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未验证 提交 30470853 编写于 作者: 石 石晓伟 提交者： GitHub
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[Refactoring Tensor PR #7] differentiate deprecated interfaces (#39228)

上级 01d04be6
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Showing with 600 addition and 642 deletion
paddle/fluid/framework/variable.h
浏览文件 @ 30470853
...@@ -72,7 +72,7 @@ class Variable { ...@@ -72,7 +72,7 @@ class Variable {
private: private:
// This method hides type T, so it doesn't appear as a template parameter of // This method hides type T, so it doesn't appear as a template parameter of
// Variable. // Variable.
pten::TensorInplaceVersion* InplaceVersionCounter(); pten::DenseTensor::InplaceVersion* InplaceVersionCounter();
public: public:
void SetInplaceVersionToZero(); void SetInplaceVersionToZero();
...@@ -114,8 +114,8 @@ class Variable { ...@@ -114,8 +114,8 @@ class Variable {
std::shared_ptr<Placeholder> holder_; std::shared_ptr<Placeholder> holder_;
}; };
inline pten::TensorInplaceVersion* Variable::InplaceVersionCounter() { inline pten::DenseTensor::InplaceVersion* Variable::InplaceVersionCounter() {
pten::TensorInplaceVersion* version_counter_ptr(nullptr); pten::DenseTensor::InplaceVersion* version_counter_ptr(nullptr);
if (IsType<framework::LoDTensor>()) { if (IsType<framework::LoDTensor>()) {
version_counter_ptr = version_counter_ptr =
&GetMutable<framework::LoDTensor>()->InplaceVersionCounter(); &GetMutable<framework::LoDTensor>()->InplaceVersionCounter();
......
paddle/pten/core/CMakeLists.txt
浏览文件 @ 30470853
...@@ -19,7 +19,7 @@ cc_library(kernel_context SRCS kernel_context.cc DEPS pten_enforce pten_context) ...@@ -19,7 +19,7 @@ cc_library(kernel_context SRCS kernel_context.cc DEPS pten_enforce pten_context)
cc_library(tensor_base SRCS tensor_base.cc allocator.cc storage.cc DEPS pten_enforce) cc_library(tensor_base SRCS tensor_base.cc allocator.cc storage.cc DEPS pten_enforce)
cc_library(tensor_meta SRCS tensor_meta.cc DEPS pten_enforce mixed_vector) cc_library(tensor_meta SRCS tensor_meta.cc DEPS pten_enforce mixed_vector)
cc_library(lod_utils SRCS lod_utils.cc DEPS pten_enforce mixed_vector) cc_library(lod_utils SRCS lod_utils.cc DEPS pten_enforce mixed_vector)
cc_library(dense_tensor SRCS dense_tensor.cc DEPS convert_utils tensor_meta tensor_base) cc_library(dense_tensor SRCS dense_tensor.cc dense_tensor_impl.cc DEPS convert_utils tensor_meta tensor_base)
cc_library(pten_device_context SRCS device_context.cc DEPS tensor_base ) cc_library(pten_device_context SRCS device_context.cc DEPS tensor_base )
cc_library(meta_tensor SRCS meta_tensor.cc DEPS tensor_base tensor_meta dense_tensor) cc_library(meta_tensor SRCS meta_tensor.cc DEPS tensor_base tensor_meta dense_tensor)
......
paddle/pten/core/dense_tensor.cc
浏览文件 @ 30470853
/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. /* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License"); Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License. you may not use this file except in compliance with the License.
...@@ -22,14 +22,6 @@ limitations under the License. */ ...@@ -22,14 +22,6 @@ limitations under the License. */
#include "paddle/pten/api/lib/utils/storage.h" #include "paddle/pten/api/lib/utils/storage.h"
#include "paddle/pten/core/convert_utils.h" #include "paddle/pten/core/convert_utils.h"
namespace paddle {
namespace framework {
extern void TensorCopy(const pten::DenseTensor& src,
const paddle::platform::Place& dst_place,
pten::DenseTensor* dst);
}
}
namespace pten { namespace pten {
DenseTensor::DenseTensor(Allocator* a, const DenseTensorMeta& meta) DenseTensor::DenseTensor(Allocator* a, const DenseTensorMeta& meta)
...@@ -180,375 +172,4 @@ DATA_MEMBER_FUNC_INSTANTIATION(::paddle::experimental::complex128); ...@@ -180,375 +172,4 @@ DATA_MEMBER_FUNC_INSTANTIATION(::paddle::experimental::complex128);
#undef DATA_MEMBER_FUNC_INSTANTIATION #undef DATA_MEMBER_FUNC_INSTANTIATION
/* --------------------------- */
/* From framework::Tensor */
/* --------------------------- */
DenseTensor::DenseTensor() {
inplace_version_counter_ = std::make_shared<TensorInplaceVersion>(0);
meta_.dtype = paddle::experimental::DataType::FLOAT32;
meta_.offset = 0;
}
DenseTensor::DenseTensor(paddle::framework::proto::VarType::Type dtype) {
inplace_version_counter_ = std::make_shared<TensorInplaceVersion>(0);
meta_.dtype = TransToPtenDataType(dtype);
meta_.offset = 0;
}
size_t DenseTensor::memory_size() const {
return holder_ == nullptr ? 0UL : holder_->size() - meta_.offset;
}
void DenseTensor::check_memory_size() const {
PADDLE_ENFORCE_NOT_NULL(holder_,
paddle::platform::errors::PreconditionNotMet(
"Tensor holds no memory. "
"Call Tensor::mutable_data firstly."));
PADDLE_ENFORCE_LE(
numel() * SizeOf(dtype()),
memory_size(),
paddle::platform::errors::PreconditionNotMet(
"Tensor's dimension is out of bound."
"Tensor's dimension must be equal or less than the size of its "
"memory."
"But received Tensor's dimension is d%, memory's size is %d.",
numel() * SizeOf(dtype()),
memory_size()));
}
const paddle::platform::Place& DenseTensor::place() const {
PADDLE_ENFORCE_NOT_NULL(
holder_,
paddle::platform::errors::PreconditionNotMet(
"Tensor not initialized yet when DenseTensor::place() is called."));
return holder_->place();
}
paddle::framework::proto::VarType::Type DenseTensor::type() const {
return TransToProtoVarType(meta_.dtype);
}
paddle::framework::proto::VarType::Type DenseTensor::saved_type() const {
return TransToProtoVarType(meta_.dtype);
}
void DenseTensor::set_layout(const paddle::framework::DataLayout layout) {
meta_.layout = layout;
}
void DenseTensor::ResetHolder(const std::shared_ptr<pten::Allocation>& holder) {
PADDLE_ENFORCE_EQ(
meta_.offset,
0,
paddle::platform::errors::Fatal(
"Only the offset is supported to zero when the holder is reset."));
if (holder_) {
// TODO(zyfncg): The change of static_cast<> in check will recover back
// when SetAllocationForOutputTenosr is deleted.
// Now the numel() may return -1, and will cast to a very large number when
// compare with a data with unsigned long type, this will make checking
// failed, so it's a temporary solution to deal with this problem.
PADDLE_ENFORCE_LE(
numel() * static_cast<int64_t>(SizeOf(dtype())),
static_cast<int64_t>(holder->size()),
paddle::platform::errors::InvalidArgument(
"The size of Holder is not enough to store the Tensor."));
}
holder_ = holder;
}
void DenseTensor::ResetHolderWithType(
const std::shared_ptr<pten::Allocation>& holder,
paddle::framework::proto::VarType::Type type) {
set_type(type);
ResetHolder(holder);
}
void DenseTensor::set_type(paddle::framework::proto::VarType::Type type) {
meta_.dtype = TransToPtenDataType(type);
}
void* DenseTensor::mutable_data(const paddle::platform::Place& place,
paddle::framework::proto::VarType::Type type,
size_t requested_size) {
set_type(type);
PADDLE_ENFORCE_GE(
numel(),
0,
paddle::platform::errors::PreconditionNotMet(
"The Tensor's element number must be equal or greater than zero. "
"The Tensor's shape is [",
dims(),
"] now"));
size_t size = numel() * SizeOf(dtype());
if (requested_size && (requested_size > size)) {
size = requested_size;
}
/* some versions of boost::variant don't have operator!= */
if (holder_ == nullptr || !(holder_->place() == place) ||
holder_->size() < size + meta_.offset) {
holder_.reset();
holder_ = paddle::memory::AllocShared(place, size);
meta_.offset = 0;
}
return reinterpret_cast<void*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
meta_.offset);
}
void* DenseTensor::mutable_data(const paddle::platform::Place& place,
size_t requested_size) {
return mutable_data(place, type(), requested_size);
}
void* DenseTensor::mutable_data(const paddle::platform::Place& place,
paddle::framework::proto::VarType::Type type,
const paddle::platform::Stream& stream) {
set_type(type);
PADDLE_ENFORCE_GE(
numel(),
0,
paddle::platform::errors::PreconditionNotMet(
"The Tensor's element number must be equal or greater than zero. "
"The Tensor's shape is [",
dims(),
"] now"));
size_t size = numel() * SizeOf(dtype());
/* some versions of boost::variant don't have operator!= */
if (holder_ == nullptr || !(holder_->place() == place) ||
holder_->size() < size + meta_.offset ||
!(paddle::platform::is_gpu_place(place) &&
paddle::memory::InSameStream(holder_, stream))) {
holder_.reset();
holder_ = paddle::memory::AllocShared(place, size, stream);
meta_.offset = 0;
}
return reinterpret_cast<void*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
meta_.offset);
}
/* @jim19930609: The following "mutable_data" only supports specific dtypes
defined in OpProto. This part need another clean up once the data type across
Fluid
and Pten get unified.
*/
template <typename T>
inline T* DenseTensor::mutable_data(const DDim& dims,
const paddle::platform::Place& place,
size_t requested_size) {
static_assert(std::is_pod<T>::value, "T must be POD");
meta_.dims = dims;
return mutable_data<T>(place, requested_size);
}
template <typename T>
inline T* DenseTensor::mutable_data(const paddle::platform::Place& place,
size_t requested_size) {
static_assert(std::is_pod<T>::value, "T must be POD");
return reinterpret_cast<T*>(mutable_data(
place, paddle::framework::DataTypeTrait<T>::DataType(), requested_size));
}
void DenseTensor::ShareBufferWith(const DenseTensor& tensor) {
holder_ = tensor.holder_;
meta_.offset = tensor.meta().offset;
meta_.dtype = tensor.dtype();
}
#define LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(dtype) \
template dtype* DenseTensor::mutable_data( \
const DDim& dims, \
const paddle::platform::Place& place, \
size_t requested_size); \
template dtype* DenseTensor::mutable_data( \
const paddle::platform::Place& place, size_t requested_size);
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(bool)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(int8_t)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(uint8_t)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(int16_t)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(int32_t)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(int64_t)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(float)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(double)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(::paddle::platform::bfloat16)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(::paddle::platform::float16)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(::paddle::experimental::complex64)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(::paddle::experimental::complex128)
#undef LEGACY_DATA_MEMBER_FUNC_INSTANTIATION
/* ------------------------------ */
/* From framework::LoDTensor */
/* ------------------------------ */
DenseTensor::DenseTensor(intrusive_ptr<Storage> storage,
const DenseTensorMeta& meta)
: meta_(meta), holder_(storage->move_data_shared()) {}
DenseTensor::DenseTensor(intrusive_ptr<Storage> storage, DenseTensorMeta&& meta)
: meta_(std::move(meta)), holder_(storage->move_data_shared()) {}
DenseTensor::DenseTensor(const LoD& lod) : DenseTensor() { meta_.lod = lod; }
void DenseTensor::set_lod(const LoD& lod) { meta_.lod = lod; }
LoD* DenseTensor::mutable_lod() { return &meta_.lod; }
std::pair<size_t, size_t> DenseTensor::lod_element(size_t level,
size_t elem) const {
PADDLE_ENFORCE_LT(
level,
NumLevels(),
paddle::platform::errors::InvalidArgument(
"The input level of LoD is invalid, it should be less than LoD "
"size. The input level is %zu, the LoD size is %zu.",
level,
NumLevels()));
PADDLE_ENFORCE_LT(elem,
NumElements(level),
paddle::platform::errors::InvalidArgument(
"The input element of LoD is invalid, it should be "
"less than the number of elements in its level."
"The input element is %zu, the number of elements in "
"its level is %zu.",
elem,
NumElements(level)));
return std::make_pair((meta_.lod)[level][elem], (meta_.lod)[level][elem + 1]);
}
size_t DenseTensor::NumLevels() const { return meta_.lod.size(); }
size_t DenseTensor::NumElements(size_t level) const {
PADDLE_ENFORCE_LT(
level,
NumLevels(),
paddle::platform::errors::InvalidArgument(
"The input level of LoD is invalid, it should be less than LoD "
"size. The input level is %zu, the LoD size is %zu.",
level,
NumLevels()));
// the last offset is the end of last element
return (meta_.lod)[level].size() - 1;
}
DenseTensor& DenseTensor::Resize(const DDim& dims) {
meta_.dims = dims;
return *this;
}
DenseTensor DenseTensor::Slice(int64_t begin_idx, int64_t end_idx) const {
check_memory_size();
PADDLE_ENFORCE_GE(begin_idx,
0,
paddle::platform::errors::OutOfRange(
"The start row index must be greater than 0."
"But received the start index is d%.",
begin_idx));
PADDLE_ENFORCE_LE(end_idx,
meta_.dims[0],
paddle::platform::errors::OutOfRange(
"The end row index is out of bound."));
PADDLE_ENFORCE_LT(
begin_idx,
end_idx,
paddle::platform::errors::InvalidArgument(
"The start row index must be less than the end row index."
"But received the start index = %d, the end index = %d.",
begin_idx,
end_idx));
if (meta_.dims[0] == 1) {
return *this;
} else {
size_t base = numel() / meta_.dims[0];
DenseTensor dst;
dst.holder_ = holder_;
dst.set_layout(meta_.layout);
dst.meta_.dtype = meta_.dtype;
DDim dst_dims = meta_.dims;
dst_dims[0] = end_idx - begin_idx;
dst.Resize(dst_dims);
dst.meta_.offset = meta_.offset + begin_idx * base * SizeOf(dtype());
return dst;
}
}
std::vector<DenseTensor> DenseTensor::Split(int64_t split_size,
int64_t axis) const {
check_memory_size();
PADDLE_ENFORCE_GE(meta_.dims.size(),
0,
paddle::platform::errors::OutOfRange(
"split expects at least a 1-dimensional tensor"));
PADDLE_ENFORCE_GE(
split_size,
0,
paddle::platform::errors::OutOfRange(
"split expects split_size be non-negative, but got split_size is %d",
split_size));
int64_t numel_size = meta_.dims[axis];
int64_t num_splits = 1;
if (split_size != 0) {
num_splits =
std::max<int64_t>((numel_size + split_size - 1) / split_size, 1);
}
std::vector<DenseTensor> splits(num_splits);
int64_t last_split_size = split_size - (split_size * num_splits - numel_size);
for (int64_t i = 0; i < num_splits; ++i) {
int64_t length = i < num_splits - 1 ? split_size : last_split_size;
splits[i] = Slice(i * split_size, i * split_size + length);
}
return splits;
}
std::vector<DenseTensor> DenseTensor::Chunk(int64_t chunks,
int64_t axis) const {
check_memory_size();
PADDLE_ENFORCE_GE(meta_.dims.size(),
0,
paddle::platform::errors::OutOfRange(
"split expects at least a 1-dimensional tensor"));
PADDLE_ENFORCE_GE(
chunks,
0,
paddle::platform::errors::OutOfRange(
"chunks expects to be greater than 0, but got chunks is %d", chunks));
int64_t numel_size = meta_.dims[axis];
int64_t split_size = (numel_size + chunks - 1) / chunks;
return Split(split_size, axis);
}
DenseTensor& DenseTensor::ShareDataWith(const DenseTensor& src) {
src.check_memory_size();
// Preserve LoD
auto lod = meta_.lod;
*this = src;
meta_.lod = lod;
return *this;
}
DenseTensor& DenseTensor::ShareInplaceVersionCounterWith(
const DenseTensor& src) {
PADDLE_ENFORCE_NOT_NULL(
inplace_version_counter_,
paddle::platform::errors::PreconditionNotMet(
"Tensor does not hold inplace_version_counter_."));
inplace_version_counter_ = src.inplace_version_counter_;
return *this;
}
} // namespace pten } // namespace pten
paddle/pten/core/dense_tensor.h
浏览文件 @ 30470853
/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. /* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License"); Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License. you may not use this file except in compliance with the License.
...@@ -33,25 +33,6 @@ namespace pten { ...@@ -33,25 +33,6 @@ namespace pten {
class CompatibleDenseTensorUtils; class CompatibleDenseTensorUtils;
/* --------------------------- */
/* From framework::Tensor */
/* --------------------------- */
/* Temporarily put TensorInplaceVersion inside DenseTensor.
Will move to AutogradMeta as soon as we switch to Eager Dygraph.
*/
class TensorInplaceVersion {
public:
explicit TensorInplaceVersion(uint32_t inplace_version = 0)
: inplace_version_(inplace_version) {}
bool IsUnique() const { return inplace_version_ == 0; }
void Bump() { ++inplace_version_; }
uint32_t CurrentVersion() const { return inplace_version_; }
void SetInplaceVersionToZero() { inplace_version_ = 0; }
private:
uint32_t inplace_version_;
};
/// \brief The Dense tensor store values in a contiguous sequential block /// \brief The Dense tensor store values in a contiguous sequential block
/// of memory where all values are represented. Tensors or multi-dimensional /// of memory where all values are represented. Tensors or multi-dimensional
/// arrays are used in math operators. /// arrays are used in math operators.
...@@ -90,6 +71,8 @@ class DenseTensor : public TensorBase, ...@@ -90,6 +71,8 @@ class DenseTensor : public TensorBase,
DenseTensor& operator=(DenseTensor&& other); DenseTensor& operator=(DenseTensor&& other);
DenseTensor();
/// \brief Destroy the tensor object and release exclusive resources. /// \brief Destroy the tensor object and release exclusive resources.
virtual ~DenseTensor() = default; virtual ~DenseTensor() = default;
...@@ -179,181 +162,6 @@ class DenseTensor : public TensorBase, ...@@ -179,181 +162,6 @@ class DenseTensor : public TensorBase,
DenseTensorMeta meta_; DenseTensorMeta meta_;
std::shared_ptr<pten::Allocation> holder_; std::shared_ptr<pten::Allocation> holder_;
/* --------------------------- */ #include "paddle/pten/core/dense_tensor.inl"
/* From framework::Tensor */
/* --------------------------- */
/* The following members & interfaces were copied from framework::Tensor,
so as to facilitate the unification of different Tensors
Will be adjusted/removed/moved in the near future
*/
public:
/* @jim19930609: The way default constructor handles allocator might change,
according to
the final design of Allocation - Allocator.
*/
DenseTensor();
/* @jim19930609: Remove dependency on protobuf after Tensor Unification.
*/
explicit DenseTensor(paddle::framework::proto::VarType::Type dtype);
/// \brief Use existing storage space to create dense tensor. This interface
/// can be used to deliberately create an uninitialized dense tensor.
/// \param storage The existing storage.
/// \param meta The meta data of dense tensor.
DenseTensor(intrusive_ptr<Storage> storage, const DenseTensorMeta& meta);
/// \brief Use existing storage space to create dense tensor. This interface
/// can be used to deliberately create an uninitialized dense tensor.
/// \param storage The existing storage.
/// \param meta The meta data of dense tensor.
DenseTensor(intrusive_ptr<Storage> storage, DenseTensorMeta&& meta);
inline bool IsInitialized() const { return holder_ != nullptr; }
template <typename T>
T* data();
void* data();
template <typename T>
T* mutable_data(const paddle::platform::Place& place,
size_t requested_size = 0);
template <typename T>
T* mutable_data(const DDim& dims,
const paddle::platform::Place& place,
size_t requested_size = 0);
void* mutable_data(const paddle::platform::Place& place,
paddle::framework::proto::VarType::Type type,
size_t requested_size = 0);
void* mutable_data(const paddle::platform::Place& place,
size_t requested_size = 0);
void* mutable_data(const paddle::platform::Place& place,
paddle::framework::proto::VarType::Type type,
const paddle::platform::Stream& stream);
/* @jim19930609: Remove dependency on protobuf after Tensor Unification.
*/
paddle::framework::proto::VarType::Type type() const;
/* @jim19930609: Remove dependency on protobuf after Tensor Unification.
*/
paddle::framework::proto::VarType::Type saved_type() const;
// memory size returns the holding memory size in byte.
size_t memory_size() const;
void check_memory_size() const;
void set_layout(const paddle::framework::DataLayout layout);
void clear() {
holder_.reset();
meta_.offset = 0;
}
void ShareBufferWith(const DenseTensor& tensor);
void ShareDataTypeWith(const DenseTensor& tensor) {
meta_.dtype = tensor.meta().dtype;
}
bool IsSharedBufferWith(const DenseTensor& src) const {
return holder_ && holder_ == src.Holder();
}
const std::shared_ptr<pten::Allocation>& Holder() const { return holder_; }
void set_offset(size_t offset) { meta_.offset = offset; }
size_t offset() const { return meta_.offset; }
std::shared_ptr<pten::Allocation> MoveMemoryHolder() {
return std::move(holder_);
}
void ResetHolder(const std::shared_ptr<pten::Allocation>& holder);
void ResetHolderWithType(const std::shared_ptr<pten::Allocation>& holder,
paddle::framework::proto::VarType::Type type);
void set_type(paddle::framework::proto::VarType::Type type);
TensorInplaceVersion& InplaceVersionCounter() {
return *inplace_version_counter_;
}
/*! The internal of two tensors share the same memory block. */
DenseTensor& ShareDataWith(const DenseTensor& src);
/*! The internal of two tensors share the same inplace version counter. */
DenseTensor& ShareInplaceVersionCounterWith(const DenseTensor& src);
DenseTensor Slice(int64_t begin_idx, int64_t end_idx) const;
std::vector<DenseTensor> Split(int64_t split_size, int64_t axis) const;
std::vector<DenseTensor> Chunk(int64_t chunks, int64_t axis) const;
protected:
std::shared_ptr<TensorInplaceVersion> inplace_version_counter_;
/* @jim19930609: This is a hack
In general, it is badly designed to fuse MKLDNN-specific objects into a
generic Tensor.
We temporarily leave them here to unblock Tensor Unification progress.
In the final state, we should come up with a MKLDNN_Tensor and move the
following codes there.
*/
#ifdef PADDLE_WITH_MKLDNN
public:
inline dnnl::memory::format_tag format() const { return format_; }
inline void set_format(const dnnl::memory::format_tag format) {
format_ = format;
}
protected:
/**
* @brief the detail format of memory block which have layout as kMKLDNN
*
* @note MKLDNN lib support various memory format like nchw, nhwc, nChw8C,
* nChw16c, etc. For a MKLDNN memory block, layout will be set as
* DataLayout::kMKLDNN meanwhile detail memory format will be kept in
* this field.
*/
dnnl::memory::format_tag format_ = dnnl::memory::format_tag::undef;
#endif
/* ------------------------------ */
/* From framework::LoDTensor */
/* ------------------------------ */
/* The following members & interfaces were copied from framework::Tensor,
so as to facilitate the unification of different Tensors
Will be adjusted/removed/moved in the near future
*/
public:
explicit DenseTensor(const LoD& lod);
void set_lod(const LoD& lod);
LoD* mutable_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;
size_t NumLevels() const;
size_t NumElements(size_t level = 0) const;
}; };
} // namespace pten } // namespace pten
paddle/pten/core/dense_tensor.inl 0 → 100644
浏览文件 @ 30470853
/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
/* --------------------------- */
/* From framework::Tensor */
/* --------------------------- */
/* The following members & interfaces were copied from framework::Tensor,
so as to facilitate the unification of different Tensors
Will be adjusted/removed/moved in the near future
*/
public:
/* Temporarily put InplaceVersion inside DenseTensor.
Will move to AutogradMeta as soon as we switch to Eager Dygraph.
*/
class InplaceVersion {
public:
bool IsUnique() const { return inplace_version_ == 0; }
void Bump() { ++inplace_version_; }
uint32_t CurrentVersion() const { return inplace_version_; }
void SetInplaceVersionToZero() { inplace_version_ = 0; }
private:
uint32_t inplace_version_{0};
};
/* @jim19930609: Remove dependency on protobuf after Tensor Unification.
*/
explicit DenseTensor(paddle::framework::proto::VarType::Type dtype);
/// \brief Use existing storage space to create dense tensor. This interface
/// can be used to deliberately create an uninitialized dense tensor.
/// \param storage The existing storage.
/// \param meta The meta data of dense tensor.
DenseTensor(intrusive_ptr<Storage> storage, const DenseTensorMeta& meta);
/// \brief Use existing storage space to create dense tensor. This interface
/// can be used to deliberately create an uninitialized dense tensor.
/// \param storage The existing storage.
/// \param meta The meta data of dense tensor.
DenseTensor(intrusive_ptr<Storage> storage, DenseTensorMeta&& meta);
inline bool IsInitialized() const { return holder_ != nullptr; }
template <typename T>
T* data();
void* data();
template <typename T>
T* mutable_data(const paddle::platform::Place& place,
size_t requested_size = 0);
template <typename T>
T* mutable_data(const DDim& dims,
const paddle::platform::Place& place,
size_t requested_size = 0);
void* mutable_data(const paddle::platform::Place& place,
paddle::framework::proto::VarType::Type type,
size_t requested_size = 0);
void* mutable_data(const paddle::platform::Place& place,
size_t requested_size = 0);
void* mutable_data(const paddle::platform::Place& place,
paddle::framework::proto::VarType::Type type,
const paddle::platform::Stream& stream);
/* @jim19930609: Remove dependency on protobuf after Tensor Unification.
*/
paddle::framework::proto::VarType::Type type() const;
/* @jim19930609: Remove dependency on protobuf after Tensor Unification.
*/
paddle::framework::proto::VarType::Type saved_type() const;
// memory size returns the holding memory size in byte.
size_t memory_size() const;
void check_memory_size() const;
void set_layout(const paddle::framework::DataLayout layout);
void clear() {
holder_.reset();
meta_.offset = 0;
}
void ShareBufferWith(const DenseTensor& tensor);
void ShareDataTypeWith(const DenseTensor& tensor) {
meta_.dtype = tensor.meta().dtype;
}
bool IsSharedBufferWith(const DenseTensor& src) const {
return holder_ && holder_ == src.Holder();
}
const std::shared_ptr<pten::Allocation>& Holder() const { return holder_; }
void set_offset(size_t offset) { meta_.offset = offset; }
size_t offset() const { return meta_.offset; }
std::shared_ptr<pten::Allocation> MoveMemoryHolder() {
return std::move(holder_);
}
void ResetHolder(const std::shared_ptr<pten::Allocation>& holder);
void ResetHolderWithType(const std::shared_ptr<pten::Allocation>& holder,
paddle::framework::proto::VarType::Type type);
void set_type(paddle::framework::proto::VarType::Type type);
InplaceVersion& InplaceVersionCounter() {
return *inplace_version_counter_;
}
/*! The internal of two tensors share the same memory block. */
DenseTensor& ShareDataWith(const DenseTensor& src);
/*! The internal of two tensors share the same inplace version counter. */
DenseTensor& ShareInplaceVersionCounterWith(const DenseTensor& src);
DenseTensor Slice(int64_t begin_idx, int64_t end_idx) const;
std::vector<DenseTensor> Split(int64_t split_size, int64_t axis) const;
std::vector<DenseTensor> Chunk(int64_t chunks, int64_t axis) const;
protected:
std::shared_ptr<InplaceVersion> inplace_version_counter_{std::make_shared<InplaceVersion>()};
/* @jim19930609: This is a hack
In general, it is badly designed to fuse MKLDNN-specific objects into a
generic Tensor.
We temporarily leave them here to unblock Tensor Unification progress.
In the final state, we should come up with a MKLDNN_Tensor and move the
following codes there.
*/
#ifdef PADDLE_WITH_MKLDNN
public:
inline dnnl::memory::format_tag format() const { return format_; }
inline void set_format(const dnnl::memory::format_tag format) {
format_ = format;
}
protected:
/**
* @brief the detail format of memory block which have layout as kMKLDNN
*
* @note MKLDNN lib support various memory format like nchw, nhwc, nChw8C,
* nChw16c, etc. For a MKLDNN memory block, layout will be set as
* DataLayout::kMKLDNN meanwhile detail memory format will be kept in
* this field.
*/
dnnl::memory::format_tag format_ = dnnl::memory::format_tag::undef;
#endif
/* ------------------------------ */
/* From framework::LoDTensor */
/* ------------------------------ */
/* The following members & interfaces were copied from framework::Tensor,
so as to facilitate the unification of different Tensors
Will be adjusted/removed/moved in the near future
*/
public:
explicit DenseTensor(const LoD& lod);
void set_lod(const LoD& lod);
LoD* mutable_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;
size_t NumLevels() const;
size_t NumElements(size_t level = 0) const;
paddle/pten/core/dense_tensor_impl.cc 0 → 100644
浏览文件 @ 30470853
/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/pten/core/dense_tensor.h"
// See Note [ Why still include the fluid headers? ]
#include "paddle/pten/common/bfloat16.h"
#include "paddle/pten/common/complex.h"
#include "paddle/pten/common/float16.h"
#include "paddle/pten/api/lib/utils/storage.h"
#include "paddle/pten/core/convert_utils.h"
namespace pten {
/* --------------------------- */
/* From framework::Tensor */
/* --------------------------- */
DenseTensor::DenseTensor() {
meta_.dtype = paddle::experimental::DataType::FLOAT32;
meta_.offset = 0;
}
DenseTensor::DenseTensor(paddle::framework::proto::VarType::Type dtype) {
meta_.dtype = TransToPtenDataType(dtype);
meta_.offset = 0;
}
size_t DenseTensor::memory_size() const {
return holder_ == nullptr ? 0UL : holder_->size() - meta_.offset;
}
void DenseTensor::check_memory_size() const {
PADDLE_ENFORCE_NOT_NULL(holder_,
paddle::platform::errors::PreconditionNotMet(
"Tensor holds no memory. "
"Call Tensor::mutable_data firstly."));
PADDLE_ENFORCE_LE(
numel() * SizeOf(dtype()),
memory_size(),
paddle::platform::errors::PreconditionNotMet(
"Tensor's dimension is out of bound."
"Tensor's dimension must be equal or less than the size of its "
"memory."
"But received Tensor's dimension is d%, memory's size is %d.",
numel() * SizeOf(dtype()),
memory_size()));
}
const paddle::platform::Place& DenseTensor::place() const {
PADDLE_ENFORCE_NOT_NULL(
holder_,
paddle::platform::errors::PreconditionNotMet(
"Tensor not initialized yet when DenseTensor::place() is called."));
return holder_->place();
}
paddle::framework::proto::VarType::Type DenseTensor::type() const {
return TransToProtoVarType(meta_.dtype);
}
paddle::framework::proto::VarType::Type DenseTensor::saved_type() const {
return TransToProtoVarType(meta_.dtype);
}
void DenseTensor::set_layout(const paddle::framework::DataLayout layout) {
meta_.layout = layout;
}
void DenseTensor::ResetHolder(const std::shared_ptr<pten::Allocation>& holder) {
PADDLE_ENFORCE_EQ(
meta_.offset,
0,
paddle::platform::errors::Fatal(
"Only the offset is supported to zero when the holder is reset."));
if (holder_) {
// TODO(zyfncg): The change of static_cast<> in check will recover back
// when SetAllocationForOutputTenosr is deleted.
// Now the numel() may return -1, and will cast to a very large number when
// compare with a data with unsigned long type, this will make checking
// failed, so it's a temporary solution to deal with this problem.
PADDLE_ENFORCE_LE(
numel() * static_cast<int64_t>(SizeOf(dtype())),
static_cast<int64_t>(holder->size()),
paddle::platform::errors::InvalidArgument(
"The size of Holder is not enough to store the Tensor."));
}
holder_ = holder;
}
void DenseTensor::ResetHolderWithType(
const std::shared_ptr<pten::Allocation>& holder,
paddle::framework::proto::VarType::Type type) {
set_type(type);
ResetHolder(holder);
}
void DenseTensor::set_type(paddle::framework::proto::VarType::Type type) {
meta_.dtype = TransToPtenDataType(type);
}
void* DenseTensor::mutable_data(const paddle::platform::Place& place,
paddle::framework::proto::VarType::Type type,
size_t requested_size) {
set_type(type);
PADDLE_ENFORCE_GE(
numel(),
0,
paddle::platform::errors::PreconditionNotMet(
"The Tensor's element number must be equal or greater than zero. "
"The Tensor's shape is [",
dims(),
"] now"));
size_t size = numel() * SizeOf(dtype());
if (requested_size && (requested_size > size)) {
size = requested_size;
}
/* some versions of boost::variant don't have operator!= */
if (holder_ == nullptr || !(holder_->place() == place) ||
holder_->size() < size + meta_.offset) {
holder_.reset();
holder_ = paddle::memory::AllocShared(place, size);
meta_.offset = 0;
}
return reinterpret_cast<void*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
meta_.offset);
}
void* DenseTensor::mutable_data(const paddle::platform::Place& place,
size_t requested_size) {
return mutable_data(place, type(), requested_size);
}
void* DenseTensor::mutable_data(const paddle::platform::Place& place,
paddle::framework::proto::VarType::Type type,
const paddle::platform::Stream& stream) {
set_type(type);
PADDLE_ENFORCE_GE(
numel(),
0,
paddle::platform::errors::PreconditionNotMet(
"The Tensor's element number must be equal or greater than zero. "
"The Tensor's shape is [",
dims(),
"] now"));
size_t size = numel() * SizeOf(dtype());
/* some versions of boost::variant don't have operator!= */
if (holder_ == nullptr || !(holder_->place() == place) ||
holder_->size() < size + meta_.offset ||
!(paddle::platform::is_gpu_place(place) &&
paddle::memory::InSameStream(holder_, stream))) {
holder_.reset();
holder_ = paddle::memory::AllocShared(place, size, stream);
meta_.offset = 0;
}
return reinterpret_cast<void*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
meta_.offset);
}
/* @jim19930609: The following "mutable_data" only supports specific dtypes
defined in OpProto. This part need another clean up once the data type across
Fluid
and Pten get unified.
*/
template <typename T>
inline T* DenseTensor::mutable_data(const DDim& dims,
const paddle::platform::Place& place,
size_t requested_size) {
static_assert(std::is_pod<T>::value, "T must be POD");
meta_.dims = dims;
return mutable_data<T>(place, requested_size);
}
template <typename T>
inline T* DenseTensor::mutable_data(const paddle::platform::Place& place,
size_t requested_size) {
static_assert(std::is_pod<T>::value, "T must be POD");
return reinterpret_cast<T*>(mutable_data(
place, paddle::framework::DataTypeTrait<T>::DataType(), requested_size));
}
void DenseTensor::ShareBufferWith(const DenseTensor& tensor) {
holder_ = tensor.holder_;
meta_.offset = tensor.meta().offset;
meta_.dtype = tensor.dtype();
}
#define LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(dtype) \
template dtype* DenseTensor::mutable_data( \
const DDim& dims, \
const paddle::platform::Place& place, \
size_t requested_size); \
template dtype* DenseTensor::mutable_data( \
const paddle::platform::Place& place, size_t requested_size);
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(bool)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(int8_t)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(uint8_t)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(int16_t)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(int32_t)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(int64_t)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(float)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(double)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(::paddle::platform::bfloat16)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(::paddle::platform::float16)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(::paddle::experimental::complex64)
LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(::paddle::experimental::complex128)
#undef LEGACY_DATA_MEMBER_FUNC_INSTANTIATION
/* ------------------------------ */
/* From framework::LoDTensor */
/* ------------------------------ */
DenseTensor::DenseTensor(intrusive_ptr<Storage> storage,
const DenseTensorMeta& meta)
: meta_(meta), holder_(storage->move_data_shared()) {}
DenseTensor::DenseTensor(intrusive_ptr<Storage> storage, DenseTensorMeta&& meta)
: meta_(std::move(meta)), holder_(storage->move_data_shared()) {}
DenseTensor::DenseTensor(const LoD& lod) : DenseTensor() { meta_.lod = lod; }
void DenseTensor::set_lod(const LoD& lod) { meta_.lod = lod; }
LoD* DenseTensor::mutable_lod() { return &meta_.lod; }
std::pair<size_t, size_t> DenseTensor::lod_element(size_t level,
size_t elem) const {
PADDLE_ENFORCE_LT(
level,
NumLevels(),
paddle::platform::errors::InvalidArgument(
"The input level of LoD is invalid, it should be less than LoD "
"size. The input level is %zu, the LoD size is %zu.",
level,
NumLevels()));
PADDLE_ENFORCE_LT(elem,
NumElements(level),
paddle::platform::errors::InvalidArgument(
"The input element of LoD is invalid, it should be "
"less than the number of elements in its level."
"The input element is %zu, the number of elements in "
"its level is %zu.",
elem,
NumElements(level)));
return std::make_pair((meta_.lod)[level][elem], (meta_.lod)[level][elem + 1]);
}
size_t DenseTensor::NumLevels() const { return meta_.lod.size(); }
size_t DenseTensor::NumElements(size_t level) const {
PADDLE_ENFORCE_LT(
level,
NumLevels(),
paddle::platform::errors::InvalidArgument(
"The input level of LoD is invalid, it should be less than LoD "
"size. The input level is %zu, the LoD size is %zu.",
level,
NumLevels()));
// the last offset is the end of last element
return (meta_.lod)[level].size() - 1;
}
DenseTensor& DenseTensor::Resize(const DDim& dims) {
meta_.dims = dims;
return *this;
}
DenseTensor DenseTensor::Slice(int64_t begin_idx, int64_t end_idx) const {
check_memory_size();
PADDLE_ENFORCE_GE(begin_idx,
0,
paddle::platform::errors::OutOfRange(
"The start row index must be greater than 0."
"But received the start index is d%.",
begin_idx));
PADDLE_ENFORCE_LE(end_idx,
meta_.dims[0],
paddle::platform::errors::OutOfRange(
"The end row index is out of bound."));
PADDLE_ENFORCE_LT(
begin_idx,
end_idx,
paddle::platform::errors::InvalidArgument(
"The start row index must be less than the end row index."
"But received the start index = %d, the end index = %d.",
begin_idx,
end_idx));
if (meta_.dims[0] == 1) {
return *this;
} else {
size_t base = numel() / meta_.dims[0];
DenseTensor dst;
dst.holder_ = holder_;
dst.set_layout(meta_.layout);
dst.meta_.dtype = meta_.dtype;
DDim dst_dims = meta_.dims;
dst_dims[0] = end_idx - begin_idx;
dst.Resize(dst_dims);
dst.meta_.offset = meta_.offset + begin_idx * base * SizeOf(dtype());
return dst;
}
}
std::vector<DenseTensor> DenseTensor::Split(int64_t split_size,
int64_t axis) const {
check_memory_size();
PADDLE_ENFORCE_GE(meta_.dims.size(),
0,
paddle::platform::errors::OutOfRange(
"split expects at least a 1-dimensional tensor"));
PADDLE_ENFORCE_GE(
split_size,
0,
paddle::platform::errors::OutOfRange(
"split expects split_size be non-negative, but got split_size is %d",
split_size));
int64_t numel_size = meta_.dims[axis];
int64_t num_splits = 1;
if (split_size != 0) {
num_splits =
std::max<int64_t>((numel_size + split_size - 1) / split_size, 1);
}
std::vector<DenseTensor> splits(num_splits);
int64_t last_split_size = split_size - (split_size * num_splits - numel_size);
for (int64_t i = 0; i < num_splits; ++i) {
int64_t length = i < num_splits - 1 ? split_size : last_split_size;
splits[i] = Slice(i * split_size, i * split_size + length);
}
return splits;
}
std::vector<DenseTensor> DenseTensor::Chunk(int64_t chunks,
int64_t axis) const {
check_memory_size();
PADDLE_ENFORCE_GE(meta_.dims.size(),
0,
paddle::platform::errors::OutOfRange(
"split expects at least a 1-dimensional tensor"));
PADDLE_ENFORCE_GE(
chunks,
0,
paddle::platform::errors::OutOfRange(
"chunks expects to be greater than 0, but got chunks is %d", chunks));
int64_t numel_size = meta_.dims[axis];
int64_t split_size = (numel_size + chunks - 1) / chunks;
return Split(split_size, axis);
}
DenseTensor& DenseTensor::ShareDataWith(const DenseTensor& src) {
src.check_memory_size();
// Preserve LoD
auto lod = meta_.lod;
*this = src;
meta_.lod = lod;
return *this;
}
DenseTensor& DenseTensor::ShareInplaceVersionCounterWith(
const DenseTensor& src) {
PADDLE_ENFORCE_NOT_NULL(
inplace_version_counter_,
paddle::platform::errors::PreconditionNotMet(
"Tensor does not hold inplace_version_counter_."));
inplace_version_counter_ = src.inplace_version_counter_;
return *this;
}
} // namespace pten
paddle/pten/core/tensor_status.h 已删除 100644 → 0
浏览文件 @ 01d04be6
/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/pten/common/backend.h"
#include "paddle/pten/common/data_type.h"
#include "paddle/pten/common/layout.h"
namespace pten {
class TensorInplaceVersion {
public:
explicit TensorInplaceVersion(uint32_t inplace_version = 0)
: inplace_version_(inplace_version) {}
bool IsUnique() const { return inplace_version_ == 0; }
void Bump() { ++inplace_version_; }
uint32_t CurrentVersion() const { return inplace_version_; }
private:
uint32_t inplace_version_;
};
/**
* The Status data member of DenseTensor.
*
* Here the `static` represents information describing the status of Tensor,
* such as version counter, or other bool status members.
*
* Note: TensorStatus is a struct, the members are named like
* ordinary nonmember variables, such as `type` instead of `type_`.
* And we direct access its members, in addition to constructor, destructor
* and functions for setting data members, can not provide other functions.
*
* Note: polish impl later
*/
struct TensorStatus {
TensorStatus() = default;
TensorStatus(const TensorStatus&) = default;
TensorStatus(TensorStatus&&) = default;
TensorStatus& operator=(const TensorStatus&) = delete;
TensorStatus& operator=(TensorStatus&&) = delete;
TensorInplaceVersion inplace_version_counter{0};
/**
* For Scalar Tensor design
*/
bool is_scalar{false};
};
} // namespace pten
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