filter_width;
const T *im_data = im.data<T>();
T *col_data = col->data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < col_height; ++h) {
int im_row_idx = h * stride[0] - padding[0] +
h_offset * dilation[0];
for (int w = 0; w < col_width; ++w) {
int im_col_idx = w * stride[1] - padding[1] +
w_offset * dilation[1];
int col_idx =
(c * col_height + h) * col_width + w;
int im_idx =
(im_row_idx + c_im * im_height) * im_width +
im_col_idx;
const T *im_data = im.data<T>();
T *col_data = col->data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < col_height; ++h) {
int im_row_idx =
h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < col_width; ++w) {
int im_col_idx =
w * stride[1] - padding[1] + w_offset * dilation[1];
int col_idx = (c * col_height + h) * col_width + w;
int im_idx =
(im_row_idx + c_im * im_height) * im_width + im_col_idx;
col_data[col_idx] =
(im_row_idx < 0 ||
im_row_idx >= im_height ||
im_col_idx < 0 || im_col_idx >= im_width)
? static_cast<T>(0)
: im_data[im_idx];
col_data[col_idx] =
(im_row_idx < 0 || im_row_idx >= im_height ||
im_col_idx < 0 || im_col_idx >= im_width)
? static_cast<T>(0)
: im_data[im_idx];
}
}
}
}
}
}
};
/*
......@@ -103,75 +98,68 @@ public:
* [input_channels, filter_height, filter_width, output_height,
* output_width]
*/
template<class T> class Col2ImFunctor<ColFormat::kCFO, CPU, T> {
public:
void operator()(const framework::Tensor &col,
const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding,
framework::Tensor *im) {
// PADDLE_ENFORCE(im->dims().size() == 3);
// PADDLE_ENFORCE(col.dims().size() == 5);
int im_channels = im->dims()[0];
int im_height = im->dims()[1];
int im_width = im->dims()[2];
int filter_height = col.dims()[1];
int filter_width = col.dims()[2];
int col_height = col.dims()[3];
int col_width = col.dims()[4];
template <class T> class Col2ImFunctor<ColFormat::kCFO, CPU, T> {
public:
void operator()(const framework::Tensor &col,
const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding, framework::Tensor *im) {
// PADDLE_ENFORCE(im->dims().size() == 3);
// PADDLE_ENFORCE(col.dims().size() == 5);
int im_channels = im->dims()[0];
int im_height = im->dims()[1];
int im_width = im->dims()[2];
int filter_height = col.dims()[1];
int filter_width = col.dims()[2];
int col_height = col.dims()[3];
int col_width = col.dims()[4];
// PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2]
// -
// ((dilation[0] * (filter_height - 1)
// + 1))) /
// stride[0] +
// 1,
// col_height,
// "Output_height and
// padding(padding_up, padding_down)
// are " "inconsistent.");
// PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3]
// -
// ((dilation[1] * (filter_width - 1)
// + 1))) /
// stride[1] +
// 1,
// col_width,
// "Output_height and
// padding(padding_up, padding_down)
// are " "inconsistent.");
// PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2]
// -
// ((dilation[0] * (filter_height - 1)
// + 1))) /
// stride[0] +
// 1,
// col_height,
// "Output_height and
// padding(padding_up, padding_down)
// are " "inconsistent.");
// PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3]
// -
// ((dilation[1] * (filter_width - 1)
// + 1))) /
// stride[1] +
// 1,
// col_width,
// "Output_height and
// padding(padding_up, padding_down)
// are " "inconsistent.");
int channels_col =
im_channels * filter_height * filter_width;
int channels_col = im_channels * filter_height * filter_width;
T *im_data = im->data<T>();
const T *col_data = col.data<T>();
T *im_data = im->data<T>();
const T *col_data = col.data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < col_height; ++h) {
int im_row_idx = h * stride[0] - padding[0] +
h_offset * dilation[0];
for (int w = 0; w < col_width; ++w) {
int im_col_idx = w * stride[1] - padding[1] +
w_offset * dilation[1];
if ((im_row_idx) >= 0 &&
(im_row_idx) < im_height &&
(im_col_idx) >= 0 &&
(im_col_idx) < im_width) {
im_data[(im_row_idx + c_im * im_height) *
im_width +
im_col_idx] +=
col_data[(c * col_height + h) *
col_width +
w];
}
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < col_height; ++h) {
int im_row_idx =
h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < col_width; ++w) {
int im_col_idx =
w * stride[1] - padding[1] + w_offset * dilation[1];
if ((im_row_idx) >= 0 && (im_row_idx) < im_height &&
(im_col_idx) >= 0 && (im_col_idx) < im_width) {
im_data[(im_row_idx + c_im * im_height) * im_width +
im_col_idx] +=
col_data[(c * col_height + h) * col_width + w];
}
}
}
}
}
}
}
};
template class Im2ColFunctor<ColFormat::kCFO, CPU, float>;
......@@ -185,85 +173,75 @@ template class Col2ImFunctor<ColFormat::kCFO, CPU, double>;
* [output_height, output_width, input_channels, filter_height,
* filter_width]
*/
template<class T> class Im2ColFunctor<ColFormat::kOCF, CPU, T> {
public:
void operator()(const framework::Tensor &im,
const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding,
framework::Tensor *col) {
// PADDLE_ENFORCE(im.dims().size() == 3);
// PADDLE_ENFORCE(col->dims().size() == 5);
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[3];
int filter_width = col->dims()[4];
int col_height = col->dims()[0];
int col_width = col->dims()[1];
template <class T> class Im2ColFunctor<ColFormat::kOCF, CPU, T> {
public:
void operator()(const framework::Tensor &im,
const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding, framework::Tensor *col) {
// PADDLE_ENFORCE(im.dims().size() == 3);
// PADDLE_ENFORCE(col->dims().size() == 5);
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[3];
int filter_width = col->dims()[4];
int col_height = col->dims()[0];
int col_width = col->dims()[1];
// PADDLE_ENFORCE_EQ(
// (im_height + padding[0] + padding[2] -
// filter_height) / stride[0]
// + 1, col_height, "Output_height and
// padding(padding_up,
// padding_down) are " "inconsistent.");
// PADDLE_ENFORCE_EQ(
// (im_width + padding[1] + padding[3] -
// filter_width) / stride[1] +
// 1, col_width, "col_width and padding(padding_left,
// padding_right)
// are " "inconsistent.");
// PADDLE_ENFORCE_EQ(
// (im_height + padding[0] + padding[2] -
// filter_height) / stride[0]
// + 1, col_height, "Output_height and
// padding(padding_up,
// padding_down) are " "inconsistent.");
// PADDLE_ENFORCE_EQ(
// (im_width + padding[1] + padding[3] -
// filter_width) / stride[1] +
// 1, col_width, "col_width and padding(padding_left,
// padding_right)
// are " "inconsistent.");
const T *im_data = im.data<T>();
T *col_data = col->data<T>();
const T *im_data = im.data<T>();
T *col_data = col->data<T>();
for (int col_row_idx = 0; col_row_idx < col_height;
++col_row_idx) {
for (int col_col_idx = 0; col_col_idx < col_width;
++col_col_idx) {
for (int channel = 0; channel < im_channels;
++channel) {
for (int filter_row_idx = 0;
filter_row_idx < filter_height;
++filter_row_idx) {
int im_row_offset =
col_row_idx * stride[0] +
filter_row_idx - padding[0];
for (int filter_col_idx = 0;
filter_col_idx < filter_width;
++filter_col_idx) {
int im_col_offset =
col_col_idx * stride[1] +
filter_col_idx - padding[1];
for (int col_row_idx = 0; col_row_idx < col_height; ++col_row_idx) {
for (int col_col_idx = 0; col_col_idx < col_width; ++col_col_idx) {
for (int channel = 0; channel < im_channels; ++channel) {
for (int filter_row_idx = 0; filter_row_idx < filter_height;
++filter_row_idx) {
int im_row_offset = col_row_idx * stride[0] +
filter_row_idx - padding[0];
for (int filter_col_idx = 0;
filter_col_idx < filter_width; ++filter_col_idx) {
int im_col_offset = col_col_idx * stride[1] +
filter_col_idx - padding[1];
int col_offset =
((((col_row_idx) * col_width +
col_col_idx) *
im_channels +
channel) *
filter_height +
filter_row_idx) *
filter_width +
filter_col_idx;
int col_offset =
((((col_row_idx)*col_width + col_col_idx) *
im_channels +
channel) *
filter_height +
filter_row_idx) *
filter_width +
filter_col_idx;
int im_offset = (channel * im_height +
im_row_offset) *
im_width +
im_col_offset;
col_data[col_offset] =
(im_row_offset < 0 ||
im_row_offset >= im_height ||
im_col_offset < 0 ||
im_col_offset >= im_width)
? static_cast<T>(0)
: im_data[im_offset];
int im_offset =
(channel * im_height + im_row_offset) *
im_width +
im_col_offset;
col_data[col_offset] =
(im_row_offset < 0 ||
im_row_offset >= im_height ||
im_col_offset < 0 || im_col_offset >= im_width)
? static_cast<T>(0)
: im_data[im_offset];
}
}
}
}
}
}
}
}
}
};
/*
......@@ -272,86 +250,75 @@ public:
* [output_height, output_width, input_channels, filter_height,
* filter_width]
*/
template<class T> class Col2ImFunctor<ColFormat::kOCF, CPU, T> {
public:
void operator()(const framework::Tensor &col,
const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding,
framework::Tensor *im) {
// PADDLE_ENFORCE(im->dims().size() == 3);
// PADDLE_ENFORCE(col.dims().size() == 5);
int im_channels = im->dims()[0];
int im_height = im->dims()[1];
int im_width = im->dims()[2];
int filter_height = col.dims()[3];
int filter_width = col.dims()[4];
int col_height = col.dims()[0];
int col_width = col.dims()[1];
template <class T> class Col2ImFunctor<ColFormat::kOCF, CPU, T> {
public:
void operator()(const framework::Tensor &col,
const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding, framework::Tensor *im) {
// PADDLE_ENFORCE(im->dims().size() == 3);
// PADDLE_ENFORCE(col.dims().size() == 5);
int im_channels = im->dims()[0];
int im_height = im->dims()[1];
int im_width = im->dims()[2];
int filter_height = col.dims()[3];
int filter_width = col.dims()[4];
int col_height = col.dims()[0];
int col_width = col.dims()[1];
// PADDLE_ENFORCE_EQ(
// (im_height + padding[0] + padding[2] -
// filter_height) / stride[0]
// + 1, col_height, "Output_height and
// padding(padding_up,
// padding_down) are " "inconsistent.");
// PADDLE_ENFORCE_EQ(
// (im_width + padding[1] + padding[3] -
// filter_width) / stride[1] +
// 1, col_width, "col_width and padding(padding_left,
// padding_right)
// are " "inconsistent.");
// PADDLE_ENFORCE_EQ(
// (im_height + padding[0] + padding[2] -
// filter_height) / stride[0]
// + 1, col_height, "Output_height and
// padding(padding_up,
// padding_down) are " "inconsistent.");
// PADDLE_ENFORCE_EQ(
// (im_width + padding[1] + padding[3] -
// filter_width) / stride[1] +
// 1, col_width, "col_width and padding(padding_left,
// padding_right)
// are " "inconsistent.");
T *im_data = im->data<T>();
const T *col_data = col.data<T>();
T *im_data = im->data<T>();
const T *col_data = col.data<T>();
for (int col_row_idx = 0; col_row_idx < col_height;
++col_row_idx) {
for (int col_col_idx = 0; col_col_idx < col_width;
++col_col_idx) {
for (int channel = 0; channel < im_channels;
++channel) {
for (int filter_row_idx = 0;
filter_row_idx < filter_height;
++filter_row_idx) {
int im_row_offset =
col_row_idx * stride[0] +
filter_row_idx - padding[0];
for (int filter_col_idx = 0;
filter_col_idx < filter_width;
++filter_col_idx) {
int im_col_offset =
col_col_idx * stride[1] +
filter_col_idx - padding[1];
for (int col_row_idx = 0; col_row_idx < col_height; ++col_row_idx) {
for (int col_col_idx = 0; col_col_idx < col_width; ++col_col_idx) {
for (int channel = 0; channel < im_channels; ++channel) {
for (int filter_row_idx = 0; filter_row_idx < filter_height;
++filter_row_idx) {
int im_row_offset = col_row_idx * stride[0] +
filter_row_idx - padding[0];
for (int filter_col_idx = 0;
filter_col_idx < filter_width; ++filter_col_idx) {
int im_col_offset = col_col_idx * stride[1] +
filter_col_idx - padding[1];
int col_offset =
(((col_row_idx * col_width +
col_col_idx) *
im_channels +
channel) *
filter_height +
filter_row_idx) *
filter_width +
filter_col_idx;
int col_offset =
(((col_row_idx * col_width + col_col_idx) *
im_channels +
channel) *
filter_height +
filter_row_idx) *
filter_width +
filter_col_idx;
if (im_row_offset >= 0 &&
im_row_offset < im_height &&
im_col_offset >= 0 &&
im_col_offset < im_width) {
int im_offset =
(channel * im_height +
im_row_offset) *
im_width +
im_col_offset;
im_data[im_offset] +=
col_data[col_offset];
}
if (im_row_offset >= 0 &&
im_row_offset < im_height &&
im_col_offset >= 0 &&
im_col_offset < im_width) {
int im_offset =
(channel * im_height + im_row_offset) *
im_width +
im_col_offset;
im_data[im_offset] += col_data[col_offset];
}
}
}
}
}
}
}
}
}
}
};
template class Im2ColFunctor<ColFormat::kOCF, CPU, float>;
......@@ -360,5 +327,5 @@ template class Col2ImFunctor<ColFormat::kOCF, CPU, float>;
template class Col2ImFunctor<ColFormat::kOCF, CPU, double>;
} // namespace math
} // namespace operators
} // namespace operators
} // namespace paddle_mobile
src/operators/math/im2col.h
浏览文件 @ 47e5978f
......@@ -87,26 +87,24 @@ enum class ColFormat { kCFO = 0, kOCF = 1 };
* equal to
* colShape.inputChannels.
*/
template<ColFormat Format, typename DeviceType, typename T>
template <ColFormat Format, typename DeviceType, typename T>
class Im2ColFunctor {
public:
void operator()(const framework::Tensor &im,
const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding,
framework::Tensor *col);
public:
void operator()(const framework::Tensor &im,
const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding, framework::Tensor *col);
};
template<ColFormat Format, typename DeviceType, typename T>
template <ColFormat Format, typename DeviceType, typename T>
class Col2ImFunctor {
public:
void operator()(const framework::Tensor &col,
const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding,
framework::Tensor *im);
public:
void operator()(const framework::Tensor &col,
const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding, framework::Tensor *im);
};
} // namespace math
} // namespace operators
} // namespace operators
} // namespace paddle_mobile
src/operators/math/math_function.cc
浏览文件 @ 47e5978f
......@@ -18,122 +18,107 @@ namespace paddle_mobile {
namespace operators {
namespace math {
template<>
void gemm<float>(const CBLAS_TRANSPOSE transA,
const CBLAS_TRANSPOSE transB, const int M,
const int N, const int K, const float alpha,
const float *A, const float *B, const float beta,
float *C) {
int lda = (transA == CblasNoTrans) ? K : M;
int ldb = (transB == CblasNoTrans) ? N : K;
int ldc = N;
cblas_sgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A,
lda, B, ldb, beta, C, ldc);
template <>
void gemm<float>(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB,
const int M, const int N, const int K, const float alpha,
const float *A, const float *B, const float beta, float *C) {
int lda = (transA == CblasNoTrans) ? K : M;
int ldb = (transB == CblasNoTrans) ? N : K;
int ldc = N;
cblas_sgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
beta, C, ldc);
}
template<>
void gemm<double>(const CBLAS_TRANSPOSE transA,
const CBLAS_TRANSPOSE transB, const int M,
const int N, const int K, const double alpha,
const double *A, const double *B,
const double beta, double *C) {
int lda = (transA == CblasNoTrans) ? K : M;
int ldb = (transB == CblasNoTrans) ? N : K;
int ldc = N;
cblas_dgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A,
lda, B, ldb, beta, C, ldc);
template <>
void gemm<double>(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB,
const int M, const int N, const int K, const double alpha,
const double *A, const double *B, const double beta,
double *C) {
int lda = (transA == CblasNoTrans) ? K : M;
int ldb = (transB == CblasNoTrans) ? N : K;
int ldc = N;
cblas_dgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
beta, C, ldc);
}
template<>
void gemm<float>(const bool transA, const bool transB, const int M,
const int N, const int K, const float alpha,
const float *A, const int lda, const float *B,
const int ldb, const float beta, float *C,
template <>
void gemm<float>(const bool transA, const bool transB, const int M, const int N,
const int K, const float alpha, const float *A, const int lda,
const float *B, const int ldb, const float beta, float *C,
const int ldc) {
cblas_sgemm(CblasRowMajor,
transA == false ? CblasNoTrans : CblasTrans,
transB == false ? CblasNoTrans : CblasTrans, M, N,
K, alpha, A, lda, B, ldb, beta, C, ldc);
cblas_sgemm(CblasRowMajor, transA == false ? CblasNoTrans : CblasTrans,
transB == false ? CblasNoTrans : CblasTrans, M, N, K, alpha, A,
lda, B, ldb, beta, C, ldc);
}
template<>
template <>
void gemm<double>(const bool transA, const bool transB, const int M,
const int N, const int K, const double alpha,
const double *A, const int lda, const double *B,
const int ldb, const double beta, double *C,
const int ldc) {
cblas_dgemm(CblasRowMajor,
transA == false ? CblasNoTrans : CblasTrans,
transB == false ? CblasNoTrans : CblasTrans, M, N,
K, alpha, A, lda, B, ldb, beta, C, ldc);
const int N, const int K, const double alpha, const double *A,
const int lda, const double *B, const int ldb,
const double beta, double *C, const int ldc) {
cblas_dgemm(CblasRowMajor, transA == false ? CblasNoTrans : CblasTrans,
transB == false ? CblasNoTrans : CblasTrans, M, N, K, alpha, A,
lda, B, ldb, beta, C, ldc);
}
template<>
template <>
void matmul<float>(const framework::Tensor &matrix_a, bool trans_a,
const framework::Tensor &matrix_b, bool trans_b,
float alpha, framework::Tensor *matrix_out,
float beta) {
auto dim_a = matrix_a.dims();
auto dim_b = matrix_b.dims();
auto dim_out = matrix_out->dims();
// PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 &&
// dim_out.size() ==
// 2,
// "The input and output of matmul be matrix");
//
// PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) &&
// platform::is_cpu_place(matrix_b.place())
// &&
// platform::is_cpu_place(matrix_out->place()),
// "Matrix must all be in CPUPlace");
int M = dim_out[0];
int N = dim_out[1];
int K = (trans_a == false) ? dim_a[1] : dim_a[0];
CBLAS_TRANSPOSE transA =
(trans_a == false) ? CblasNoTrans : CblasTrans;
CBLAS_TRANSPOSE transB =
(trans_b == false) ? CblasNoTrans : CblasTrans;
gemm<float>(transA, transB, M, N, K, alpha,
matrix_a.data<float>(), matrix_b.data<float>(),
beta, matrix_out->data<float>());
const framework::Tensor &matrix_b, bool trans_b, float alpha,
framework::Tensor *matrix_out, float beta) {
auto dim_a = matrix_a.dims();
auto dim_b = matrix_b.dims();
auto dim_out = matrix_out->dims();
// PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 &&
// dim_out.size() ==
// 2,
// "The input and output of matmul be matrix");
//
// PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) &&
// platform::is_cpu_place(matrix_b.place())
// &&
// platform::is_cpu_place(matrix_out->place()),
// "Matrix must all be in CPUPlace");
int M = dim_out[0];
int N = dim_out[1];
int K = (trans_a == false) ? dim_a[1] : dim_a[0];
CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
gemm<float>(transA, transB, M, N, K, alpha, matrix_a.data<float>(),
matrix_b.data<float>(), beta, matrix_out->data<float>());
}
template<>
template <>
void matmul<double>(const framework::Tensor &matrix_a, bool trans_a,
const framework::Tensor &matrix_b, bool trans_b,
double alpha, framework::Tensor *matrix_out,
double beta) {
auto dim_a = matrix_a.dims();
auto dim_b = matrix_b.dims();
auto dim_out = matrix_out->dims();
// PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 &&
// dim_out.size() ==
// 2,
// "The input and output of matmul be matrix");
//
// PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) &&
// platform::is_cpu_place(matrix_b.place())
// &&
// platform::is_cpu_place(matrix_out->place()),
// "Matrix must all be in CPUPlace");
int M = dim_out[0];
int N = dim_out[1];
int K = (trans_a == false) ? dim_a[1] : dim_a[0];
CBLAS_TRANSPOSE transA =
(trans_a == false) ? CblasNoTrans : CblasTrans;
CBLAS_TRANSPOSE transB =
(trans_b == false) ? CblasNoTrans : CblasTrans;
gemm<double>(transA, transB, M, N, K, alpha,
matrix_a.data<double>(), matrix_b.data<double>(),
beta, matrix_out->data<double>());
double alpha, framework::Tensor *matrix_out, double beta) {
auto dim_a = matrix_a.dims();
auto dim_b = matrix_b.dims();
auto dim_out = matrix_out->dims();
// PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 &&
// dim_out.size() ==
// 2,
// "The input and output of matmul be matrix");
//
// PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) &&
// platform::is_cpu_place(matrix_b.place())
// &&
// platform::is_cpu_place(matrix_out->place()),
// "Matrix must all be in CPUPlace");
int M = dim_out[0];
int N = dim_out[1];
int K = (trans_a == false) ? dim_a[1] : dim_a[0];
CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
gemm<double>(transA, transB, M, N, K, alpha, matrix_a.data<double>(),
matrix_b.data<double>(), beta, matrix_out->data<double>());
}
} // namespace math
} // namespace operators
} // namespace operators
} // namespace paddle_mobile
src/operators/math/math_function.h
浏览文件 @ 47e5978f
......@@ -22,23 +22,21 @@ namespace paddle_mobile {
namespace operators {
namespace math {
template<typename T>
void gemm(const CBLAS_TRANSPOSE transA,
const CBLAS_TRANSPOSE transB, const int M, const int N,
const int K, const T alpha, const T *A, const T *B,
const T beta, T *C);
template<typename T>
void gemm(const bool transA, const bool transB, const int M,
const int N, const int K, const T alpha, const T *A,
const int lda, const T *B, const int ldb, const T beta,
T *C, const int ldc);
template <typename T>
void gemm(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB,
const int M, const int N, const int K, const T alpha, const T *A,
const T *B, const T beta, T *C);
template <typename T>
void gemm(const bool transA, const bool transB, const int M, const int N,
const int K, const T alpha, const T *A, const int lda, const T *B,
const int ldb, const T beta, T *C, const int ldc);
// matrix multiply with continuous memory
template<typename T>
template <typename T>
void matmul(const framework::Tensor &matrix_a, bool trans_a,
const framework::Tensor &matrix_b, bool trans_b,
T alpha, framework::Tensor *matrix_out, T beta);
const framework::Tensor &matrix_b, bool trans_b, T alpha,
framework::Tensor *matrix_out, T beta);
} // namespace math
} // namespace operators
} // namespace operators
} // namespace paddle_mobile
src/operators/math/transform.h
浏览文件 @ 47e5978f
......@@ -37,21 +37,19 @@ namespace math {
// class, paddle::fluid::operators::RowwiseTRansformIterator.
struct Transform {
template<typename InputIter, typename OutputIter,
typename UnaryOperation>
void operator()(InputIter first, InputIter last,
OutputIter result, UnaryOperation op) {
std::transform(first, last, result, op);
}
template<typename InputIter1, typename InputIter2,
typename OutputIter, typename BinaryOperation>
void operator()(InputIter1 first1, InputIter1 last1,
InputIter2 first2, OutputIter result,
BinaryOperation op) {
std::transform(first1, last1, first2, result, op);
}
template <typename InputIter, typename OutputIter, typename UnaryOperation>
void operator()(InputIter first, InputIter last, OutputIter result,
UnaryOperation op) {
std::transform(first, last, result, op);
}
template <typename InputIter1, typename InputIter2, typename OutputIter,
typename BinaryOperation>
void operator()(InputIter1 first1, InputIter1 last1, InputIter2 first2,
OutputIter result, BinaryOperation op) {
std::transform(first1, last1, first2, result, op);
}
};
}
} // namespace platform
} // namespace paddle
} // namespace math
} // namespace operators
} // namespace paddle_mobile
src/operators/math/vol2col.cc
浏览文件 @ 47e5978f
......@@ -25,97 +25,91 @@ using Tensor = paddle_mobile::framework::Tensor;
* [input_channels, filter_depth, filter_height, filter_width,
* output_depth, output_height, output_width]
*/
template<typename T> class Vol2ColFunctor<CPU, T> {
public:
void operator()(const Tensor &vol,
const std::vector<int> &dilations,
const std::vector<int> &strides,
const std::vector<int> &paddings,
Tensor *col) const {
// PADDLE_ENFORCE(vol.dims().size() == 4);
// PADDLE_ENFORCE(col->dims().size() == 7);
int input_channels = vol.dims()[0];
int input_depth = vol.dims()[1];
int input_height = vol.dims()[2];
int input_width = vol.dims()[3];
int filter_depth = col->dims()[1];
int filter_height = col->dims()[2];
int filter_width = col->dims()[3];
int output_depth = col->dims()[4];
int output_height = col->dims()[5];
int output_width = col->dims()[6];
int channels_col = input_channels * filter_depth *
filter_height * filter_width;
// PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] -
// ((dilations[0] * (filter_depth - 1)
// + 1))) /
// strides[0] +
// 1,
// output_depth,
// "input_depth and output_depth are "
// "mismatching.");
// PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] -
// ((dilations[1] * (filter_height -
// 1) + 1))) /
// strides[1] +
// 1,
// output_height,
// "input_height and output_height are
// "
// "mismatching.");
// PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] -
// ((dilations[2] * (filter_width - 1)
// + 1))) /
// strides[2] +
// 1,
// output_width,
// "input_width and output_width are "
// "mismatching.");
const T *vol_data = vol.data<T>();
T *col_data = col->data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int d_offset =
(c / filter_width / filter_height) % filter_depth;
int c_in =
c / filter_width / filter_height / filter_depth;
for (int d = 0; d < output_depth; ++d) {
int d_pad = d * strides[0] - paddings[0] +
d_offset * dilations[0];
for (int h = 0; h < output_height; ++h) {
int h_pad = h * strides[1] - paddings[1] +
h_offset * dilations[1];
for (int w = 0; w < output_width; ++w) {
int w_pad = w * strides[2] - paddings[2] +
w_offset * dilations[2];
int col_idx = ((c * output_depth + d) *
output_height +
h) *
output_width +
w;
int vol_idx =
((c_in * input_depth + d_pad) *
input_height +
h_pad) *
input_width +
w_pad;
col_data[col_idx] =
(h_pad < 0 || h_pad >= input_height ||
w_pad < 0 || w_pad >= input_width ||
d_pad < 0 || d_pad >= input_depth)
? static_cast<T>(0)
: vol_data[vol_idx];
}
template <typename T> class Vol2ColFunctor<CPU, T> {
public:
void operator()(const Tensor &vol, const std::vector<int> &dilations,
const std::vector<int> &strides,
const std::vector<int> &paddings, Tensor *col) const {
// PADDLE_ENFORCE(vol.dims().size() == 4);
// PADDLE_ENFORCE(col->dims().size() == 7);
int input_channels = vol.dims()[0];
int input_depth = vol.dims()[1];
int input_height = vol.dims()[2];
int input_width = vol.dims()[3];
int filter_depth = col->dims()[1];
int filter_height = col->dims()[2];
int filter_width = col->dims()[3];
int output_depth = col->dims()[4];
int output_height = col->dims()[5];
int output_width = col->dims()[6];
int channels_col =
input_channels * filter_depth * filter_height * filter_width;
// PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] -
// ((dilations[0] * (filter_depth - 1)
// + 1))) /
// strides[0] +
// 1,
// output_depth,
// "input_depth and output_depth are "
// "mismatching.");
// PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] -
// ((dilations[1] * (filter_height -
// 1) + 1))) /
// strides[1] +
// 1,
// output_height,
// "input_height and output_height are
// "
// "mismatching.");
// PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] -
// ((dilations[2] * (filter_width - 1)
// + 1))) /
// strides[2] +
// 1,
// output_width,
// "input_width and output_width are "
// "mismatching.");
const T *vol_data = vol.data<T>();
T *col_data = col->data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int d_offset = (c / filter_width / filter_height) % filter_depth;
int c_in = c / filter_width / filter_height / filter_depth;
for (int d = 0; d < output_depth; ++d) {
int d_pad =
d * strides[0] - paddings[0] + d_offset * dilations[0];
for (int h = 0; h < output_height; ++h) {
int h_pad =
h * strides[1] - paddings[1] + h_offset * dilations[1];
for (int w = 0; w < output_width; ++w) {
int w_pad = w * strides[2] - paddings[2] +
w_offset * dilations[2];
int col_idx =
((c * output_depth + d) * output_height + h) *
output_width +
w;
int vol_idx =
((c_in * input_depth + d_pad) * input_height +
h_pad) *
input_width +
w_pad;
col_data[col_idx] =
(h_pad < 0 || h_pad >= input_height || w_pad < 0 ||
w_pad >= input_width || d_pad < 0 ||
d_pad >= input_depth)
? static_cast<T>(0)
: vol_data[vol_idx];
}
}
}
}
}
}
}
};
/*
......@@ -124,96 +118,90 @@ public:
* [input_channels, filter_depth, filter_height, filter_width,
* output_depth, output_height, output_width]
*/
template<typename T> class Col2VolFunctor<CPU, T> {
public:
void operator()(const Tensor &col,
const std::vector<int> &dilations,
const std::vector<int> &strides,
const std::vector<int> &paddings,
Tensor *vol) const {
// PADDLE_ENFORCE(vol->dims().size() == 4);
// PADDLE_ENFORCE(col.dims().size() == 7);
int input_channels = vol->dims()[0];
int input_depth = vol->dims()[1];
int input_height = vol->dims()[2];
int input_width = vol->dims()[3];
int filter_depth = col.dims()[1];
int filter_height = col.dims()[2];
int filter_width = col.dims()[3];
int output_depth = col.dims()[4];
int output_height = col.dims()[5];
int output_width = col.dims()[6];
int channels_col = input_channels * filter_depth *
filter_height * filter_width;
// PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] -
// ((dilations[0] * (filter_depth - 1)
// + 1))) /
// strides[0] +
// 1,
// output_depth,
// "input_depth and output_depth are "
// "mismatching.");
// PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] -
// ((dilations[1] * (filter_height -
// 1) + 1))) /
// strides[1] +
// 1,
// output_height,
// "input_height and output_height are
// "
// "mismatching.");
// PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] -
// ((dilations[2] * (filter_width - 1)
// + 1))) /
// strides[2] +
// 1,
// output_width,
// "input_width and output_width are "
// "mismatching.");
T *vol_data = vol->data<T>();
const T *col_data = col.data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int d_offset =
(c / filter_width / filter_height) % filter_depth;
int cIm =
c / filter_width / filter_height / filter_depth;
for (int d = 0; d < output_depth; ++d) {
int d_pad = d * strides[0] - paddings[0] +
d_offset * dilations[0];
for (int h = 0; h < output_height; ++h) {
int h_pad = h * strides[1] - paddings[1] +
h_offset * dilations[1];
for (int w = 0; w < output_width; ++w) {
int w_pad = w * strides[2] - paddings[2] +
w_offset * dilations[2];
if (h_pad >= 0 && h_pad < input_height &&
w_pad >= 0 && w_pad < input_width &&
d_pad >= 0 && d_pad < input_depth) {
int vol_idx =
((cIm * input_depth + d_pad) *
input_height +
h_pad) *
input_width +
w_pad;
int col_idx = ((c * output_depth + d) *
output_height +
h) *
output_width +
w;
vol_data[vol_idx] += col_data[col_idx];
template <typename T> class Col2VolFunctor<CPU, T> {
public:
void operator()(const Tensor &col, const std::vector<int> &dilations,
const std::vector<int> &strides,
const std::vector<int> &paddings, Tensor *vol) const {
// PADDLE_ENFORCE(vol->dims().size() == 4);
// PADDLE_ENFORCE(col.dims().size() == 7);
int input_channels = vol->dims()[0];
int input_depth = vol->dims()[1];
int input_height = vol->dims()[2];
int input_width = vol->dims()[3];
int filter_depth = col.dims()[1];
int filter_height = col.dims()[2];
int filter_width = col.dims()[3];
int output_depth = col.dims()[4];
int output_height = col.dims()[5];
int output_width = col.dims()[6];
int channels_col =
input_channels * filter_depth * filter_height * filter_width;
// PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] -
// ((dilations[0] * (filter_depth - 1)
// + 1))) /
// strides[0] +
// 1,
// output_depth,
// "input_depth and output_depth are "
// "mismatching.");
// PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] -
// ((dilations[1] * (filter_height -
// 1) + 1))) /
// strides[1] +
// 1,
// output_height,
// "input_height and output_height are
// "
// "mismatching.");
// PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] -
// ((dilations[2] * (filter_width - 1)
// + 1))) /
// strides[2] +
// 1,
// output_width,
// "input_width and output_width are "
// "mismatching.");
T *vol_data = vol->data<T>();
const T *col_data = col.data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int d_offset = (c / filter_width / filter_height) % filter_depth;
int cIm = c / filter_width / filter_height / filter_depth;
for (int d = 0; d < output_depth; ++d) {
int d_pad =
d * strides[0] - paddings[0] + d_offset * dilations[0];
for (int h = 0; h < output_height; ++h) {
int h_pad =
h * strides[1] - paddings[1] + h_offset * dilations[1];
for (int w = 0; w < output_width; ++w) {
int w_pad = w * strides[2] - paddings[2] +
w_offset * dilations[2];
if (h_pad >= 0 && h_pad < input_height && w_pad >= 0 &&
w_pad < input_width && d_pad >= 0 &&
d_pad < input_depth) {
int vol_idx =
((cIm * input_depth + d_pad) * input_height +
h_pad) *
input_width +
w_pad;
int col_idx =
((c * output_depth + d) * output_height + h) *
output_width +
w;
vol_data[vol_idx] += col_data[col_idx];
}
}
}
}
}
}
}
}
}
};
template class Vol2ColFunctor<CPU, float>;
......@@ -222,5 +210,5 @@ template class Col2VolFunctor<CPU, float>;
template class Col2VolFunctor<CPU, double>;
} // namespace math
} // namespace operators
} // namespace operators
} // namespace paddle_mobile
src/operators/math/vol2col.h
浏览文件 @ 47e5978f
......@@ -72,24 +72,20 @@ namespace math {
*/
using Tensor = paddle_mobile::framework::Tensor;
template<typename DeviceType, typename T> class Vol2ColFunctor {
public:
void operator()(const Tensor &vol,
const std::vector<int> &dilations,
const std::vector<int> &strides,
const std::vector<int> &paddings,
Tensor *col) const;
template <typename DeviceType, typename T> class Vol2ColFunctor {
public:
void operator()(const Tensor &vol, const std::vector<int> &dilations,
const std::vector<int> &strides,
const std::vector<int> &paddings, Tensor *col) const;
};
template<typename DeviceType, typename T> class Col2VolFunctor {
public:
void operator()(const Tensor &col,
const std::vector<int> &dilations,
const std::vector<int> &strides,
const std::vector<int> &paddings,
Tensor *vol) const;
template <typename DeviceType, typename T> class Col2VolFunctor {
public:
void operator()(const Tensor &col, const std::vector<int> &dilations,
const std::vector<int> &strides,
const std::vector<int> &paddings, Tensor *vol) const;
};
} // namespace math
} // namespace operators
} // namespace operators
} // namespace paddle_mobile
src/operators/mul_op.cpp
浏览文件 @ 47e5978f
......@@ -21,37 +21,36 @@ SOFTWARE.
namespace paddle_mobile {
namespace operators {
template<typename Dtype, typename T>
void MulOp<Dtype, T>::InferShape() const {
auto x_dims = param_.InputX()->dims();
auto y_dims = param_.InputY()->dims();
int x_num_col_dims = param_.XNumColDims();
int y_num_col_dims = param_.YNumColDims();
template <typename Dtype, typename T> void MulOp<Dtype, T>::InferShape() const {
auto x_dims = param_.InputX()->dims();
auto y_dims = param_.InputY()->dims();
int x_num_col_dims = param_.XNumColDims();
int y_num_col_dims = param_.YNumColDims();
assert(x_dims.size() > x_num_col_dims);
assert(y_dims.size() > y_num_col_dims);
assert(x_dims.size() > x_num_col_dims);
assert(y_dims.size() > y_num_col_dims);
/// (1,2,3,4) , x_num_col_dims = 2 -> (2,12)
auto x_mat_dims = framework::flatten_to_2d(x_dims, x_num_col_dims);
auto y_mat_dims = framework::flatten_to_2d(y_dims, y_num_col_dims);
/// (1,2,3,4) , x_num_col_dims = 2 -> (2,12)
auto x_mat_dims = framework::flatten_to_2d(x_dims, x_num_col_dims);
auto y_mat_dims = framework::flatten_to_2d(y_dims, y_num_col_dims);
assert(x_mat_dims[1] == y_mat_dims[0]);
assert(x_mat_dims[1] == y_mat_dims[0]);
std::vector<int64_t> output_dims;
output_dims.reserve(static_cast<size_t>(
x_num_col_dims + y_dims.size() - y_num_col_dims));
std::vector<int64_t> output_dims;
output_dims.reserve(
static_cast<size_t>(x_num_col_dims + y_dims.size() - y_num_col_dims));
for (int i = 0; i < x_num_col_dims; ++i) {
output_dims.push_back(x_dims[i]);
}
for (int i = 0; i < x_num_col_dims; ++i) {
output_dims.push_back(x_dims[i]);
}
for (int i = y_num_col_dims; i < y_dims.size(); ++i) {
output_dims.push_back(y_dims[i]);
}
for (int i = y_num_col_dims; i < y_dims.size(); ++i) {
output_dims.push_back(y_dims[i]);
}
framework::DDim ddim = framework::make_ddim(output_dims);
param_.Out()->Resize(ddim);
framework::DDim ddim = framework::make_ddim(output_dims);
param_.Out()->Resize(ddim);
}
template class MulOp<CPU, float>;
}
}
} // namespace operators
} // namespace paddle_mobile
src/operators/mul_op.h
浏览文件 @ 47e5978f
......@@ -25,28 +25,27 @@ namespace operators {
using namespace framework;
template<typename DeviceType, typename T>
template <typename DeviceType, typename T>
class MulOp : public framework::OperatorWithKernel<DeviceType> {
public:
MulOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType>(
type, inputs, outputs, attrs, scope),
param_(inputs, outputs, attrs, *scope) {}
void Run() const {
operators::MulKernel<DeviceType, T, MulParam> kernel;
kernel.Compute(param_);
}
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override;
protected:
MulParam param_;
public:
MulOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const framework::AttributeMap attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType>(type, inputs, outputs,
attrs, scope),
param_(inputs, outputs, attrs, *scope) {}
void Run() const {
operators::MulKernel<DeviceType, T, MulParam> kernel;
kernel.Compute(param_);
}
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override;
protected:
MulParam param_;
};
} // namespace operators
} // namespace paddle
} // namespace paddle_mobile
src/operators/op_param.cpp
浏览文件 @ 47e5978f
......@@ -21,25 +21,25 @@ SOFTWARE.
namespace paddle_mobile {
namespace operators {
Print &operator<<(Print &printer, const ConvParam &conv_param) {
printer << "parameter of conv: "
<< "\n";
printer << " stride: "
<< " (" << conv_param.Strides()[0]
<< conv_param.Strides()[1] << ") "
<< "\n";
printer << " paddings: "
<< " (" << conv_param.Paddings()[0]
<< conv_param.Paddings()[1] << ") "
<< "\n";
printer << " dilations: "
<< " (" << conv_param.Dilations()[0]
<< conv_param.Dilations()[1] << ") "
<< "\n";
printer << " groups: " << conv_param.Groups() << "\n";
printer << " input dims: " << conv_param.Input()->dims() << "\n";
printer << " filter dims: " << conv_param.Filter()->dims() << "\n";
printer << " output dims: " << conv_param.Output()->dims();
return printer;
printer << "parameter of conv: "
<< "\n";
printer << " stride: "
<< " (" << conv_param.Strides()[0] << conv_param.Strides()[1]
<< ") "
<< "\n";
printer << " paddings: "
<< " (" << conv_param.Paddings()[0] << conv_param.Paddings()[1]
<< ") "
<< "\n";
printer << " dilations: "
<< " (" << conv_param.Dilations()[0] << conv_param.Dilations()[1]
<< ") "
<< "\n";
printer << " groups: " << conv_param.Groups() << "\n";
printer << " input dims: " << conv_param.Input()->dims() << "\n";
printer << " filter dims: " << conv_param.Filter()->dims() << "\n";
printer << " output dims: " << conv_param.Output()->dims();
return printer;
}
} // namespace operators
} // namespace paddle_mobile
src/operators/op_param.h
浏览文件 @ 47e5978f
......@@ -31,205 +31,195 @@ namespace operators {
using namespace framework;
class OpParam : PaddleMobileObject {
public:
protected:
template<typename T>
static T *InputFrom(const VariableNameMap &inputs,
const Scope &scope) {
return GetVarValue<T>("Input", inputs, scope);
}
template<typename T>
static T *InputXFrom(const VariableNameMap &inputs,
const Scope &scope) {
return GetVarValue<T>("X", inputs, scope);
}
template<typename T>
static T *InputYFrom(const VariableNameMap &inputs,
const Scope &scope) {
return GetVarValue<T>("Y", inputs, scope);
}
template<typename T>
static std::vector<T *>
InputMultiFrom(const VariableNameMap &inputs, const Scope &scope) {
return GetMultiVarValue<T>("Input", inputs, scope);
}
template<typename T>
static T *OutputFrom(const VariableNameMap &outputs,
const Scope &scope) {
return GetVarValue<T>("Output", outputs, scope);
}
template<typename T>
static T *OutFrom(const VariableNameMap &outputs,
const Scope &scope) {
return GetVarValue<T>("Out", outputs, scope);
}
template<typename T>
static T *FilterFrom(const VariableNameMap &inputs,
const Scope &scope) {
return GetVarValue<T>("Filter", inputs, scope);
}
template<typename T>
static const T GetAttr(std::string key, const AttributeMap &map) {
return ((Attribute) map.at(key)).Get<T>();
}
template<typename T>
static T *GetVarValue(std::string key,
const VariableNameMap &var_map,
const Scope &scope) {
auto var_vec = var_map.at(key);
if (var_vec.size()) {
// std::cout << " get var value -- " << var_vec[0] <<
// std::endl;
auto var = scope.FindVar(var_vec[0]);
return var->GetMutable<T>();
} else {
return nullptr;
public:
protected:
template <typename T>
static T *InputFrom(const VariableNameMap &inputs, const Scope &scope) {
return GetVarValue<T>("Input", inputs, scope);
}
}
template<typename T>
static std::vector<T *>
GetMultiVarValue(std::string key, const VariableNameMap &var_map,
const Scope &scope) {
auto var_vecs = var_map.at(key);
assert(var_vecs.size() > 1);
std::vector<T *> var_res;
for (auto &var_vec : var_vecs) {
auto var = scope.FindVar(var_vec);
var_res.push_back(var->GetMutable<T>());
template <typename T>
static T *InputXFrom(const VariableNameMap &inputs, const Scope &scope) {
return GetVarValue<T>("X", inputs, scope);
}
template <typename T>
static T *InputYFrom(const VariableNameMap &inputs, const Scope &scope) {
return GetVarValue<T>("Y", inputs, scope);
}
template <typename T>
static std::vector<T *> InputMultiFrom(const VariableNameMap &inputs,
const Scope &scope) {
return GetMultiVarValue<T>("Input", inputs, scope);
}
template <typename T>
static T *OutputFrom(const VariableNameMap &outputs, const Scope &scope) {
return GetVarValue<T>("Output", outputs, scope);
}
template <typename T>
static T *OutFrom(const VariableNameMap &outputs, const Scope &scope) {
return GetVarValue<T>("Out", outputs, scope);
}
template <typename T>
static T *FilterFrom(const VariableNameMap &inputs, const Scope &scope) {
return GetVarValue<T>("Filter", inputs, scope);
}
template <typename T>
static const T GetAttr(std::string key, const AttributeMap &map) {
return ((Attribute)map.at(key)).Get<T>();
}
template <typename T>
static T *GetVarValue(std::string key, const VariableNameMap &var_map,
const Scope &scope) {
auto var_vec = var_map.at(key);
if (var_vec.size()) {
// std::cout << " get var value -- " << var_vec[0] <<
// std::endl;
auto var = scope.FindVar(var_vec[0]);
return var->GetMutable<T>();
} else {
return nullptr;
}
}
template <typename T>
static std::vector<T *> GetMultiVarValue(std::string key,
const VariableNameMap &var_map,
const Scope &scope) {
auto var_vecs = var_map.at(key);
assert(var_vecs.size() > 1);
std::vector<T *> var_res;
for (auto &var_vec : var_vecs) {
auto var = scope.FindVar(var_vec);
var_res.push_back(var->GetMutable<T>());
}
return var_res;
}
return var_res;
}
};
class ConvParam : OpParam {
public:
ConvParam(const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
filter_ = FilterFrom<framework::LoDTensor>(inputs, scope);
input_ = InputFrom<framework::Tensor>(inputs, scope);
output_ = OutputFrom<framework::Tensor>(outputs, scope);
strides_ = GetAttr<std::vector<int>>("strides", attrs);
paddings_ = GetAttr<std::vector<int>>("paddings", attrs);
dilations_ = GetAttr<std::vector<int>>("dilations", attrs);
groups = GetAttr<int>("groups", attrs);
}
const Tensor *Input() const { return input_; }
const LoDTensor *Filter() const { return filter_; }
Tensor *Output() const { return output_; }
const std::vector<int> &Strides() const { return strides_; }
const std::vector<int> &Paddings() const { return paddings_; }
const std::vector<int> &Dilations() const { return dilations_; }
const int &Groups() const { return groups; }
private:
Tensor *input_;
Tensor *output_;
LoDTensor *filter_;
std::vector<int> strides_;
std::vector<int> paddings_;
std::vector<int> dilations_;
int groups;
public:
ConvParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
filter_ = FilterFrom<framework::LoDTensor>(inputs, scope);
input_ = InputFrom<framework::Tensor>(inputs, scope);
output_ = OutputFrom<framework::Tensor>(outputs, scope);
strides_ = GetAttr<std::vector<int>>("strides", attrs);
paddings_ = GetAttr<std::vector<int>>("paddings", attrs);
dilations_ = GetAttr<std::vector<int>>("dilations", attrs);
groups = GetAttr<int>("groups", attrs);
}
const Tensor *Input() const { return input_; }
const LoDTensor *Filter() const { return filter_; }
Tensor *Output() const { return output_; }
const std::vector<int> &Strides() const { return strides_; }
const std::vector<int> &Paddings() const { return paddings_; }
const std::vector<int> &Dilations() const { return dilations_; }
const int &Groups() const { return groups; }
private:
Tensor *input_;
Tensor *output_;
LoDTensor *filter_;
std::vector<int> strides_;
std::vector<int> paddings_;
std::vector<int> dilations_;
int groups;
};
Print &operator<<(Print &printer, const ConvParam &conv_param);
class ElementwiseAddParam : OpParam {
public:
ElementwiseAddParam(const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
input_y_ = InputYFrom<framework::Tensor>(inputs, scope);
out_ = OutFrom<framework::Tensor>(outputs, scope);
axis_ = GetAttr<int>("axis", attrs);
}
const Tensor *InputX() const { return input_x_; }
const Tensor *InputY() const { return input_y_; }
Tensor *Out() const { return out_; }
const int &Axis() const { return axis_; }
private:
Tensor *input_x_;
Tensor *input_y_;
Tensor *out_;
int axis_;
public:
ElementwiseAddParam(const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
input_y_ = InputYFrom<framework::Tensor>(inputs, scope);
out_ = OutFrom<framework::Tensor>(outputs, scope);
axis_ = GetAttr<int>("axis", attrs);
}
const Tensor *InputX() const { return input_x_; }
const Tensor *InputY() const { return input_y_; }
Tensor *Out() const { return out_; }
const int &Axis() const { return axis_; }
private:
Tensor *input_x_;
Tensor *input_y_;
Tensor *out_;
int axis_;
};
class MulParam : OpParam {
public:
MulParam(const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
input_y_ = InputYFrom<framework::Tensor>(inputs, scope);
out_ = OutFrom<framework::Tensor>(outputs, scope);
x_num_col_dims_ = GetAttr<int>("x_num_col_dims", attrs);
y_num_col_dims_ = GetAttr<int>("y_num_col_dims", attrs);
}
const Tensor *InputX() const { return input_x_; }
const Tensor *InputY() const { return input_y_; }
Tensor *Out() const { return out_; }
const int &XNumColDims() const { return x_num_col_dims_; }
const int &YNumColDims() const { return y_num_col_dims_; }
private:
Tensor *input_x_;
Tensor *input_y_;
Tensor *out_;
int x_num_col_dims_;
int y_num_col_dims_;
public:
MulParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
input_y_ = InputYFrom<framework::Tensor>(inputs, scope);
out_ = OutFrom<framework::Tensor>(outputs, scope);
x_num_col_dims_ = GetAttr<int>("x_num_col_dims", attrs);
y_num_col_dims_ = GetAttr<int>("y_num_col_dims", attrs);
}
const Tensor *InputX() const { return input_x_; }
const Tensor *InputY() const { return input_y_; }
Tensor *Out() const { return out_; }
const int &XNumColDims() const { return x_num_col_dims_; }
const int &YNumColDims() const { return y_num_col_dims_; }
private:
Tensor *input_x_;
Tensor *input_y_;
Tensor *out_;
int x_num_col_dims_;
int y_num_col_dims_;
};
class ConcatParam : public OpParam {
public:
ConcatParam(const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
inputs_ = InputMultiFrom<framework::Tensor>(inputs, scope);
out_ = OutFrom<framework::Tensor>(outputs, scope);
axis_ = GetAttr<int>("axis", attrs);
}
public:
ConcatParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
inputs_ = InputMultiFrom<framework::Tensor>(inputs, scope);
out_ = OutFrom<framework::Tensor>(outputs, scope);
axis_ = GetAttr<int>("axis", attrs);
}
std::vector<Tensor *> Inputs() const { return inputs_; }
std::vector<Tensor *> Inputs() const { return inputs_; }
Tensor *Out() const { return out_; }
Tensor *Out() const { return out_; }
const int &Axis() const { return axis_; }
const int &Axis() const { return axis_; }
private:
std::vector<Tensor *> inputs_;
Tensor *out_;
int axis_;
private:
std::vector<Tensor *> inputs_;
Tensor *out_;
int axis_;
};
} // namespace operators
......
test/common/test_log.cpp
浏览文件 @ 47e5978f
......@@ -22,9 +22,10 @@ int main() {
DLOGF("DASJFDAFJ%d -- %f", 12345, 344.234);
LOGF( paddle_mobile::kLOG_DEBUG, "DASJFDAFJ%d -- %f", 12345, 344.234);
LOGF(paddle_mobile::kLOG_DEBUG, "DASJFDAFJ%d -- %f", 12345, 344.234);
LOG(paddle_mobile::kLOG_DEBUG) << "test debug" << " next log";
LOG(paddle_mobile::kLOG_DEBUG) << "test debug"
<< " next log";
LOG(paddle_mobile::kLOG_DEBUG1) << "test debug1"
<< " next log";
......
test/elementwise_add_op_test.h
浏览文件 @ 47e5978f
......@@ -21,149 +21,144 @@ SOFTWARE.
#include "test_include.h"
namespace paddle_mobile {
namespace framework {
template <typename Dtype> class TestElementwiseAddOp {
public:
TestElementwiseAddOp(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();
// DLOG << " **block size " << blocks.size();
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();
// DLOG << " ops " << ops.size();
for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j];
// if (op->Type() ==
// "elementwise_add") {
// if
// (op->GetAttrMap().at("axis").Get<int>()
// != -1) {
// DLOG << "attr: axis =
// "
// <<
// op->GetAttrMap().at("axis").Get<int>();
// }
// }
// DLOG << "op:" << op->Type();
if (op->Type() == "elementwise_add" &&
op->Input("X")[0] == "batch_norm_2.tmp_2") {
DLOG << " elementwise_add attr size: "
<< op->GetAttrMap().size();
DLOG << " inputs size: " << op->GetInputs().size();
DLOG << " outputs size: "
<< op->GetOutputs().size();
DLOG << " Input X is : " << op->Input("X")[0];
DLOG << " Input Y is : " << op->Input("Y")[0];
DLOG << " Output Out is : " << op->Output("Out")[0];
Attribute axis_attr = op->GetAttrMap().at("axis");
int axis = axis_attr.Get<int>();
DLOG << " Attr axis is : " << axis;
std::shared_ptr<
operators::ElementwiseAddOp<Dtype, float>>
add = std::make_shared<
operators::ElementwiseAddOp<Dtype, float>>(
op->Type(), op->GetInputs(),
op->GetOutputs(), op->GetAttrMap(),
program_.scope);
ops_of_block_[*block_desc.get()].push_back(add);
}
}
}
}
std::shared_ptr<Tensor> predict_add(Tensor &t1, Tensor &t2) {
// feed
auto scope = program_.scope;
Variable *x_feed_value = scope->Var("batch_norm_2.tmp_2");
auto tensor_x = x_feed_value->GetMutable<Tensor>();
tensor_x->ShareDataWith(t1);
Variable *y_feed_value = scope->Var("batch_norm_0.tmp_3");
auto tensor_y = y_feed_value->GetMutable<Tensor>();
tensor_y->ShareDataWith(t2);
Variable *con_output = scope->Var("elementwise_add_0.tmp_0");
Tensor *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({1, 3, 224, 224});
// DLOG << typeid(output_tensor).name();
// DLOG << "output_tensor dims: " << output_tensor->dims();
std::shared_ptr<Tensor> out_tensor =
std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
predict_add(t1, t2, 0);
return out_tensor;
}
namespace framework {
template <typename Dtype> class TestElementwiseAddOp {
public:
TestElementwiseAddOp(const Program<Dtype> p) : program_(p) {
if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram;
} else {
to_predict_program_ = program_.originProgram;
}
private:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
void predict_add(const Tensor &t1, const Tensor &t2, 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];
DLOG << "op -> run()";
op->Run();
const std::vector<std::shared_ptr<BlockDesc>> blocks =
to_predict_program_->Blocks();
// DLOG << " **block size " << blocks.size();
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();
// DLOG << " ops " << ops.size();
for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j];
// if (op->Type() ==
// "elementwise_add") {
// if
// (op->GetAttrMap().at("axis").Get<int>()
// != -1) {
// DLOG << "attr: axis =
// "
// <<
// op->GetAttrMap().at("axis").Get<int>();
// }
// }
// DLOG << "op:" << op->Type();
if (op->Type() == "elementwise_add" &&
op->Input("X")[0] == "batch_norm_2.tmp_2") {
DLOG << " elementwise_add attr size: "
<< op->GetAttrMap().size();
DLOG << " inputs size: " << op->GetInputs().size();
DLOG << " outputs size: " << op->GetOutputs().size();
DLOG << " Input X is : " << op->Input("X")[0];
DLOG << " Input Y is : " << op->Input("Y")[0];
DLOG << " Output Out is : " << op->Output("Out")[0];
Attribute axis_attr = op->GetAttrMap().at("axis");
int axis = axis_attr.Get<int>();
DLOG << " Attr axis is : " << axis;
std::shared_ptr<operators::ElementwiseAddOp<Dtype, float>>
add = std::make_shared<
operators::ElementwiseAddOp<Dtype, float>>(
op->Type(), op->GetInputs(), op->GetOutputs(),
op->GetAttrMap(), program_.scope);
ops_of_block_[*block_desc.get()].push_back(add);
}
}
};
template class TestElementwiseAddOp<CPU>;
} // namespace framework
namespace test {
void testElementwiseAdd() {
DLOG << "----------**********----------";
DLOG << "begin to run ElementAddOp Test";
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program = loader.Load(
std::string("../../test/models/"
"image_classification_resnet.inference.model"));
/// input x (1,3,224,224)
paddle_mobile::framework::Tensor inputx;
SetupTensor<float>(&inputx, {1, 3, 224, 224}, static_cast<float>(0),
static_cast<float>(1));
float *inputx_ptr = inputx.data<float>();
/// input y (224,)
paddle_mobile::framework::Tensor inputy;
SetupTensor<float>(&inputy, {224}, static_cast<float>(0),
static_cast<float>(1));
float *inputy_ptr = inputy.data<float>();
paddle_mobile::framework::TestElementwiseAddOp<paddle_mobile::CPU>
testElementwiseAddOp(program);
auto output_add = testElementwiseAddOp.predict_add(inputx, inputy);
float *output_add_ptr = output_add->data<float>();
// for (int j = 0; j < output_add->numel(); ++j) {
// DLOG << "value of output: " << output_add_ptr[j];
// }
/// output (1,3,224,224)
DLOG << "output memory size : " << output_add->memory_size();
DLOG << "output numel : " << output_add->numel();
DLOG << inputx_ptr[226] << " + " << inputy_ptr[2] << " = "
<< output_add_ptr[226];
}
} // namespace test
}
std::shared_ptr<Tensor> predict_add(Tensor &t1, Tensor &t2) {
// feed
auto scope = program_.scope;
Variable *x_feed_value = scope->Var("batch_norm_2.tmp_2");
auto tensor_x = x_feed_value->GetMutable<Tensor>();
tensor_x->ShareDataWith(t1);
Variable *y_feed_value = scope->Var("batch_norm_0.tmp_3");
auto tensor_y = y_feed_value->GetMutable<Tensor>();
tensor_y->ShareDataWith(t2);
Variable *con_output = scope->Var("elementwise_add_0.tmp_0");
Tensor *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({1, 3, 224, 224});
// DLOG << typeid(output_tensor).name();
// DLOG << "output_tensor dims: " << output_tensor->dims();
std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
predict_add(t1, t2, 0);
return out_tensor;
}
private:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
void predict_add(const Tensor &t1, const Tensor &t2, 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];
DLOG << "op -> run()";
op->Run();
}
}
};
template class TestElementwiseAddOp<CPU>;
} // namespace framework
namespace test {
void testElementwiseAdd() {
DLOG << "----------**********----------";
DLOG << "begin to run ElementAddOp Test";
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program =
loader.Load(std::string("../../test/models/"
"image_classification_resnet.inference.model"));
/// input x (1,3,224,224)
paddle_mobile::framework::Tensor inputx;
SetupTensor<float>(&inputx, {1, 3, 224, 224}, static_cast<float>(0),
static_cast<float>(1));
float *inputx_ptr = inputx.data<float>();
/// input y (224,)
paddle_mobile::framework::Tensor inputy;
SetupTensor<float>(&inputy, {224}, static_cast<float>(0),
static_cast<float>(1));
float *inputy_ptr = inputy.data<float>();
paddle_mobile::framework::TestElementwiseAddOp<paddle_mobile::CPU>
testElementwiseAddOp(program);
auto output_add = testElementwiseAddOp.predict_add(inputx, inputy);
float *output_add_ptr = output_add->data<float>();
// for (int j = 0; j < output_add->numel(); ++j) {
// DLOG << "value of output: " << output_add_ptr[j];
// }
/// output (1,3,224,224)
DLOG << "output memory size : " << output_add->memory_size();
DLOG << "output numel : " << output_add->numel();
DLOG << inputx_ptr[226] << " + " << inputy_ptr[2] << " = "
<< output_add_ptr[226];
}
} // namespace test
} // namespace paddle_mobile
test/mul_op_test.h
浏览文件 @ 47e5978f
......@@ -21,182 +21,170 @@ SOFTWARE.
#include "test_include.h"
namespace paddle_mobile {
namespace framework {
template <typename Dtype> class TestMulOp {
public:
TestMulOp(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();
// DLOG << " **block size " << blocks.size();
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();
// DLOG << " ops " << ops.size();
for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j];
// if (op->Type() == "mul") {
// DLOG << "x_num_col_dims :
// "
// << op->GetAttrMap()
// .at("x_num_col_dims")
// .Get<int>();
// DLOG << "y_num_col_dims :
// "
// << op->GetAttrMap()
// .at("y_num_col_dims")
// .Get<int>();
// DLOG << " Input X is : "
// << op->Input("X")[0];
// }
// DLOG << "op:" << op->Type();
if (op->Type() == "mul" &&
op->Input("X")[0] == "pool2d_0.tmp_0") {
DLOG << " mul attr size: "
<< op->GetAttrMap().size();
DLOG << " inputs size: " << op->GetInputs().size();
DLOG << " outputs size: "
<< op->GetOutputs().size();
DLOG << " Input X is : " << op->Input("X")[0];
DLOG << " Input Y is : " << op->Input("Y")[0];
DLOG << " Output Out is : " << op->Output("Out")[0];
DLOG << "x_num_col_dims : "
<< op->GetAttrMap()
.at("x_num_col_dims")
.Get<int>();
DLOG << "y_num_col_dims : "
<< op->GetAttrMap()
.at("y_num_col_dims")
.Get<int>();
std::shared_ptr<operators::MulOp<Dtype, float>>
add = std::make_shared<
operators::MulOp<Dtype, float>>(
op->Type(), op->GetInputs(),
op->GetOutputs(), op->GetAttrMap(),
program_.scope);
ops_of_block_[*block_desc.get()].push_back(add);
}
}
}
}
std::shared_ptr<Tensor> predict_add(Tensor &t1, Tensor &t2) {
// feed
auto scope = program_.scope;
Variable *x_feed_value = scope->Var("pool2d_0.tmp_0");
auto tensor_x = x_feed_value->GetMutable<Tensor>();
tensor_x->ShareDataWith(t1);
Variable *y_feed_value = scope->Var("fc_0.w_0");
auto tensor_y = y_feed_value->GetMutable<Tensor>();
tensor_y->ShareDataWith(t2);
Variable *con_output = scope->Var("fc_0.tmp_0");
Tensor *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({3, 3});
// DLOG << typeid(output_tensor).name();
// DLOG << "output_tensor dims: " << output_tensor->dims();
std::shared_ptr<Tensor> out_tensor =
std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
predict_add(t1, t2, 0);
return out_tensor;
}
private:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
void predict_add(const Tensor &t1, const Tensor &t2, 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];
DLOG << "op -> run()";
op->Run();
}
}
};
template class TestMulOp<CPU>;
} // namespace framework
namespace test {
void testMul() {
DLOG << "----------**********----------";
DLOG << "begin to run MulOp Test";
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program = loader.Load(
std::string("../../test/models/"
"image_classification_resnet.inference.model"));
/// input x (3,2,1,1)
paddle_mobile::framework::Tensor inputx;
SetupTensor<float>(&inputx, {3, 2, 1, 1}, static_cast<float>(0),
static_cast<float>(1));
float *inputx_ptr = inputx.data<float>();
/// input y (2,3)
paddle_mobile::framework::Tensor inputy;
SetupTensor<float>(&inputy, {2, 3}, static_cast<float>(0),
static_cast<float>(1));
float *inputy_ptr = inputy.data<float>();
paddle_mobile::framework::TestMulOp<paddle_mobile::CPU> testMulOp(
program);
auto output_mul = testMulOp.predict_add(inputx, inputy);
float *output_mul_ptr = output_mul->data<float>();
auto dimx_1 = inputx.numel() / inputx.dims()[0];
DLOG << " inputx : ";
for (int i = 0; i < inputx.dims()[0]; ++i) {
for (int j = 0; j < dimx_1; ++j) {
DLOGF("%f ", inputx_ptr[i * dimx_1 + j]);
}
DLOGF("\n");
}
auto dimy_1 = inputy.numel() / inputy.dims()[0];
DLOG << " inputy : ";
for (int i = 0; i < inputy.dims()[0]; ++i) {
for (int j = 0; j < dimy_1; ++j) {
DLOGF("%f ", inputy_ptr[i * dimx_1 + j]);
}
DLOGF("\n");
}
namespace framework {
template <typename Dtype> class TestMulOp {
public:
TestMulOp(const Program<Dtype> p) : program_(p) {
if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram;
} else {
to_predict_program_ = program_.originProgram;
}
auto dim_output_1 = output_mul->numel() / output_mul->dims()[0];
DLOG << " output : ";
for (int i = 0; i < output_mul->dims()[0]; ++i) {
for (int j = 0; j < dim_output_1; ++j) {
DLOGF("%f ", output_mul_ptr[i * dimy_1 + j]);
const std::vector<std::shared_ptr<BlockDesc>> blocks =
to_predict_program_->Blocks();
// DLOG << " **block size " << blocks.size();
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();
// DLOG << " ops " << ops.size();
for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j];
// if (op->Type() == "mul") {
// DLOG << "x_num_col_dims :
// "
// << op->GetAttrMap()
// .at("x_num_col_dims")
// .Get<int>();
// DLOG << "y_num_col_dims :
// "
// << op->GetAttrMap()
// .at("y_num_col_dims")
// .Get<int>();
// DLOG << " Input X is : "
// << op->Input("X")[0];
// }
// DLOG << "op:" << op->Type();
if (op->Type() == "mul" &&
op->Input("X")[0] == "pool2d_0.tmp_0") {
DLOG << " mul attr size: " << op->GetAttrMap().size();
DLOG << " inputs size: " << op->GetInputs().size();
DLOG << " outputs size: " << op->GetOutputs().size();
DLOG << " Input X is : " << op->Input("X")[0];
DLOG << " Input Y is : " << op->Input("Y")[0];
DLOG << " Output Out is : " << op->Output("Out")[0];
DLOG << "x_num_col_dims : "
<< op->GetAttrMap().at("x_num_col_dims").Get<int>();
DLOG << "y_num_col_dims : "
<< op->GetAttrMap().at("y_num_col_dims").Get<int>();
std::shared_ptr<operators::MulOp<Dtype, float>> add =
std::make_shared<operators::MulOp<Dtype, float>>(
op->Type(), op->GetInputs(), op->GetOutputs(),
op->GetAttrMap(), program_.scope);
ops_of_block_[*block_desc.get()].push_back(add);
}
DLOGF("\n");
}
}
}
std::shared_ptr<Tensor> predict_add(Tensor &t1, Tensor &t2) {
// feed
auto scope = program_.scope;
Variable *x_feed_value = scope->Var("pool2d_0.tmp_0");
auto tensor_x = x_feed_value->GetMutable<Tensor>();
tensor_x->ShareDataWith(t1);
Variable *y_feed_value = scope->Var("fc_0.w_0");
auto tensor_y = y_feed_value->GetMutable<Tensor>();
tensor_y->ShareDataWith(t2);
Variable *con_output = scope->Var("fc_0.tmp_0");
Tensor *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({3, 3});
// DLOG << typeid(output_tensor).name();
// DLOG << "output_tensor dims: " << output_tensor->dims();
std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
predict_add(t1, t2, 0);
return out_tensor;
}
private:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
void predict_add(const Tensor &t1, const Tensor &t2, 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];
DLOG << "op -> run()";
op->Run();
}
}
};
template class TestMulOp<CPU>;
} // namespace framework
namespace test {
void testMul() {
DLOG << "----------**********----------";
DLOG << "begin to run MulOp Test";
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program =
loader.Load(std::string("../../test/models/"
"image_classification_resnet.inference.model"));
/// input x (3,2,1,1)
paddle_mobile::framework::Tensor inputx;
SetupTensor<float>(&inputx, {3, 2, 1, 1}, static_cast<float>(0),
static_cast<float>(1));
float *inputx_ptr = inputx.data<float>();
/// input y (2,3)
paddle_mobile::framework::Tensor inputy;
SetupTensor<float>(&inputy, {2, 3}, static_cast<float>(0),
static_cast<float>(1));
float *inputy_ptr = inputy.data<float>();
paddle_mobile::framework::TestMulOp<paddle_mobile::CPU> testMulOp(program);
auto output_mul = testMulOp.predict_add(inputx, inputy);
float *output_mul_ptr = output_mul->data<float>();
auto dimx_1 = inputx.numel() / inputx.dims()[0];
DLOG << " inputx : ";
for (int i = 0; i < inputx.dims()[0]; ++i) {
for (int j = 0; j < dimx_1; ++j) {
DLOGF("%f ", inputx_ptr[i * dimx_1 + j]);
}
DLOGF("\n");
}
auto dimy_1 = inputy.numel() / inputy.dims()[0];
DLOG << " inputy : ";
for (int i = 0; i < inputy.dims()[0]; ++i) {
for (int j = 0; j < dimy_1; ++j) {
DLOGF("%f ", inputy_ptr[i * dimx_1 + j]);
}
DLOGF("\n");
}
auto dim_output_1 = output_mul->numel() / output_mul->dims()[0];
DLOG << " output : ";
for (int i = 0; i < output_mul->dims()[0]; ++i) {
for (int j = 0; j < dim_output_1; ++j) {
DLOGF("%f ", output_mul_ptr[i * dimy_1 + j]);
}
DLOGF("\n");
}
/// output (3,3)
DLOG << "output memory size : " << output_mul->memory_size();
DLOG << "output numel : " << output_mul->numel();
/// output (3,3)
DLOG << "output memory size : " << output_mul->memory_size();
DLOG << "output numel : " << output_mul->numel();
DLOG << inputx_ptr[0] << " x " << inputy_ptr[0] << " + "
<< inputx_ptr[1] << " x " << inputy_ptr[0 + 3] << " = "
<< output_mul_ptr[0];
}
} // namespace test
DLOG << inputx_ptr[0] << " x " << inputy_ptr[0] << " + " << inputx_ptr[1]
<< " x " << inputy_ptr[0 + 3] << " = " << output_mul_ptr[0];
}
} // namespace test
} // namespace paddle_mobile
test/test_include.h
浏览文件 @ 47e5978f
......@@ -7,7 +7,6 @@
#include "framework/scope.h"
#include "framework/tensor.h"
#include "framework/variable.h"
#include "framework/variable.h"
#include "io.h"
#include "test_helper.h"
#include <map>
......
tools/pre-commit.hooks/.clang-format.hook 0 → 100644
浏览文件 @ 47e5978f
#!/bin/bash
#set -e
#
#readonly VERSION="3.8"
#
#version=$(clang-format -version)
#
#if ! [[ $version == *"$VERSION"* ]]; then
# echo "clang-format version check failed."
# echo "a version contains '$VERSION' is needed, but get '$version'"
# echo "you can install the right version, and make an soft-link to '\$PATH' env"
# exit -1
#fi
clang-format $@
tools/pre-commit.hooks/.clang-tidy.hook
浏览文件 @ 47e5978f
#!/bin/bash
echo "allonli clang-tidy formating init"
cmake -DCMAKE_EXPORT_COMPILE_COMMANDS=ON
clang-foramt -version
clang-tidy -version
python ./tools/pre-commit.hooks/run-clang-tidy.py
python ./tools/pre-commit.hooks/run-clang-tidy.py -header-filter=third-party/
#TOTAL_ERRORS=0
# The trick to remove deleted files: https://stackoverflow.com/a/2413151
......
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