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提交 dff4a781 编写于 作者: water94's avatar water94 提交者： GitHub
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Merge pull request #213 from allonli/develop 

add some gitignore option & modify to 2 spaces indent & format code & rm build folder
上级 e35ef6fe ba186e1d
展开全部 隐藏空白更改
内联 并排
Showing with 13546 addition and 14089 deletion
.clang-format
浏览文件 @ dff4a781
......@@ -2,5 +2,4 @@
Language: Cpp
BasedOnStyle: LLVM
Standard: Cpp11
IndentWidth: 4
...
.gitignore
浏览文件 @ dff4a781
......@@ -54,5 +54,6 @@ paddle-mobile.cbp
compile_commands.json
cmake-build-debug/
cmake-build-release/
test/models/
\ No newline at end of file
test/models/
cmake-build-release/compile_commands.json 已删除 100644 → 0
浏览文件 @ e35ef6fe
此差异已折叠。
src/common/log.h
浏览文件 @ dff4a781
......@@ -28,15 +28,15 @@ SOFTWARE.
namespace paddle_mobile {
enum LogLevel {
kNO_LOG,
kLOG_ERROR,
kLOG_WARNING,
kLOG_INFO,
kLOG_DEBUG,
kLOG_DEBUG1,
kLOG_DEBUG2,
kLOG_DEBUG3,
kLOG_DEBUG4
kNO_LOG,
kLOG_ERROR,
kLOG_WARNING,
kLOG_INFO,
kLOG_DEBUG,
kLOG_DEBUG1,
kLOG_DEBUG2,
kLOG_DEBUG3,
kLOG_DEBUG4
};
// log level
......@@ -49,119 +49,117 @@ 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();
}
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();
}
std::ostringstream buffer_;
}
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_;
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())
if (level > paddle_mobile::log_level) { \
} else \
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())
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())
} // namespace paddle_mobile
#define LOGF(level, format, ...) \
if (level > paddle_mobile::log_level) { \
} else \
printf(format, ##__VA_ARGS__)
if (level > paddle_mobile::log_level) { \
} else \
printf(format, ##__VA_ARGS__)
#define DLOGF(format, ...) \
if (paddle_mobile::kLOG_DEBUG > paddle_mobile::log_level) { \
} else \
printf(format, ##__VA_ARGS__)
if (paddle_mobile::kLOG_DEBUG > paddle_mobile::log_level) { \
} else \
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
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) {}
friend struct ToLog;
template <typename T> Print &operator<<(T const &value) {}
private:
private:
};
struct ToLog {
ToLog(LogLevel level) {}
ToLog(LogLevel level) {}
template <typename T> ToLog &operator<<(T const &value) { return *this; }
template <typename T> ToLog &operator<<(T const &value) { return *this; }
};
#define LOG(level) \
if (true) { \
} else \
paddle_mobile::ToLog(level)
if (true) { \
} else \
paddle_mobile::ToLog(level)
#define DLOG \
if (true) { \
} else \
paddle_mobile::ToLog(paddle_mobile::kLOG_DEBUG)
if (true) { \
} else \
paddle_mobile::ToLog(paddle_mobile::kLOG_DEBUG)
#define LOGF(level, format, ...)
......
src/common/types.h
浏览文件 @ dff4a781
......@@ -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 @ dff4a781
......@@ -24,74 +24,74 @@ namespace paddle_mobile {
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;
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;
}
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);
// }
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;
}
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; };
......
src/framework/attribute.h
浏览文件 @ dff4a781
......@@ -27,102 +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;
}
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;
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;
}
Attribute() {}
template <typename T, typename... Args> Attribute &Set(Args &&... args) {
variant_.Set<T>(args...);
return *this;
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;
}
template <typename T> T &Get() const { return variant_.Get<T>(); }
Attribute() {}
template <typename T, typename... Args> Attribute &Set(Args &&... args) {
variant_.Set<T>(args...);
return *this;
}
private:
Variant<int, float, std::string, std::vector<int>, std::vector<float>,
std::vector<std::string>, bool, std::vector<bool>, BlockDesc *,
int64_t>
variant_;
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_;
};
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 @ dff4a781
......@@ -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 @ dff4a781
......@@ -27,29 +27,28 @@ 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
......@@ -58,13 +57,13 @@ class BlockDesc : PaddleMobileObject {
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);
}
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 @ dff4a781
......@@ -22,45 +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;
out << DataLayoutToString(l);
return out;
}
} // namespace framework
......
src/framework/data_transform.cpp
浏览文件 @ dff4a781
......@@ -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");
// }
// 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/ddim.cc
浏览文件 @ dff4a781
......@@ -20,158 +20,156 @@ 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));
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;
}
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 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 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);
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) {}
public:
explicit DynamicMutableIndexer(int idx) : idx_(idx) {}
template <int D> int64_t &operator()(Dim<D> &dim) const {
return dim[idx_];
}
template <int D> int64_t &operator()(Dim<D> &dim) const { return dim[idx_]; }
private:
int idx_;
private:
int idx_;
};
struct DynamicConstIndexer : public Vistor<int64_t> {
public:
explicit DynamicConstIndexer(int idx) : idx_(idx) {}
public:
explicit DynamicConstIndexer(int idx) : idx_(idx) {}
template <int D> int64_t operator()(const Dim<D> &dim) const {
return dim[idx_];
}
template <int D> int64_t operator()(const Dim<D> &dim) const {
return dim[idx_];
}
private:
int idx_;
private:
int idx_;
};
/// @endcond
int64_t &DDim::operator[](int idx) {
return DDim::ApplyVistor(DynamicMutableIndexer(idx), *this);
return DDim::ApplyVistor(DynamicMutableIndexer(idx), *this);
}
int64_t DDim::operator[](int idx) const {
return DDim::ApplyVistor(DynamicConstIndexer(idx), *this);
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;
}
// 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;
// }
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> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
std::vector<int64_t> v3;
std::vector<int64_t> v3;
assert(v1.size() == v2.size());
assert(v1.size() == v2.size());
for (unsigned int i = 0; i < v1.size(); i++) {
v3.push_back(v1[i] + v2[i]);
}
for (unsigned int i = 0; i < v1.size(); i++) {
v3.push_back(v1[i] + v2[i]);
}
return make_ddim(v3);
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> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
std::vector<int64_t> v3;
std::vector<int64_t> v3;
assert(v1.size() == v2.size());
assert(v1.size() == v2.size());
for (unsigned int i = 0; i < v1.size(); i++) {
v3.push_back(v1[i] * v2[i]);
}
for (unsigned int i = 0; i < v1.size(); i++) {
v3.push_back(v1[i] * v2[i]);
}
return make_ddim(v3);
return make_ddim(v3);
}
int64_t get(const DDim &ddim, int idx) { return ddim[idx]; }
......@@ -180,152 +178,152 @@ void set(DDim &ddim, int idx, int value) { ddim[idx] = value; }
/// @cond HIDDEN
struct VectorizeVisitor : Vistor<void> {
std::vector<int64_t> &vector;
std::vector<int64_t> &vector;
explicit VectorizeVisitor(std::vector<int64_t> &v) : vector(v) {}
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);
}
template <typename T> void operator()(const T &t) {
vector.push_back(t.head);
this->operator()(t.tail);
}
void operator()(const Dim<0> &t) {}
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;
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;
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);
}
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);
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.");
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;
}
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);
}
--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.");
}
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);
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; }
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); }
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) {}
OSVistor(std::ostream &os) : os_(os) {}
template <int D> std::ostream &operator()(Dim<D> dim) const {
return os_ << dim;
}
template <int D> std::ostream &operator()(Dim<D> dim) const {
return os_ << dim;
}
private:
std::ostream &os_;
private:
std::ostream &os_;
};
std::ostream &operator<<(std::ostream &os, const DDim &ddim) {
auto vistor = OSVistor(os);
DDim::ApplyVistor(vistor, ddim);
return os;
auto vistor = OSVistor(os);
DDim::ApplyVistor(vistor, ddim);
return os;
}
DDim::DDim(std::initializer_list<int64_t> init_list) {
*this = make_ddim(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))});
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);
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);
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
......
src/framework/ddim.h
浏览文件 @ dff4a781
......@@ -30,77 +30,77 @@ namespace framework {
* 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();
}
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>()); }
DDim() { var.Set<Dim<1>>(Dim<1>()); }
template <int D> explicit DDim(const Dim<D> &in) { var.Set<Dim<D>>(in); }
template <int D> explicit DDim(const Dim<D> &in) { var.Set<Dim<D>>(in); }
/*implicit*/ DDim(std::initializer_list<int64_t> init_list);
/*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;
}
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);
int64_t operator[](int idx) const;
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);
// }
// 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; }
DDimVar getVar() { return var; }
bool operator==(DDim d) const;
bool operator==(DDim d) const;
bool operator!=(DDim d) const;
bool operator!=(DDim d) const;
DDim operator+(DDim d) const;
DDim operator+(DDim d) const;
DDim operator*(DDim d) const;
DDim operator*(DDim d) const;
int size() const;
int size() const;
};
/**
......
src/framework/dim.h
浏览文件 @ dff4a781
......@@ -25,199 +25,197 @@ namespace framework {
// Statically sized, statically indexed dimension
template <int i> struct Dim {
static constexpr int dimensions = i;
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;
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 true; }
HOSTDEVICE
bool operator!=(const Dim<0> &o) const { return false; }
HOSTDEVICE
bool operator!=(const Dim<0> &o) const { return false; }
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;
};
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);
}
// 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;
}
// 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) {
#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) {
#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>(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);
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);
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);
// 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);
return indexer(*this, i);
}
// Dynamic access to constant Dim
inline HOSTDEVICE int64_t Dim<0>::operator[](int i) const {
return indexer(*this, i);
return indexer(*this, i);
}
// Dynamic access to mutable Dim
inline HOSTDEVICE int64_t &Dim<0>::operator[](int i) {
return indexer(*this, i);
return indexer(*this, i);
}
// Dynamic access to constant Dim
......@@ -225,52 +223,52 @@ inline HOSTDEVICE int64_t &Dim<0>::operator[](int i) {
template <int l>
HOSTDEVICE typename std::enable_if<(l > 0), int64_t>::type get(const Dim<l> &d,
int i) {
return d[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];
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);
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;
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);
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;
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));
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;
return true;
}
/**
......@@ -278,14 +276,14 @@ HOSTDEVICE inline bool contained(const Dim<0> &idx, const Dim<0> &size) {
*/
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));
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>();
return Dim<0>();
}
///\endcond
......@@ -293,36 +291,36 @@ template <> HOSTDEVICE inline Dim<0> ex_prefix_mul(const Dim<0> &src, int mul) {
* 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));
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>();
return Dim<0>();
}
template <int i>
HOSTDEVICE Dim<i> operator+(const Dim<i> &lhs, const Dim<i> &rhs) {
return dim_plus(lhs, 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));
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>();
return Dim<0>();
}
template <int i>
HOSTDEVICE Dim<i> operator*(const Dim<i> &lhs, const Dim<i> &rhs) {
return dim_mult(lhs, rhs);
return dim_mult(lhs, rhs);
}
/**
......@@ -337,8 +335,8 @@ HOSTDEVICE Dim<i> operator*(const Dim<i> &lhs, const Dim<i> &rhs) {
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));
int norm_stride = size.head == 1 ? 0 : stride.head;
return Dim<i>(norm_stride, normalize_strides(size.tail, stride.tail));
}
///\cond HIDDEN
......@@ -346,7 +344,7 @@ HOSTDEVICE Dim<i> normalize_strides(const Dim<i> &size, const Dim<i> &stride) {
template <>
HOSTDEVICE inline Dim<0> normalize_strides(const Dim<0> &size,
const Dim<0> &stride) {
return Dim<0>();
return Dim<0>();
}
///\endcond
......@@ -361,7 +359,7 @@ HOSTDEVICE inline Dim<0> normalize_strides(const Dim<0> &size,
template <typename... Args>
HOSTDEVICE Dim<sizeof...(Args)> make_dim(Args... idxes) {
return Dim<sizeof...(Args)>(idxes...);
return Dim<sizeof...(Args)>(idxes...);
}
// Allows us to output a Dim
......@@ -369,8 +367,8 @@ HOSTDEVICE Dim<sizeof...(Args)> make_dim(Args... idxes) {
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;
os << d.head << ", " << d.tail;
return os;
}
// Base case that allows us to output a Dim
......@@ -378,34 +376,34 @@ operator<<(std::ostream &os, const Dim<i> &d) {
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;
os << d.head;
return os;
}
inline std::ostream &operator<<(std::ostream &os, const Dim<0> &d) {
return os;
return os;
}
template <int i> HOST std::string Dim<i>::to_string() const {
std::stringstream stream;
std::stringstream stream;
stream << *this;
stream << *this;
return stream.str();
return stream.str();
}
template <int D>
HOSTDEVICE Dim<D> linear_to_dimension(int linear_index, Dim<D> extents) {
Dim<D> result;
Dim<D> result;
for (int i = 0; i < D - 1; ++i) {
result[i] = linear_index % extents[i];
linear_index /= extents[i];
}
for (int i = 0; i < D - 1; ++i) {
result[i] = linear_index % extents[i];
linear_index /= extents[i];
}
result[D - 1] = linear_index;
result[D - 1] = linear_index;
return result;
return result;
}
} // namespace framework
......
src/framework/executor.cpp
浏览文件 @ dff4a781
......@@ -26,66 +26,66 @@ 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;
}
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);
// 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];
op->Run();
}
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];
op->Run();
}
}
template class Executor<CPU>;
......
src/framework/executor.h
浏览文件 @ dff4a781
......@@ -35,23 +35,23 @@ namespace paddle_mobile {
namespace framework {
template <typename Dtype> class Executor {
public:
Executor();
public:
Executor();
Executor(const Program<Dtype> p);
Executor(const Program<Dtype> p);
std::shared_ptr<Tensor> predict(Tensor &t);
std::shared_ptr<Tensor> predict(Tensor &t);
public:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
public:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
void predict(const Tensor &t, int block_id);
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;
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
};
} // namespace framework
......
src/framework/framework.pb.cpp
浏览文件 @ dff4a781
此差异已折叠。
src/framework/framework.pb.h
浏览文件 @ dff4a781
此差异已折叠。
src/framework/lod_tensor.cc
浏览文件 @ dff4a781
......@@ -22,291 +22,289 @@ namespace paddle_mobile {
namespace framework {
std::ostream &operator<<(std::ostream &os, const LoD &lod) {
os << "{";
for (auto &v : 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 << "}";
bool is_first = true;
for (auto &i : v) {
if (is_first) {
os << i;
is_first = false;
} else {
os << ", " << i;
}
}
os << "}";
}
os << "}";
return 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;
// 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();
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;
}
// 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 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];
}
// 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;
}
return result;
}
bool operator==(const LoD &a, const LoD &b) {
if (a.size() != b.size()) {
return false;
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;
}
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;
}
}
for (size_t j = 0; j < a_level.size(); j++) {
if (a_level[j] != b_level[j]) {
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;
}
}
// 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;
}
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;
}
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;
}
}
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;
}
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];
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}};
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;
}
*lod = LoD(lod_length.size(), std::vector<size_t>({0}));
// 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;
}
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);
}
*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 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 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));
}
// 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 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));
}
// the 3st filed, Tensor
TensorFromStream(is, static_cast<Tensor *>(tensor));
}
} // namespace framework
......
src/framework/lod_tensor.h
浏览文件 @ dff4a781
......@@ -102,45 +102,45 @@ bool CheckAbsLoD(const LoD &in, int tensor_height = -1);
* 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_;
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_;
};
/*
......@@ -155,26 +155,26 @@ class LoDTensor : public Tensor {
*/
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);
}
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;
}
return tensor;
}
// Get the absolute offset of a lod[start_level][start_idx:end_idx] and
......
src/framework/op_desc.cpp
浏览文件 @ dff4a781
......@@ -8,51 +8,51 @@ 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_.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 (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;
// }
}
}
for (const proto::OpDesc::Attr &attr : desc_.attrs()) {
std::string attr_name = attr.name();
if (attr.type() != proto::AttrType::BLOCK) {
attrs_[attr_name] = Attribute::GetAttrValue(attr);
// if (attr.type() == proto::AttrType::INT){
// std::cout << " attrName " << attr_name << " " <<
// attrs_[attr_name].Get<int>() << std::endl;
// }
}
}
}
const std::vector<std::string> &OpDesc::Input(const std::string &name) const {
return inputs_.find(name)->second;
return inputs_.find(name)->second;
}
const std::vector<std::string> &OpDesc::Output(const std::string &name) const {
return outputs_.find(name)->second;
return outputs_.find(name)->second;
}
Attribute OpDesc::GetAttr(const std::string &name) const {
auto it = attrs_.find(name);
return it->second;
auto it = attrs_.find(name);
return it->second;
}
const std::unordered_map<std::string, Attribute> &OpDesc::GetAttrMap() const {
return attrs_;
return attrs_;
}
} // namespace framework
......
src/framework/op_desc.h
浏览文件 @ dff4a781
......@@ -26,25 +26,25 @@ namespace paddle_mobile {
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;
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
......
src/framework/op_info.h
浏览文件 @ dff4a781
......@@ -25,13 +25,13 @@ 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_;
}
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;
......@@ -39,55 +39,55 @@ 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;
public:
static OpInfoMap &Instance() {
if (g_op_info_map<Dtype> == nullptr) {
g_op_info_map<Dtype> = new OpInfoMap();
}
const OpInfo<Dtype> *GetNullable(const std::string &type) const {
auto it = map_.find(type);
if (it == map_.end()) {
return nullptr;
} else {
return &it->second;
}
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_;
}
const std::unordered_map<std::string, OpInfo<Dtype>> &map() const {
return map_;
}
std::unordered_map<std::string, OpInfo<Dtype>> *mutable_map() {
return &map_;
}
std::unordered_map<std::string, OpInfo<Dtype>> *mutable_map() {
return &map_;
}
private:
OpInfoMap() = default;
std::unordered_map<std::string, OpInfo<Dtype>> map_;
private:
OpInfoMap() = default;
std::unordered_map<std::string, OpInfo<Dtype>> map_;
// DISABLE_COPY_AND_ASSIGN(OpInfoMap);
// DISABLE_COPY_AND_ASSIGN(OpInfoMap);
};
} // namespace framework
......
src/framework/op_kernel_type.h
浏览文件 @ dff4a781
......@@ -24,41 +24,40 @@ SOFTWARE.
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);
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;
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_);
return (l.data_type_ != r.data_type_) ||
NeedTransformLayout(l.data_layout_, r.data_layout_);
}
} // namespace framework
......
src/framework/operator.cpp
浏览文件 @ dff4a781
......@@ -30,7 +30,7 @@ OperatorBase<Dtype>::OperatorBase(const std::string &type,
std::shared_ptr<Scope> scope)
: type_(type), inputs_(inputs), outputs_(outputs), attrs_(attrs),
scope_(scope) {
CheckAllInputOutputSet();
CheckAllInputOutputSet();
}
template <typename Dtype>
void OperatorBase<Dtype>::CheckAllInputOutputSet() const {}
......
src/framework/operator.h
浏览文件 @ dff4a781
......@@ -49,50 +49,50 @@ static std::unordered_map<
{"fetch", {{"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;
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 std::vector<std::string> &var_names) const {
if (this->scope_) {
this->scope_->EraseVars(var_names);
}
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 std::vector<std::string> &var_names) const {
if (this->scope_) {
this->scope_->EraseVars(var_names);
}
}
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;
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;
public:
virtual void Compute(const P &para) const = 0;
virtual ~OpKernelBase() = default;
virtual ~OpKernelBase() = default;
};
} // namespace framework
......
src/framework/paddle_mobile_object.h
浏览文件 @ dff4a781
......@@ -24,13 +24,13 @@ SOFTWARE.
namespace paddle_mobile {
class PaddleMobileObject {
public:
virtual std::string ToString() {
char address[128] = {0};
sprintf(address, "%p", this);
return std::string(address);
}
public:
virtual std::string ToString() {
char address[128] = {0};
sprintf(address, "%p", this);
return std::string(address);
}
private:
private:
};
} // namespace paddle_mobile
src/framework/program-optimize/node.cpp
浏览文件 @ dff4a781
......@@ -25,71 +25,71 @@ namespace paddle_mobile {
namespace framework {
Node &Node::operator>(std::shared_ptr<Node> node) {
outputs_.push_back(node);
std::shared_ptr<Node> this_node;
node->inputs_.push_back(this);
return *node;
outputs_.push_back(node);
std::shared_ptr<Node> this_node;
node->inputs_.push_back(this);
return *node;
}
bool Node::operator==(const Node &in) {
if (in.type_ == this->type_) {
if (this->outputs_.size() == in.outputs_.size()) {
for (int i = 0; i < outputs_.size(); ++i) {
if (!(*outputs_[i] == *in.outputs_[i])) {
return false;
}
}
} else {
return false;
if (in.type_ == this->type_) {
if (this->outputs_.size() == in.outputs_.size()) {
for (int i = 0; i < outputs_.size(); ++i) {
if (!(*outputs_[i] == *in.outputs_[i])) {
return false;
}
}
} else {
return false;
return false;
}
return true;
} else {
return false;
}
return true;
}
std::string Node::ToString(std::string blank, const Node *node) const {
std::stringstream ss;
ss << type_ << "-> \n";
std::stringstream ss;
ss << type_ << "-> \n";
if (inputs_.size() > 1 && node != inputs_.back()) {
return ss.str();
} else if (inputs_.size() > 1 && node == inputs_.back()) {
ss << "\n" << blank << type_ << "\n";
}
for (int i = 0; i < outputs_.size(); ++i) {
ss << blank << outputs_[i]->ToString(blank + " ", this) << "";
}
if (inputs_.size() > 1 && node != inputs_.back()) {
return ss.str();
} else if (inputs_.size() > 1 && node == inputs_.back()) {
ss << "\n" << blank << type_ << "\n";
}
for (int i = 0; i < outputs_.size(); ++i) {
ss << blank << outputs_[i]->ToString(blank + " ", this) << "";
}
return ss.str();
}
std::string Node::ToString() const { return this->ToString(" ", this); }
Node &Node::To(int index) {
if (index == 0) {
this->outputs_.clear();
}
if (index == 0) {
this->outputs_.clear();
}
for (int j = 0; j < this->outputs_.size(); ++j) {
outputs_[j]->To(index - 1);
}
return *this;
for (int j = 0; j < this->outputs_.size(); ++j) {
outputs_[j]->To(index - 1);
}
return *this;
}
uint Node::depth(uint begin) {
uint depth = 0;
begin++;
for (int i = 0; i < outputs_.size(); ++i) {
uint output_depth = outputs_[i]->depth(begin);
depth = output_depth > depth ? output_depth : depth;
}
return begin > depth ? begin : depth;
uint depth = 0;
begin++;
for (int i = 0; i < outputs_.size(); ++i) {
uint output_depth = outputs_[i]->depth(begin);
depth = output_depth > depth ? output_depth : depth;
}
return begin > depth ? begin : depth;
}
Print &operator<<(Print &printer, const Node &node) {
printer << node.ToString();
return printer;
printer << node.ToString();
return printer;
}
} // namespace framework
......
src/framework/program-optimize/node.h
浏览文件 @ dff4a781
......@@ -29,22 +29,22 @@ namespace paddle_mobile {
namespace framework {
class Node : PaddleMobileObject {
public:
Node(const std::string &type) : type_(type) {}
Node(std::shared_ptr<OpDesc> op_desc)
: op_desc_(op_desc), type_(op_desc->Type()){};
Node &operator>(std::shared_ptr<Node> node);
bool operator==(const Node &in);
std::string ToString() const;
Node &To(int index);
uint depth(uint begin = 0);
public:
Node(const std::string &type) : type_(type) {}
Node(std::shared_ptr<OpDesc> op_desc)
: op_desc_(op_desc), type_(op_desc->Type()){};
Node &operator>(std::shared_ptr<Node> node);
bool operator==(const Node &in);
std::string ToString() const;
Node &To(int index);
uint depth(uint begin = 0);
private:
std::shared_ptr<OpDesc> op_desc_;
std::string ToString(std::string blank, const Node *node) const;
std::vector<std::shared_ptr<Node>> outputs_;
std::vector<Node *> inputs_;
std::string type_;
private:
std::shared_ptr<OpDesc> op_desc_;
std::string ToString(std::string blank, const Node *node) const;
std::vector<std::shared_ptr<Node>> outputs_;
std::vector<Node *> inputs_;
std::string type_;
};
Print &operator<<(Print &printer, const Node &node);
......
src/framework/program-optimize/program_optimize.cpp
浏览文件 @ dff4a781
......@@ -26,49 +26,48 @@ std::shared_ptr<ProgramDesc> ProgramOptimize::Optimize() {}
std::shared_ptr<ProgramDesc>
ProgramOptimize::FushionOptimize(std::shared_ptr<ProgramDesc> ori_des) {
for (int i = 0; i < ori_des->Blocks().size(); ++i) {
std::unordered_map<std::string, std::shared_ptr<Node>> output_nodes;
std::shared_ptr<Node> begin_node;
auto block = ori_des->Block(i);
// DLOG << " ops size: " << block->Ops().size();
for (int j = 0; j < block->Ops().size(); ++j) {
auto op = block->Ops()[j];
auto op_type = op->Type();
// DLOG << "op type: " << op_type << " index: " << j;
if (op_input_output_key.find(op->Type()) ==
op_input_output_key.end()) {
return NULL;
}
for (int i = 0; i < ori_des->Blocks().size(); ++i) {
std::unordered_map<std::string, std::shared_ptr<Node>> output_nodes;
std::shared_ptr<Node> begin_node;
auto block = ori_des->Block(i);
// DLOG << " ops size: " << block->Ops().size();
for (int j = 0; j < block->Ops().size(); ++j) {
auto op = block->Ops()[j];
auto op_type = op->Type();
// DLOG << "op type: " << op_type << " index: " << j;
if (op_input_output_key.find(op->Type()) == op_input_output_key.end()) {
return NULL;
}
std::shared_ptr<Node> node = std::make_shared<Node>(op);
if (j == 0) {
begin_node = node;
}
std::shared_ptr<Node> node = std::make_shared<Node>(op);
if (j == 0) {
begin_node = node;
}
auto input_keys = op_input_output_key.at(op->Type()).first;
for (auto input_key : input_keys) {
auto op_inputs = op->Input(input_key);
for (int l = 0; l < op_inputs.size(); ++l) {
std::string input_key = op_inputs[l];
if (output_nodes.find(input_key) != output_nodes.end()) {
auto input_node = output_nodes[input_key];
*input_node > node;
}
}
}
auto output_keys = op_input_output_key.at(op_type).second;
for (auto output_key : output_keys) {
auto op_outputs = op->Output(output_key);
for (int k = 0; k < op_outputs.size(); ++k) {
output_nodes[op_outputs[k]] = node;
}
}
auto input_keys = op_input_output_key.at(op->Type()).first;
for (auto input_key : input_keys) {
auto op_inputs = op->Input(input_key);
for (int l = 0; l < op_inputs.size(); ++l) {
std::string input_key = op_inputs[l];
if (output_nodes.find(input_key) != output_nodes.end()) {
auto input_node = output_nodes[input_key];
*input_node > node;
}
}
}
DLOG << "node: \n" << *begin_node;
auto output_keys = op_input_output_key.at(op_type).second;
for (auto output_key : output_keys) {
auto op_outputs = op->Output(output_key);
for (int k = 0; k < op_outputs.size(); ++k) {
output_nodes[op_outputs[k]] = node;
}
}
}
return ori_des;
DLOG << "node: \n" << *begin_node;
}
return ori_des;
}
} // namespace framework
} // namespace paddle_mobile
src/framework/program-optimize/program_optimize.h
浏览文件 @ dff4a781
......@@ -26,16 +26,16 @@ namespace paddle_mobile {
namespace framework {
class ProgramOptimize {
public:
ProgramOptimize() {}
std::shared_ptr<ProgramDesc> Optimize();
std::shared_ptr<ProgramDesc>
FushionOptimize(std::shared_ptr<ProgramDesc> ori_des);
public:
ProgramOptimize() {}
std::shared_ptr<ProgramDesc> Optimize();
std::shared_ptr<ProgramDesc>
FushionOptimize(std::shared_ptr<ProgramDesc> ori_des);
private:
// std::shared_ptr<ProgramDesc> ori_desc_;
std::vector<std::unordered_map<std::string, std::shared_ptr<Node>>>
outputs_nodes_;
private:
// std::shared_ptr<ProgramDesc> ori_desc_;
std::vector<std::unordered_map<std::string, std::shared_ptr<Node>>>
outputs_nodes_;
};
} // namespace framework
} // namespace paddle_mobile
src/framework/program.h
浏览文件 @ dff4a781
......@@ -28,12 +28,12 @@ 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;
public:
std::shared_ptr<ProgramDesc> originProgram;
std::shared_ptr<ProgramDesc> optimizeProgram;
std::shared_ptr<Scope> scope;
private:
private:
};
} // namespace framework
......
src/framework/program_desc.cpp
浏览文件 @ dff4a781
......@@ -8,14 +8,14 @@ namespace paddle_mobile {
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));
}
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];
return blocks_[idx];
}
} // namespace framework
......
src/framework/program_desc.h
浏览文件 @ dff4a781
......@@ -28,14 +28,14 @@ namespace paddle_mobile {
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_; };
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
......
src/framework/scope.cc
浏览文件 @ dff4a781
......@@ -7,20 +7,20 @@ namespace paddle_mobile {
namespace framework {
Scope &Scope::NewScope() const {
std::unique_lock<std::mutex> lock(mutex_);
kids_.push_back(new Scope(this));
return *kids_.back();
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);
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) {
......@@ -33,70 +33,70 @@ Variable *Scope::Var(const std::string &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);
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;
}
for (auto &name_var : vars_) {
if (name_var.second == var) {
return this;
}
return (parent_ == nullptr) ? nullptr : parent_->FindScope(var);
}
return (parent_ == nullptr) ? nullptr : parent_->FindScope(var);
}
void Scope::DropKids() {
for (Scope *s : kids_) {
delete s;
}
kids_.clear();
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> 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
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;
}
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);
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)
......@@ -108,11 +108,11 @@ void Scope::Rename(const std::string &origin_name,
// }
Variable *Scope::FindVarLocally(const std::string &name) const {
auto it = vars_.find(name);
if (it != vars_.end()) {
return it->second;
}
return nullptr;
auto it = vars_.find(name);
if (it != vars_.end()) {
return it->second;
}
return nullptr;
}
} // namespace framework
......
src/framework/scope.h
浏览文件 @ dff4a781
......@@ -26,56 +26,56 @@ SOFTWARE.
namespace paddle_mobile {
namespace framework {
class Scope {
public:
Scope() {}
~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_;
mutable std::mutex mutex_;
};
} // namespace framework
} // namespace paddle_mobile
src/framework/selected_rows.h
浏览文件 @ dff4a781
......@@ -27,54 +27,54 @@ namespace paddle_mobile {
namespace framework {
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());
}
const Tensor &value() const { return *value_; }
Tensor *mutable_value() { return value_.get(); }
int64_t height() const { return height_; }
void set_height(int64_t height) { height_ = height; }
const std::vector<int64_t> &rows() const { return rows_; }
std::vector<int64_t> *mutable_rows() { return &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));
}
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_;
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());
}
const Tensor &value() const { return *value_; }
Tensor *mutable_value() { return value_.get(); }
int64_t height() const { return height_; }
void set_height(int64_t height) { height_ = height; }
const std::vector<int64_t> &rows() const { return rows_; }
std::vector<int64_t> *mutable_rows() { return &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));
}
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_;
};
} // namespace framework
......
src/framework/tensor.h
浏览文件 @ dff4a781
......@@ -29,305 +29,304 @@ 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;
}
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; }
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);
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;
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_);
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);
}
/*! 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_);
// 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;
}
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)));
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;
}
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_);
}
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"));
}
inline void *mutable_data() {
// PADDLE_ENFORCE(this->holder_ != nullptr,
// "Cannot invoke mutable data if current hold
// nothing.");
return mutable_data(holder_->type());
}
virtual size_t size() const { return size_; }
/**
* @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>();
}
virtual void *ptr() const { return static_cast<void *>(ptr_.get()); }
/*! Return the dimensions of the memory block. */
inline const DDim &dims() const { return dims_; }
virtual std::type_index type() const { return type_; }
/*! Return the numel of the memory block. */
inline int64_t numel() const { return product(dims_); }
virtual void set_type(std::type_index type) { type_ = type; }
/*! Resize the dimensions of the memory block. */
inline Tensor &Resize(const DDim &dims) {
dims_ = dims;
return *this;
}
/*! the pointer of memory block. */
std::unique_ptr<uint8_t, memory::PODDeleter<uint8_t>> ptr_;
/*! The internal of two tensors share the same memory block. */
inline Tensor &ShareDataWith(const Tensor &src) {
src.check_memory_size();
*this = src;
return *this;
}
/*! the size of memory block. */
size_t size_;
/**
* @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;
}
}
/* the current type of memory */
std::type_index type_;
};
std::type_index type() const {
// PADDLE_ENFORCE_NOT_NULL(
// holder_, "Tensor not initialized yet
// when
// Tensor::type() is called.");
return holder_->type();
}
/*! holds the memory block if allocated. */
std::shared_ptr<Placeholder> holder_;
// memory size returns the holding memory size in byte.
size_t memory_size() const {
return holder_ == nullptr ? 0UL : holder_->size() - offset_;
}
/**
* @brief points to elements dimensions.
*
* @note dims_ do not indicate the memory block size.
*/
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.");
}
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;
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_;
/**
* @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;
Tensor res;
res.ShareDataWith(src);
res.Resize(flatten_to_2d(src.dims(), num_col_dims));
return res;
}
} // namespace framework
......
src/framework/tensor_util.cc
浏览文件 @ dff4a781
此差异已折叠。
src/framework/tensor_util.h
浏览文件 @ dff4a781
......@@ -43,23 +43,23 @@ void TensorFromStream(std::istream &is, Tensor *tensor);
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);
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);
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
......
src/framework/var_desc.h
浏览文件 @ dff4a781
......@@ -25,63 +25,63 @@ namespace paddle_mobile {
namespace framework {
class VarDesc {
public:
VarDesc(const proto::VarDesc &desc);
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
......
src/framework/var_type.h
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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());
}
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
......
src/framework/variable.h
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src/io.cpp
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src/io.h
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template <typename Dtype, Precision P = Precision::FP32>
class Loader : PaddleMobileObject {
public:
const framework::Program<Dtype, P> Load(const std::string &dirname);
public:
const framework::Program<Dtype, P> Load(const std::string &dirname);
private:
void LoadVar(framework::LoDTensor *tensor, const std::string &file_path);
private:
void LoadVar(framework::LoDTensor *tensor, const std::string &file_path);
};
} // namespace paddle_mobile
src/memory/t_malloc.cc
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src/memory/t_malloc.h
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* static_cast
*/
template <typename T> class PODDeleter {
static_assert(std::is_pod<T>::value, "T must be POD");
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)); }
};
/**
......@@ -55,10 +55,10 @@ template <typename T> class PODDeleter {
* reinterpret_cast
*/
template <typename T> class PlainDeleter {
public:
explicit PlainDeleter(){};
public:
explicit PlainDeleter(){};
void operator()(T *ptr) { Free(reinterpret_cast<void *>(ptr)); }
void operator()(T *ptr) { Free(reinterpret_cast<void *>(ptr)); }
};
} // namespace memory
} // namespace paddle_mobile
src/operators/batchnorm_op.cpp
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src/operators/batchnorm_op.h
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src/operators/concat_op.cpp
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src/operators/concat_op.h
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src/operators/conv_op.cpp
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src/operators/conv_op.h
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src/operators/elementwise_add_op.h
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src/operators/kernel/conv_kernel.h
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src/operators/kernel/lrn_kernel.h
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src/operators/kernel/mul_kernel.h
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src/operators/kernel/pool_kernel.h
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src/operators/lrn_op.cpp
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src/operators/lrn_op.h
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src/operators/math/im2col.cc
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src/operators/math/im2col.h
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src/operators/math/math_function.cc
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src/operators/math/pool3x3.h
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src/operators/math/pool_2x2.h
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src/operators/math/pooling.cpp
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src/operators/math/pooling.h
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src/operators/math/transform.h
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src/operators/math/vol2col.cc
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src/operators/math/vol2col.h
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src/operators/mul_op.cpp
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src/operators/mul_op.h
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src/operators/op_param.cpp
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src/operators/op_param.h
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src/operators/pool_op.cpp
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src/operators/pool_op.h
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src/platform/data_type.h
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src/platform/macros.h
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test/common/test_log.cpp
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test/framework/executor_for_test.h
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test/framework/test_load.cpp
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test/framework/test_optimize.cpp
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test/operators/test_concat_op.cpp
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test/operators/test_cov_op.cpp
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test/operators/test_lrn_op.cpp
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test/operators/test_mul_op.cpp
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test/operators/test_pool_op.cpp
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test/test_helper.h
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